Next-Generation DNA Sequencing Methods

Arthropod diversity in the alpine tundra using metabarcoding: Spatial and temporal differences in alpha- and beta-diversity

All ecosystems face ecological challenges in this century. Therefore, it is becoming increasingly important to understand the ecology and degree of local adaptation of functionally important Arctic-alpine biomes by looking at the most diverse taxon of metazoans: the Arthropoda. This is the first study to utilize metabarcoding in the Alpine tundra, providing insights into the effects of micro-environmental parameters on alpha- and beta-diversity of arthropods in such unique environments. To characterize arthropod diversity, pitfall traps were set at three middle-alpine sampling sites in the Scandinavian mountain range in Norway during the snow-free season in 2015. A metabarcoding approach was then used to determine the small-scale biodiversity patterns of arthropods in the Alpine tundra. All DNA was extracted directly from the preservative EtOH from 27 pitfall traps. In order to identify the controlling environmental conditions, all sampling locations were equipped with automatic data loggers for permanent measurement of the microenvironmental conditions. The variables measured were: air temperature [°C] at 15 cm height, soil temperature [°C] at 15 cm depth, and soil moisture [vol.%] at 15 cm depth. A total of 233 Arthropoda OTUs were identified. The number of unique OTUs found per sampling location (ridge, south-facing slope, and depression) was generally higher than the OTUs shared between the sampling locations, demonstrating that niche features greatly impact arthropod community structure. Our findings emphasize the fine-scale heterogeneity of arctic-alpine ecosystems and provide evidence for trait-based and niche-driven adaptation. The spatial and temporal differences in arthropod diversity were best explained by soil moisture and soil temperature at the respective locations. Furthermore, our results show that arthropod diversity is underestimated in alpine-tundra ecosystems using classical approaches and highlight the importance of integrating long-term functional environmental data and modern taxonomic techniques into biodiversity research to expand our ecological understanding of fine- and meso-scale biogeographical patterns.

Selective Sensing of DNA Nucleobases with Angular Discrimination

The fast and precise selective sensing of DNA nucleobases is a long-pursued method that can lead to huge advances in the field of genomics and have an impact on aspects such as the prevention of diseases, health enhancement, and, in general, all types of medical treatments. We present here a new type of nanoscale sensor based on carbon nanotubes with a specific geometry that can discriminate the type of nucleobase and also its angle of orientation. The proper differentiation of nucleobases is essential to clearly sequence DNA chains, while angular discrimination is key to improving the sensing selectivity. We perform first-principle and quantum transport simulations to calculate the transmission, conductance, and current of the nanotube-based nanoscale sensor in the presence of the four nucleotides (A, C, G, and T), each of them rotated 0, 90, 180, or 270°. Our results show that this system is able to effectively discriminate between the four nucleotides and their angle of orientation. We explain these findings in terms of the interaction between the phosphate group of the nucleotide and the nanotube wall. The phosphate specifically distorts the electronic structure of the nanotube depending on the distance and the orientation and leads to nontrivial changes in the transmission. This work provides a method for finer and more precise sequential DNA chains.

Is the metabarcoding ripe enough to be applied to the authentication of foodstuff of animal origin? A systematic review

Food authentication using molecular techniques is of great importance to fight food fraud. Metabarcoding, based on the next-generation sequencing (NGS) technologies, allowing large-scale taxonomic identification of complex samples via massive parallel sequencing of fragments (called DNA barcodes) simultaneously, has become increasingly popular in many scientific fields. A systematic review to answer the question "Is the metabarcoding ripe enough to be applied to the authentication of foodstuff of animal origin?" is presented. The inclusion criteria were focused on the selection of scientific papers (SPs) only applying metabarcoding to foodstuff of animal origin collected on the market. The 23 included SPs were first analyzed with respect to the metabarcoding phases: library preparation (target genes, primer pairs, and fragment length), sequencing (NGS platforms), and final data analysis (bioinformatic pipelines). Given the importance of primer selection, the taxonomic coverage of the used primers was also evaluated. In addition, the SPs were scored based on the use of quality control measures (procedural blanks, positive controls, replicates, curated databases, and thresholds to filter the data). A lack of standardized protocols, especially with respect to the target barcode/s and the universal primer/s, and the infrequent application of the quality control measures, leads to answer that metabarcoding is not ripe enough for authenticating foodstuff of animal origin. However, the observed trend of the SP quality improvement over the years is encouraging. Concluding, a proper protocol standardization would allow a wider use of metabarcoding by both official and private laboratories, enabling this method to become the primary for the authentication of foodstuffs of animal origin.

Microbiota and Lipotoxicity

Gut microbiota is an indispensable commensal partner of human superorganism. The wealth of genetic repertoire provided by these microorganisms extends host's substrate processing capability. Energy and nutrient harvesting machinery primarily depends on the proper function of these organisms. However, the dynamic composition of microbiota changes with age, lifestyle, stress factors, infections, medications, and host pathophysiological conditions. Host immune system is primarily responsible for shaping up the microbial community and sustaining the symbiotic state. This involves controlling the delicate balance between agility toward pathobionts and tolerance toward symbionts. When things go wrong with this crosstalk, dysbiosis may arise.Metabolic syndrome is a multisystemic, low-grade chronic inflammatory disease that involves dyslipidemia, glucose intolerance, insulin resistance, and central obesity. Excess caloric intake with high-sugar and high-fat diet promote high energy harvesting and lipogenesis. The secretion of adipokines accompanies lipid spillover from fat cells, which contribute to insulin resistance and the expansion of adipose tissue in ectopic sites. Proinflammatory cytokines from adipose tissue macrophages increase the extent of adipose dysfunction.The inflammatory nature of obesity and metabolic syndrome recall the connection between dysbiosis and immune dysfunction. A remarkable association exits between obesity, inflammatory bowel disease, gluten-sensitive enteropathy, and dysbiosis. These conditions compromise the gut mucosa barrier and allow lipopolysaccharide to enter circulation. Unresolved chronic inflammation caused by one condition may overlap or trigger the other(s). Experimental studies and therapeutic trials of fecal microbiota transplantation promise limited improvement in some of these conditions.Typically, metabolic syndrome is considered as a consequence of overnutrition and the vicious cycle of lipogenesis, lipid accumulation, and chronic low-level inflammation. Because of the complex nature of this disorder, it remains inconclusive whether dysbiosis is a cause or consequence of obesity and metabolic syndrome.

Analyzing RNA posttranscriptional modifications to decipher the epitranscriptomic code

The discovery of RNA silencing has revealed that non-protein-coding sequences (ncRNAs) can cover essential roles in regulatory networks and their malfunction may result in severe consequences on human health. These findings have prompted a general reassessment of the significance of RNA as a key player in cellular processes. This reassessment, however, will not be complete without a greater understanding of the distribution and function of the over 170 variants of the canonical ribonucleotides, which contribute to the breathtaking structural diversity of natural RNA. This review surveys the analytical approaches employed for the identification, characterization, and detection of RNA posttranscriptional modifications (rPTMs). The merits of analyzing individual units after exhaustive hydrolysis of the initial biopolymer are outlined together with those of identifying their position in the sequence of parent strands. Approaches based on next generation sequencing and mass spectrometry technologies are covered in depth to provide a comprehensive view of their respective merits. Deciphering the epitranscriptomic code will require not only mapping the location of rPTMs in the various classes of RNAs, but also assessing the variations of expression levels under different experimental conditions. The fact that no individual platform is currently capable of meeting all such demands implies that it will be essential to capitalize on complementary approaches to obtain the desired information. For this reason, the review strived to cover the broadest possible range of techniques to provide readers with the fundamental elements necessary to make informed choices and design the most effective possible strategy to accomplish the task at hand.

Challenges involved in the application of artificial intelligence in gastroenterology: The race is on!

Gastroenterology is a particularly data-rich field, generating vast repositories of data that are a fruitful ground for artificial intelligence (AI) and machine learning (ML) applications. In this opinion review, we initially elaborate on the current status of the application of AI/ML-based software in gastroenterology. Currently, AI/ML-based models have been developed in the following applications: Models integrated into the clinical setting following real-time patient data flagging patients at high risk for developing a gastrointestinal disease, models employing non-invasive parameters that provide accurate diagnoses aiming to either replace, minimize, or refine the indications of endoscopy, models utilizing genomic data to diagnose various gastrointestinal diseases, computer-aided diagnosis systems facilitating the interpretation of endoscopy images, models to facilitate treatment allocation and predict the response to treatment, and finally, models in prognosis predicting complications, recurrence following treatment, and overall survival. Then, we elaborate on several challenges and how they may negatively impact the widespread application of AI in healthcare and gastroenterology. Specifically, we elaborate on concerns regarding accuracy, cost-effectiveness, cybersecurity, interpretability, oversight, and liability. While AI is unlikely to replace physicians, it will transform the skillset demanded by future physicians to practice. Thus, physicians are expected to engage with AI to avoid becoming obsolete.

Control Theory and Systems Biology: Potential Applications in Neurodegeneration and Search for Therapeutic Targets

Control theory, a well-established discipline in engineering and mathematics, has found novel applications in systems biology. This interdisciplinary approach leverages the principles of feedback control and regulation to gain insights into the complex dynamics of cellular and molecular networks underlying chronic diseases, including neurodegeneration. By modeling and analyzing these intricate systems, control theory provides a framework to understand the pathophysiology and identify potential therapeutic targets. Therefore, this review examines the most widely used control methods in conjunction with genomic-scale metabolic models in the steady state of the multi-omics type. According to our research, this approach involves integrating experimental data, mathematical modeling, and computational analyses to simulate and control complex biological systems. In this review, we find that the most significant application of this methodology is associated with cancer, leaving a lack of knowledge in neurodegenerative models. However, this methodology, mainly associated with the Minimal Dominant Set (MDS), has provided a starting point for identifying therapeutic targets for drug development and personalized treatment strategies, paving the way for more effective therapies.

Ambiguous genes due to aligners and their impact on RNA-seq data analysis

The main scope of the study is ambiguous genes, i.e. genes whose expression is difficult to estimate from the data produced by next-generation sequencing technologies. We focused on the RNA sequencing (RNA-Seq) type of experiment performed on the Illumina platform. It is crucial to identify such genes and understand the cause of their difficulty, as these genes may be involved in some diseases. By giving misleading results, they could contribute to a misunderstanding of the cause of certain diseases, which could lead to inappropriate treatment. We thought that the ambiguous genes would be difficult to map because of their complex structure. So we looked at RNA-seq analysis using different mappers to find genes that would have different measurements from the aligners. We were able to identify such genes using a generalized linear model with two factors: mappers and groups introduced by the experiment. A large proportion of ambiguous genes are pseudogenes. High sequence similarity of pseudogenes to functional genes may indicate problems in alignment procedures. In addition, predictive analysis verified the performance of difficult genes in classification. The effectiveness of classifying samples into specific groups was compared, including the expression of difficult and not difficult genes as covariates. In almost all cases considered, ambiguous genes have less predictive power.

A preliminary metabarcoding analysis of Portuguese raw honeys

The microbial diversity in Portuguese raw honeys remains largely uncharacterized, constituting a serious knowledge gap in one of the country's most important resources. This work provides an initial investigation with amplicon metabarcoding analysis of two Lavandula spp. from different geographical regions of Portugal and one Eucalyptus spp. honey. The results obtained allowed to identify that each honey harbors diverse microbiomes with taxa that can potentially affect bee and human health, cause spoilage, and highlight bad bee-hive management practices. We verified that prokaryotes had a tendency towards a more marked core bacterial and a relative homogenous taxa distribution, and that the botanical origin of honey is likely to have a stronger impact on the fungal community. Thus, the results obtained in this work provide important information that can be helpful to improve this critical Portuguese product and industry.

Dominant influence of plants on soil microbial carbon cycling functions during natural restoration of degraded karst vegetation

The impacts of natural restoration projects on soil microbial carbon (C) cycling functions have not been well recognized despite their wide implementation in the degraded karst areas of southwest China. In this study, metagenomic sequencing assays were conducted on functional genes and microorganisms related to soil C-cycling at three natural restoration stages (shrubbery, TG; secondary forest, SG; old-growth forest, OG) in the southeast of Guizhou Province, China. The aims were to investigate the changes in microbial potentials responsible for soil C cycling and the underlying driving forces. The natural restoration resulted in vegetation establishment at all three restoration stages, rendering alterations of soil microbial C cycle functions as indicated by metagenomic gene assays. When TG was restored into OG, the number and diversity of genes and microorganisms involved in soil C cycling remained unchanged, but their composition underwent significant shifts. Specifically, microbial potentials for soil C decomposition exhibited an increase driven by the collaborative efforts of plants and soils, while microbial potentials for soil C biosynthesis displayed an initial upswing followed by a subsequent decline which was primarily influenced by plants alone. In comparison to soil nutrients, it was determined that plant diversities served as the primary driving factor for the alterations in microbial carbon cycle potentials. Soil microbial communities involved in C cycling were predominantly attributed to Proteobacteria (31.87%-40.25%) and Actinobacteria (11.29%-26.07%), although their contributions varied across the three restoration stages. The natural restoration of degraded karst vegetation thus influences soil microbial C cycle functions by enhancing C decomposition potentials and displaying a nuanced pattern of biosynthesis potentials, primarily influenced by above-ground plants. These results provide valuable new insights into the regulation of soil C cycling during the restoration of degraded karst vegetation from genetic and microbial perspectives.

Predictive value of DNA methylation patterns in AML patients treated with an azacytidine containing induction regimen

BACKGROUND

Acute myeloid leukemia (AML) is a heterogeneous disease with a poor prognosis. Dysregulation of the epigenetic machinery is a significant contributor to disease development. Some AML patients benefit from treatment with hypomethylating agents (HMAs), but no predictive biomarkers for therapy response exist. Here, we investigated whether unbiased genome-wide assessment of pre-treatment DNA-methylation profiles in AML bone marrow blasts can help to identify patients who will achieve a remission after an azacytidine-containing induction regimen.

RESULTS

A total of n = 155 patients with newly diagnosed AML treated in the AMLSG 12-09 trial were randomly assigned to a screening and a refinement and validation cohort. The cohorts were divided according to azacytidine-containing induction regimens and response status. Methylation status was assessed for 664,227 500-bp-regions using methyl-CpG immunoprecipitation-seq, resulting in 1755 differentially methylated regions (DMRs). Top regions were distilled and included genes such as WNT10A and GATA3. 80% of regions identified as a hit were represented on HumanMethlyation 450k Bead Chips. Quantitative methylation analysis confirmed 90% of these regions (36 of 40 DMRs). A classifier was trained using penalized logistic regression and fivefold cross validation containing 17 CpGs. Validation based on mass spectra generated by MALDI-TOF failed (AUC 0.59). However, discriminative ability was maintained by adding neighboring CpGs. A recomposed classifier with 12 CpGs resulted in an AUC of 0.77. When evaluated in the non-azacytidine containing group, the AUC was 0.76.

CONCLUSIONS

Our analysis evaluated the value of a whole genome methyl-CpG screening assay for the identification of informative methylation changes. We also compared the informative content and discriminatory power of regions and single CpGs for predicting response to therapy. The relevance of the identified DMRs is supported by their association with key regulatory processes of oncogenic transformation and support the idea of relevant DMRs being enriched at distinct loci rather than evenly distribution across the genome. Predictive response to therapy could be established but lacked specificity for treatment with azacytidine. Our results suggest that a predictive epigenotype carries its methylation information at a complex, genome-wide level, that is confined to regions, rather than to single CpGs. With increasing application of combinatorial regimens, response prediction may become even more complicated.

Cauchy combination methods for the detection of gene-environment interactions for rare variants related to quantitative phenotypes

The characterization of gene-environment interactions (GEIs) can provide detailed insights into the biological mechanisms underlying complex diseases. Despite recent interest in GEIs for rare variants, published GEI tests are underpowered for an extremely small proportion of causal rare variants in a gene or a region. By extending the aggregated Cauchy association test (ACAT), we propose three GEI tests to address this issue: a Cauchy combination GEI test with fixed main effects (CCGEI-F), a Cauchy combination GEI test with random main effects (CCGEI-R), and an omnibus Cauchy combination GEI test (CCGEI-O). ACAT was applied to combine p values of single-variant GEI analyses to obtain CCGEI-F and CCGEI-R and p values of multiple GEI tests were combined in CCGEI-O. Through numerical simulations, for small numbers of causal variants, CCGEI-F, CCGEI-R and CCGEI-O provided approximately 5% higher power than the existing GEI tests INT-FIX and INT-RAN; however, they had slightly higher power than the existing GEI test TOW-GE. For large numbers of causal variants, although CCGEI-F and CCGEI-R exhibited comparable or slightly lower power values than the competing tests, the results were still satisfactory. Among all simulation conditions evaluated, CCGEI-O provided significantly higher power than that of competing GEI tests. We further applied our GEI tests in genome-wide analyses of systolic blood pressure or diastolic blood pressure to detect gene-body mass index (BMI) interactions, using whole-exome sequencing data from UK Biobank. At a suggestive significance level of 1.0 × 10-4, KCNC4, GAR1, FAM120AOS and NT5C3B showed interactions with BMI by our GEI tests.

Fluorescence detection of apolipoprotein E gene polymorphisms based on oligonucleotide ligation and magnetic separation

Alzheimer's disease is a progressive neurodegenerative condition that causes brain cell death and is the leading cause of dementia. Most patients with Alzheimer's disease are diagnosed with late-onset Alzheimer's disease (LOAD), with apolipoprotein E (APOE) genotypes being highly associated with the frequency of LOAD risk. A fluorescence detection system coupled with oligonucleotide ligation and magnetic separation was developed to identify two single-nucleotide polymorphisms (SNPs) for the APOE gene and recognize APOE alleles for LOAD. The system utilized a fluorescence probe with one base-discriminating nucleoside for SNP (F probe) and a perfectly complementary biotin-modified sequence against the target DNA (P probe). When the F and P probes matched the target DNA sequences, DNA ligation occurred, and ligation products were produced. Streptavidin magnetic beads were subsequently employed to remove the ligation products, and a decrease in fluorescence intensity was observed in the supernatant compared to when there was no target DNA. This system detected two SNPs of APOE alleles, namely rs429358 and rs7412. The results indicated that the R-values ((F0 - F1)/F0) for rs429358 were 0.92 ± 0.002 for the T/T target, 0.47 ± 0.004 for the T/C target and 0.11 ± 0.004 for the C/C target, respectively. The R-values for rs7412 were 0.73 ± 0.009 for the C/C target, 0.42 ± 0.001 for the C/T target and 0.16 ± 0.007 for the T/T target, respectively. F0 and F1 represent the fluorescence intensity of the F probe without and with target DNA, respectively. Based on fluorescence intensity, the fluorescence detection system was able to identify the genotypes of the APOE gene accurately to evaluate the risk of Alzheimer's disease.

Exploration of whole genome amplification generated chimeric sequences in long-read sequencing data

MOTIVATION

Multiple displacement amplification (MDA) has become the most commonly used method of whole genome amplification, generating a vast amount of DNA with higher molecular weight and greater genome coverage. Coupling with long-read sequencing, it is possible to sequence the amplicons of over 20 kb in length. However, the formation of chimeric sequences (chimeras, expressed as structural errors in sequencing data) in MDA seriously interferes with the bioinformatics analysis but its influence on long-read sequencing data is unknown.

RESULTS

We sequenced the phi29 DNA polymerase-mediated MDA amplicons on the PacBio platform and analyzed chimeras within the generated data. The 3rd-ChimeraMiner has been constructed as a pipeline for recognizing and restoring chimeras into the original structures in long-read sequencing data, improving the efficiency of using TGS data. Five long-read datasets and one high-fidelity long-read dataset with various amplification folds were analyzed. The result reveals that the mis-priming events in amplification are more frequently occurring than widely perceived, and the propor tion gradually accumulates from 42% to over 78% as the amplification continues. In total, 99.92% of recognized chimeric sequences were demonstrated to be artifacts, whose structures were wrongly formed in MDA instead of existing in original genomes. By restoring chimeras to their original structures, the vast majority of supplementary alignments that introduce false-positive structural variants are recycled, removing 97% of inversions on average and contributing to the analysis of structural variation in MDA-amplified samples. The impact of chimeras in long-read sequencing data analysis should be emphasized, and the 3rd-ChimeraMiner can help to quantify and reduce the influence of chimeras.

AVAILABILITY AND IMPLEMENTATION

The 3rd-ChimeraMiner is available on GitHub, https://github.com/dulunar/3rdChimeraMiner.

CircRNAs: A Promising Star for Treatment and Prognosis in Oral Squamous Cell Carcinoma

CircRNAs are a class of endogenous long non-coding RNAs with a single-stranded circular structure. Most circRNAs are relatively stable, highly conserved, and specifically expressed in tissue during the cell and developmental stages. Many circRNAs have been discovered in OSCC. OSCC is one of the most severe and frequent forms of head and neck cancer today, with a poor prognosis and low overall survival rate. Due to its prevalence, OSCC is a global health concern, characterized by genetic and epigenomic changes. However, the mechanism remains vague. With the advancement of biotechnology, a large number of circRNAs have been discovered in mammalian cells. CircRNAs are dysregulated in OSCC tissues and thus associated with the clinicopathological characteristics and prognosis of OSCC patients. Research studies have demonstrated that circRNAs can serve as biomarkers for OSCC diagnosis and treatment. Here, we summarized the properties, functions, and biogenesis of circRNAs, focusing on the progress of current research on circRNAs in OSCC.

Microfluidic delivery of cutting enzymes for fragmentation of surface-adsorbed DNA molecules

We describe a method for fragmenting, in-situ, surface-adsorbed and immobilized DNAs on polymethylmethacrylate(PMMA)-coated silicon substrates using microfluidic delivery of the cutting enzyme DNase I. Soft lithography is used to produce silicone elastomer (Sylgard 184) gratings which form microfluidic channels for delivery of the enzyme. Bovine serum albumin (BSA) is used to reduce DNase I adsorption to the walls of the microchannels and enable diffusion of the cutting enzyme to a distance of 10mm. Due to the DNAs being immobilized, the fragment order is maintained on the surface. Possible methods of preserving the order for application to sequencing are discussed.

Learning from cancer to address COVID-19

Patients with cancer have been disproportionately affected by the novel coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Knowledge collected during the last three decades of cancer research has helped the medical research community worldwide to respond to many of the challenges raised by COVID-19, during the pandemic. The review, briefly summarizes the underlying biology and risk factors of COVID-19 and cancer, and aims to present recent evidence on cellular and molecular relationship between the two diseases, with a focus on those that are related to the hallmarks of cancer and uncovered in the first less than three years of the pandemic (2020-2022). This may not only help answer the question "Why cancer patients are considered to be at a particularly high risk of developing severe COVID-19 illness?", but also helped treatments of patients during the COVID-19 pandemic. The last session highlights the pioneering mRNA studies and the breakthrough discovery on nucleoside-modifications of mRNA by Katalin Karikó, which led to the innovation and development of the mRNA-based SARSCoV-2 vaccines saving lives of millions and also opened the door for a new era of vaccines and a new class of therapeutics.

The multifaceted roles of mass spectrometric analysis in nucleic acids drug discovery and development

The deceptively simple concepts of mass determination and fragment analysis are the basis for the application of mass spectrometry (MS) to a boundless range of analytes, including fundamental components and polymeric forms of nucleic acids (NAs). This platform affords the intrinsic ability to observe first-hand the effects of NA-active drugs on the chemical structure, composition, and conformation of their targets, which might affect their ability to interact with cognate NAs, proteins, and other biomolecules present in a natural environment. The possibility of interfacing with high-performance separation techniques represents a multiplying factor that extends these capabilities to cover complex sample mixtures obtained from organisms that were exposed to NA-active drugs. This report provides a brief overview of these capabilities in the context of the analysis of the products of NA-drug activity and NA therapeutics. The selected examples offer proof-of-principle of the applicability of this platform to all phases of the journey undertaken by any successful NA drug from laboratory to bedside, and provide the rationale for its rapid expansion outside traditional laboratory settings in support to ever growing manufacturing operations.

Development of a simplified and cost-effective sample preparation method for genotyping of human papillomavirus by next-generation sequencing

High-risk human papillomavirus (HPV) infection is the most common cause of cervical cancer, but low-risk HPV strains can sometimes also be involved. Although HPV genotyping techniques used in clinical diagnosis cannot detect low-risk HPV, next-generation sequencing (NGS) can detect both types. However, DNA library preparation is complicated and expensive. The aim of this study was to develop a simplified, cost-effective sample preparation procedure for HPV genotyping based on next-generation sequencing (NGS). After DNA extraction, a first round of PCR was performed using modified MY09/11 primers specific for the L1 region of the HPV genome, followed by a second round of PCR to add the indexes and adaptors. Then, the DNA libraries were purified and quantified, and high-throughput sequencing was performed using an Illumina MiSeq platform. The sequencing reads were compared with reference sequences for HPV genotyping. The limit of detection for HPV amplification was 100 copies/µl. Analysis of the correlation of pathological cytology with the HPV genotype in individual clinical samples showed that HPV66 was the most common genotype found in the normal stage, whereas HPV16 was the main genotype found in low-grade squamous intraepithelial lesions, high-grade squamous intraepithelial lesions, and cervical cancer. This NGS method can detect and identify several HPV genotypes with 92% accuracy and 100% reproducibility, and it shows potential as a simplified and cost-effective technique for large-scale HPV genotyping in clinical samples.

microRNA Detection via Nanostructured Biochips for Early Cancer Diagnostics

MicroRNA (miRNA) are constituted of approximately 22 nucleotides and play an important role in the regulation of many physiological functions and diseases. In the last 10 years, an increasing interest has been recorded in studying the expression profile of miRNAs in cancer. Real time-quantitative polymerase chain reaction (RT-qPCR), microarrays, and small RNA sequencing represent the gold standard techniques used in the last 30 years as detection methods. The advent of nanotechnology has allowed the fabrication of nanostructured biosensors which are widely exploited in the diagnostic field. Nanostructured biosensors offer many advantages: (i) their small size allows the construction of portable, wearable, and low-cost products; (ii) the large surface-volume ratio enables the loading of a great number of biorecognition elements (e.g., probes, receptors); and (iii) direct contact of the recognition element with the analyte increases the sensitivity and specificity inducing low limits of detection (LOD). In this review, the role of nanostructured biosensors in miRNA detection is explored, focusing on electrochemical and optical sensing. In particular, four types of nanomaterials (metallic nanoparticles, graphene oxide, quantum dots, and nanostructured polymers) are reported for both detection strategies with the aim to show their distinct properties and applications.

A fast machine-learning-guided primer design pipeline for selective whole genome amplification

Addressing many of the major outstanding questions in the fields of microbial evolution and pathogenesis will require analyses of populations of microbial genomes. Although population genomic studies provide the analytical resolution to investigate evolutionary and mechanistic processes at fine spatial and temporal scales-precisely the scales at which these processes occur-microbial population genomic research is currently hindered by the practicalities of obtaining sufficient quantities of the relatively pure microbial genomic DNA necessary for next-generation sequencing. Here we present swga2.0, an optimized and parallelized pipeline to design selective whole genome amplification (SWGA) primer sets. Unlike previous methods, swga2.0 incorporates active and machine learning methods to evaluate the amplification efficacy of individual primers and primer sets. Additionally, swga2.0 optimizes primer set search and evaluation strategies, including parallelization at each stage of the pipeline, to dramatically decrease program runtime. Here we describe the swga2.0 pipeline, including the empirical data used to identify primer and primer set characteristics, that improve amplification performance. Additionally, we evaluate the novel swga2.0 pipeline by designing primer sets that successfully amplify Prevotella melaninogenica, an important component of the lung microbiome in cystic fibrosis patients, from samples dominated by human DNA.

Microbiota and Cardiovascular Diseases: Mechanisms of Influence and Correction Possibilities

The term "microbiota"  refers to the microbial  community  occupying a specific  habitat  with  defined  physical  and  chemical  properties  and  forming specific  ecological  niches.  The adult  intestinal  microbiota  is diverse.  It mainly  consists  of bacteria  of Bacteroidetes  and  Firmicutes  types.  The link between the gut microbiota  and cardiovascular disease (CVD) is being actively discussed.  Rapid progress  in this field is explained  by the development of new generation  sequencing methods and the use of sterile gut mice in experiments.  More and more data are being published about the influence of microbiota  on the development  and course of hypertension, coronary  heart disease (IHD), myocardial  hypertrophy, chronic heart failure (CHF) and atrial fibrillation (AF). Diet therapy,  antibacterial drugs,  pro- and prebiotics are successfully  used as tools to correct the structure of the gut microbiota of the macroorganism. Correction  of gut microbiota  in an experiment  on rats with coronary  occlusion  demonstrates  a significant  reduction in necrotic area. A study  involving  patients  suffering  from  CHF  reveals a significant  reduction  in the level of uric acid,  highly  sensitive C-reactive protein,  and creatinine. In addition to structural and laboratory changes  in patients with CVD when modifying the microbiota  of the gut, also revealed the effect on the course of arterial hypertension. Correction  of gut microbiota  has a beneficial  effect on the course of AF. We assume  that further active study of issues of influence and interaction of gut microbiota  and macroorganism may in the foreseeable future make significant  adjustments  in approaches to treatment of such patients.

Whole-Genome Sequencing-Based Resistome Analysis of Nosocomial Multidrug-Resistant Non-Fermenting Gram-Negative Pathogens from the Balkans

Non-fermenting Gram-negative bacilli (NFGNB), such as Pseudomonas aeruginosa and Acinetobacter baumannii, are among the major opportunistic pathogens involved in the global antibiotic resistance epidemic. They are designated as urgent/serious threats by the Centers for Disease Control and Prevention and are part of the World Health Organization’s list of critical priority pathogens. Also, Stenotrophomonas maltophilia is increasingly recognized as an emerging cause for healthcare-associated infections in intensive care units, life-threatening diseases in immunocompromised patients, and severe pulmonary infections in cystic fibrosis and COVID-19 individuals. The last annual report of the ECDC showed drastic differences in the proportions of NFGNB with resistance towards key antibiotics in different European Union/European Economic Area countries. The data for the Balkans are of particular concern, indicating more than 80% and 30% of invasive Acinetobacter spp. and P. aeruginosa isolates, respectively, to be carbapenem-resistant. Moreover, multidrug-resistant and extensively drug-resistant S. maltophilia from the region have been recently reported. The current situation in the Balkans includes a migrant crisis and reshaping of the Schengen Area border. This results in collision of diverse human populations subjected to different protocols for antimicrobial stewardship and infection control. The present review article summarizes the findings of whole-genome sequencing-based resistome analyses of nosocomial multidrug-resistant NFGNBs in the Balkan countries.

Comparative Genomic Analysis and Physiological Properties of Limosilactobacillus fermentum SMFM2017-NK2 with Ability to Inflammatory Bowel Disease

The objective of this study was to evaluate the anti-inflammatory effect of Latilactobacillus sakei SMFM2017-NK1 (LS1), L. sakei SMFM2017-NK3 (LS2), and Limosilactobacillus fermentum SMFM2017-NK2 (LF) on colitis using an animal model. DSS (dextran sulfate sodium salt) was orally injected into C57BL/6N mice to induce inflammation in the colon for seven days. Colitis mice models were treated with LS1, LS2, and LF, respectively, and Lacticaseibacillus rhamnosus GG (LGG) was used as a positive control. During oral administration of lactic acid bacteria, the weights of the mice were measured, and the disease activity index (DAI) score was determined by judging the degree of diarrhea and bloody stool. When comparing the differences between the minimum weight after DSS administration and the maximum weight after lactic acid bacteriaadministration were compared, the LF-treated group showed the highest weight gain at 8.91%. The DAI scores of the LF, LS2, and LGG groups were lower than that of the control group. After sacrifice, mRNA expression levels for proinflammatory cytokines (TNF-α, IL-1β, IL-6, and IFN-γ) and mediators (iNOS and COX-2) in the colon were measured. LF was selected as a superior strain for anti-inflammation in the colon. It was further analyzed to determine its biochemical characteristics, cytotoxicity, and thermal stability. Catalase and oxidase activities for LF were negative. In cytotoxicity and heat stability tests, the LF group had higher cell viability than the LGG group. The genome of LF was obtained, and 5682 CDS, 114 tRNA, 2 RNA, and 5 repeat regions were predicted. Especially, LF could be distinguished from the other three L. fermentum strains based on taxonomic profiling, specific orthologous genes of the strain, and genomic variants. The results of this study suggest that L. fermentum SMFM2017-NK2 is a novel strain with an anti-inflammatory effect on colitis.

EVI2B Is a Prognostic Biomarker and Is Correlated with Monocyte and Macrophage Infiltration in Osteosarcoma Based on an Integrative Analysis

Osteosarcoma (OS) is the most common malignant bone tumor. However, treatment strategies have not changed over the past 30 years. The relationship between OS and the immune microenvironment may provide a basis for the establishment of novel therapeutic targets. In this study, a large-scale gene expression dataset (GSE42352) was used to identify key genes in OS. A Target-OS dataset from the Cancer Genome Atlas was used as a validation set. Ecotropic viral integration site 2B (EVI2B) was significantly upregulated in OS tumor samples. Differentially expressed genes (DEGs) were identified between samples with high and low EVI2B expression in both the test and validation cohorts. The top three functions of DEGs determined by a gene set enrichment analysis (GSEA) were chemokine signaling, cytokine-cytokine receptor interaction, and Human T-cell leukemia virus 1 infection. A prognostic prediction model including EVI2B, DOCK2, and CD33 was constructed by a Cox regression analysis. This model indicated that EVI2B is an independent protective prognostic marker in OS. An analysis of immune infiltration further showed that high EVI2B expression levels were correlated with high levels of macrophage infiltration. Protein expression data derived from the Human Protein Atlas suggested EVI2B to be highly expressed in monocytes. Finally, we validated the elevated expression of EVI2B in OS cell lines and OS tissue samples; these results were consistent with those of the analyses of the GSE42352 and Target-OS datasets. Our integrative bioinformatics analysis and experimental results provide clear evidence for the prognostic value of EVI2B in OS and its close relationship with monocyte and macrophage infiltration.

What determines plant species diversity along the Modern Silk Road in the east?

As primary producers, plants provide food, oxygen, and other resources for global ecosystems, and should therefore be given priority in biodiversity protection. Most biodiversity research focuses on biodiversity hotspots, while biodiversity coldspots, such as deserts, are largely ignored. We propose that the factors shaping plant species diversity differ between biodiversity hot spots and cold spots, especially for desert ecosystems. To test this hypothesis, we investigated plant species diversity along the Modern Silk Road in the Northwest China desert, an area characterized by low precipitation, scarce vegetation, a limited number of species, and variable human activities. Surface soil was sampled from 144 plots, environmental DNA (eDNA) was extracted from soil samples, and seed plant species were identified using DNA metabarcoding technology. A total of 671 seed plant species were detected, which was more diverse than indicated by plot survey data. Plant species diversity gradually decreased from east to west along the Silk Road. In this area, temperature determines plant species diversity more than precipitation. Additionally, human activity has altered plant species diversity by introducing crops and invasive plants and eliminating environmentally adapted indigenous plants. Our results demonstrate the potential of eDNA metabarcoding technology for plant species diversity surveying. Desert plants have adapted to dry environments by relying on underground water or utilizing occasional rainfall as ephemerals, which are often not visible during surface surveys because of their short aboveground life cycle but can be detected with eDNA metabarcoding technology. Groundwater maintenance and human activity control are recommended for plant species diversity conservation and desertification control.

Genome Evolution and the Future of Phylogenomics of Non-Avian Reptiles

Non-avian reptiles comprise a large proportion of amniote vertebrate diversity, with squamate reptiles-lizards and snakes-recently overtaking birds as the most species-rich tetrapod radiation. Despite displaying an extraordinary diversity of phenotypic and genomic traits, genomic resources in non-avian reptiles have accumulated more slowly than they have in mammals and birds, the remaining amniotes. Here we review the remarkable natural history of non-avian reptiles, with a focus on the physical traits, genomic characteristics, and sequence compositional patterns that comprise key axes of variation across amniotes. We argue that the high evolutionary diversity of non-avian reptiles can fuel a new generation of whole-genome phylogenomic analyses. A survey of phylogenetic investigations in non-avian reptiles shows that sequence capture-based approaches are the most commonly used, with studies of markers known as ultraconserved elements (UCEs) especially well represented. However, many other types of markers exist and are increasingly being mined from genome assemblies in silico, including some with greater information potential than UCEs for certain investigations. We discuss the importance of high-quality genomic resources and methods for bioinformatically extracting a range of marker sets from genome assemblies. Finally, we encourage herpetologists working in genomics, genetics, evolutionary biology, and other fields to work collectively towards building genomic resources for non-avian reptiles, especially squamates, that rival those already in place for mammals and birds. Overall, the development of this cross-amniote phylogenomic tree of life will contribute to illuminate interesting dimensions of biodiversity across non-avian reptiles and broader amniotes.

The geography of genetic data: Current status and future perspectives

The biogeography field benefits more and more from the growth and application of genetic data such as nucleotide sequences and whole genomes. It has been perceived by scientists that genetic data may be imbalanced among different geographical regions and taxonomic groups. However, the lack of empirical evidence prevents the understanding of current data volume and distribution of genetic data. Based on the construction of a dataset including records for 365 millions of nucleotide sequences of Animalia, Plantae, and Fungi kingdoms, 6 millions of COI sequences of insects, 77 thousands of COI sequences of mammals, 220 thousands of rbcl sequences of Magnoliopsida, and 44 thousands of ITS sequences of Dothideomycetes, here we present evidence on geographical and taxonomical imbalance of the genetic data, identify major gaps and inappropriate practices in the production, application and sharing of genetic data. We then discuss our perspectives on how to fill up gaps and improve the quantity and quality of genetic data.

Decoding the microbial universe with metagenomics: a brief insight

A major part of any biological system on earth involves microorganisms, of which the majority are yet to be cultured. The conventional methods of culturing microbes have given fruitful outcomes yet have limitations. The curiosity for better understanding has led to the development of culture-independent molecular methods that help push aside the roadblocks of earlier methods. Metagenomics unifies the scientific community in search of a better understanding of the functioning of the ecosystem and its component organisms. This approach has opened a new paradigm in advanced research. It has brought to light the vast diversity and novelty among microbial communities and their genomes. This review focuses on the development of this field over time, the techniques and analysis of data generated through sequencing platforms, and its prominent interpretation and representation.

Genome-wide identification and expression analysis of the NAC transcription factor family in Saccharum spontaneum under different stresses

The NAC (NAM, ATAF1/2, and CUC2) transcription factor family is one of the largest families unique to plants and is involved in plant growth and development, organs, morphogenesis, and stress responses. The NAC family has been identified in many plants. As the main source of resistance genes for sugarcane breeding, the NAC gene family in the wild species Saccharum spontaneum has not been systematically studied. In this study, 115 SsNAC genes were identified in the S. spontaneum genome, and these genes were heterogeneously distributed on 25 chromosomes. Phylogenetic analysis divided the SsNAC family members into 18 subgroups, and the gene structure and conserved motif analysis further supported the phylogenetic classification. Four groups of tandemly duplicated genes and nine pairs of segmentally duplicated genes were detected. The SsNAC gene has different expression patterns at different developmental stages of stems and leaves. Further qRT-PCR analysis showed that drought, low-temperature, salinity, pathogenic fungi, and other stresses as well as abscisic acid (ABA) and methyl jasmonate (MeJA) treatments significantly induced the expression of 12 SsNAC genes, indicating that these genes may play a key role in the resistance of S. spontaneum to biotic and abiotic stresses. In summary, the results from this study provide comprehensive information on the NAC transcription factor family, providing a reference for further functional studies of the SsNAC gene.

Noninvasive prenatal diagnosis targeting fetal nucleated red blood cells

Noninvasive prenatal diagnosis (NIPD) aims to detect fetal-related genetic disorders before birth by detecting markers in the peripheral blood of pregnant women, holding the potential in reducing the risk of fetal birth defects. Fetal-nucleated red blood cells (fNRBCs) can be used as biomarkers for NIPD, given their remarkable nature of carrying the entire genetic information of the fetus. Here, we review recent advances in NIPD technologies based on the isolation and analysis of fNRBCs. Conventional cell separation methods rely primarily on physical properties and surface antigens of fNRBCs, such as density gradient centrifugation, fluorescence-activated cell sorting, and magnetic-activated cell sorting. Due to the limitations of sensitivity and purity in Conventional methods, separation techniques based on micro-/nanomaterials have been developed as novel methods for isolating and enriching fNRBCs. We also discuss emerging methods based on microfluidic chips and nanostructured substrates for static and dynamic isolation of fNRBCs. Additionally, we introduce the identification techniques of fNRBCs and address the potential clinical diagnostic values of fNRBCs. Finally, we highlight the challenges and the future directions of fNRBCs as treatment guidelines in NIPD.

Molecular Diagnosis of Hypertrophic Cardiomyopathy (HCM): In the Heart of Cardiac Disease

Hypertrophic cardiomyopathy (HCM) is an inherited myocardial disease with the presence of left ventricular hypertrophy (LVH). The disease is characterized by high locus, allelic and phenotypic heterogeneity, even among members of the same family. The list of confirmed and potentially relevant genes implicating the disease is constantly increasing, with novel genes frequently reported. Heterozygous alterations in the five main sarcomeric genes (MYBPC3, MYH7, TNNT2, TNNI3, and MYL2) are estimated to account for more than half of confirmed cases. The genetic discoveries of recent years have shed more light on the molecular pathogenic mechanisms of HCM, contributing to substantial advances in the diagnosis of the disease. Genetic testing applying next-generation sequencing (NGS) technologies and early diagnosis prior to the clinical manifestation of the disease among family members demonstrate an important improvement in the field.

Ciliate Morpho-Taxonomy and Practical Considerations before Deploying Metabarcoding to Ciliate Community Diversity Surveys in Urban Receiving Waters

Disentangling biodiversity and community assembly effects on ecosystem function has always been an important topic in ecological research. The development and application of a DNA metabarcoding method has fundamentally changed the way we describe prokaryotic communities and estimate biodiversity. Compared to prokaryotes (bacteria and archaea), the eukaryotic microbes (unicellular eukaryotes) also fulfill extremely important ecological functions in different ecosystems regarding their intermediate trophic positions. For instance, ciliated microbes (accounting for a substantial portion of the diversity of unicellular eukaryotes) perform pivotal roles in microbial loops and are essential components in different ecosystems, especially in water purification processes. Therefore, the community composition of ciliated species has been widely utilized as a proxy for water quality and biological assessment in urban river ecosystems and WWTPs (wastewater treatment plants). Unfortunately, investigating the dynamic changes and compositions in ciliate communities relies heavily on existing morpho-taxonomical descriptions, which is limited by traditional microscopic approaches. To deal with this dilemma, we discuss the DNA-based taxonomy of ciliates, the relative merits and challenges of deploying its application using DNA metabarcoding for surveys of ciliate community diversity in urban waterbodies, and provide suggestions for minimizing relevant sources of biases in its implementation. We expect that DNA metabarcoding could untangle relationships between community assembly and environmental changes affecting ciliate communities. These analyses and discussions could offer a replicable method in support of the application of evaluating communities of ciliated protozoa as indicators of urban freshwater ecosystems.

Applying multi-omics techniques to the discovery of biomarkers for acute aortic dissection

Acute aortic dissection (AAD) is a cardiovascular disease that manifests suddenly and fatally. Due to the lack of specific early symptoms, many patients with AAD are often overlooked or misdiagnosed, which is undoubtedly catastrophic for patients. The particular pathogenic mechanism of AAD is yet unknown, which makes clinical pharmacological therapy extremely difficult. Therefore, it is necessary and crucial to find and employ unique biomarkers for Acute aortic dissection (AAD) as soon as possible in clinical practice and research. This will aid in the early detection of AAD and give clear guidelines for the creation of focused treatment agents. This goal has been made attainable over the past 20 years by the quick advancement of omics technologies and the development of high-throughput tissue specimen biomarker screening. The primary histology data support and add to one another to create a more thorough and three-dimensional picture of the disease. Based on the introduction of the main histology technologies, in this review, we summarize the current situation and most recent developments in the application of multi-omics technologies to AAD biomarker discovery and emphasize the significance of concentrating on integration concepts for integrating multi-omics data. In this context, we seek to offer fresh concepts and recommendations for fundamental investigation, perspective innovation, and therapeutic development in AAD.

Reading Information Stored in Synthetic Macromolecules

The storage of information in synthetic (macro)molecules provides an attractive alternative for current archival storage media, and the advancements made within this area have prompted the investigation of such molecules for numerous other applications (e.g., anti-counterfeiting tags, steganography). While different strategies have been described for storing information at the molecular level, this Perspective aims to provide a critical overview of the most prominent approaches that can be utilized for retrieving the encoded information. The major part will focus on the sequence determination of synthetic macromolecules, wherein information is stored by the precise arrangement of constituting monomers, with an emphasis on chemically aided strategies, (tandem) mass spectrometry, and nanopore sensing. In addition, recent progress in utilizing (mixtures of) small molecules for information storage will be discussed. Finally, the closing remarks aim to highlight which strategy we believe is the most suitable for a series of specific applications, and will also touch upon the future research avenues that can be pursued for reading (macro)molecular information.

Neoplasia-associated Chromosome Translocations Resulting in Gene Truncation

Chromosomal translocations in cancer as well as benign neoplasias typically lead to the formation of fusion genes. Such genes may encode chimeric proteins when two protein-coding regions fuse in-frame, or they may result in deregulation of genes via promoter swapping or translocation of the gene into the vicinity of a highly active regulatory element. A less studied consequence of chromosomal translocations is the fusion of two breakpoint genes resulting in an out-of-frame chimera. The breaks then occur in one or both protein-coding regions forming a stop codon in the chimeric transcript shortly after the fusion point. Though the latter genetic events and mechanisms at first awoke little research interest, careful investigations have established them as neither rare nor inconsequential. In the present work, we review and discuss the truncation of genes in neoplastic cells resulting from chromosomal rearrangements, especially from seemingly balanced translocations.

Degradome Assisted Plant MicroRNA Prediction Under Alternative Annotation Criteria

Current microRNA (miRNA) prediction methods are generally based on annotation criteria that tend to miss potential functional miRNAs. Recently, new miRNA annotation criteria have been proposed that could lead to improvements in miRNA prediction methods in plants. Here, we investigate the effect of the new criteria on miRNA prediction in Arabidopsis thaliana and present a new degradome assisted functional miRNA prediction approach. We investigated the effect by applying the new criteria, and a more permissive criteria on miRNA prediction using existing miRNA prediction tools. We also developed an approach to miRNA prediction that is assisted by the functional information extracted from the analysis of degradome sequencing. We demonstrate the improved performance of degradome assisted miRNA prediction compared to unassisted prediction and evaluate the approach using miRNA differential expression analysis. We observe how the miRNA predictions fit under the different criteria and show a potential novel miRNA that has been missed within Arabidopsis thaliana. Additionally, we introduce a freely available software 'PAREfirst' that employs the degradome assisted approach. The study shows that some miRNAs could be missed due to the stringency of the former annotation criteria, and combining a degradome assisted approach with more permissive miRNA criteria can expand confident miRNA predictions.

Artificial intelligence-based approaches for traditional fermented alcoholic beverages' development: review and prospect

Traditional fermented alcoholic beverages (TFABs) have gained widespread acceptance and enjoyed great popularity for centuries. COVID-19 pandemics lead to the surge in health demand for diet, thus TFABs once again attract increased focus for the health benefits. Though the production technology is quite mature, food companies and research institutions are looking for transformative innovation in TFABs to make healthy, nutritious offerings that give a competitive advantage in current beverage market. The implementation of intelligent platforms enables companies and researchers to gather, store and analyze data in a more convenient way. The development of data collection methods contributed to the big data environment of TFABs, providing a fresh perspective that helps brewers to observe and improve the production steps. Among data analytical tools, Artificial Intelligence (AI) is considered to be one of the most promising methodological approaches for big data analytics and decision-making of automated production, and machine learning (ML) is an important method to fulfill the goal. This review describes the development trends and challenges of TFABs in big data era and summarize the application of AI-based methods in TFABs. Finally, we provide perspectives on the potential research directions of new frontiers in application of AI approaches in the supply chain of TFABs.

Polydimethylsiloxane microstructure-induced acoustic streaming for enhanced ultrasonic DNA fragmentation on a microfluidic chip

Next-generation sequencing (NGS) is an essential technology for DNA identification in genomic research. DNA fragmentation is a critical step for NGS and doing this on-chip is of great interest for future integrated genomic solutions. Here we demonstrate fast acoustofluidic DNA fragmentation via ultrasound-actuated elastic polydimethylsiloxane (PDMS) microstructures that induce acoustic streaming and associated shear forces when placed in the field of an ultrasonic transducer. Indeed, acoustic streaming locally generates high tensile stresses that can mechanically stretch and break DNA molecule chains. The improvement in efficiency of the on-chip DNA fragmentation is due to the synergistic effect of these tensile stresses and ultrasonic cavitation phenomena. We tested these microstructure-induced effects in a DNA-containing microfluidic channel both experimentally and by simulation. The DNA fragmentation process was evaluated by measuring the change in the DNA fragment size over time. The chip works well with both long and short DNA chains; in particular, purified lambda (λ) DNA was cut from 48.5 kbp to 3 kbp in one minute with selected microstructures and further down to 300 bp within two and a half minutes. The fragment size of mouse genomic DNA was reduced from 1.4 kbp to 400 bp in one minute and then to 200 bp in two and a half minutes. The DNA fragmentation efficiency of the chip equipped with the PDMS microstructures was twice that of the chip without the microstructures. Exhaustive comparison shows that the on-chip fragmentation performance reaches the level of high-end professional standards. Recently, DNA fragmentation was shown to be enhanced using vibrating air microbubbles when the chip was placed in an acoustic field. We think the microbubble-free microstructure-based device we present is easier to operate and more reliable, as it avoids microbubble preparation and maintenance, while showing high DNA fragmentation performance.

A Review of Omics Studies on Arboviruses: Alphavirus, Orthobunyavirus and Phlebovirus

Since the intricate and complex steps in pathogenesis and host-viral interactions of arthropod-borne viruses or arboviruses are not completely understood, the multi-omics approaches, which encompass proteomics, transcriptomics, genomics and metabolomics network analysis, are of great importance. We have reviewed the omics studies on mosquito-borne viruses of the Togaviridae, Peribuyaviridae and Phenuiviridae families, specifically for Chikungunya, Mayaro, Oropouche and Rift Valley Fever viruses. Omics studies can potentially provide a new perspective on the pathophysiology of arboviruses, contributing to a better comprehension of these diseases and their effects and, hence, provide novel insights for the development of new antiviral drugs or therapies.

A Comprehensive Review of Performance of Next-Generation Sequencing Platforms

Background

Next-generation sequencing methods have been developed and proposed to investigate any query in genomics or clinical activity involving DNA. Technical advancement in these sequencing methods has enhanced sequencing volume to several billion nucleotides within a very short time and low cost. During the last few years, the usage of the latest DNA sequencing platforms in a large number of research projects helped to improve the sequencing methods and technologies, thus enabling a wide variety of research/review publications and applications of sequencing technologies.

Objective

The proposed study is aimed at highlighting the most fast and accurate NGS instruments developed by various companies by comparing output per hour, quality of the reads, maximum read length, reads per run, and their applications in various domains. This will help research institutions and biological/clinical laboratories to choose the sequencing instrument best suited to their environment. The end users will have a general overview about the history of the sequencing technologies, latest developments, and improvements made in the sequencing technologies till now.

Results

The proposed study, based on previous studies and manufacturers' descriptions, highlighted that in terms of output per hour, Nanopore PromethION outperformed all sequencers. BGI was on the second position, and Illumina was on the third position.

Conclusion

The proposed study investigated various sequencing instruments and highlighted that, overall, Nanopore PromethION is the fastest sequencing approach. BGI and Nanopore can beat Illumina, which is currently the most popular sequencing company. With respect to quality, Ion Torrent NGS instruments are on the top of the list, Illumina is on the second position, and BGI DNB is on the third position. Secondly, memory- and time-saving algorithms and databases need to be developed to analyze data produced by the 3rd- and 4th-generation sequencing methods. This study will help people to adopt the best suited sequencing platform for their research work, clinical or diagnostic activities.

Comparative transcriptome provides insights into differentially expressed genes between testis and ovary of Onychostoma macrolepis in reproduction period

The Onychostoma macrolepis (O. macrolepis) is a rare and endangered fishery species inhabiting the river of Qinling Mountains and some flowing freshwaters in China. The declining population of O. macrolepis caused by asynchrony of male and female development prompted us to focus on genetic regulation of its reproduction. In this study, high-throughput RNA-sequencing technology was applied to assemble and annotate the transcriptome of O. macrolepis testis and ovary. The results showed that a number of 338089335 (ovary:163216500, testis:174872835) raw sequences were obtained. After non-redundant analysis, a number of 207826065 (ovary:102334008, testis:105492057) high quality reads were obtained and predicted as unigenes, in which 201,038,682 unigenes were annotated with multiple databases. Taking the ovarian transcriptome as a control, comparative transcriptome analysis showed that 9918 differentially expressed genes (DEGs) up-regulated in the testis and 13,095 DEGs down-regulated. Many DEGs were involved with sex-related GO terms and KEGG pathways, such as oocyte maturation, gonadal development, steroid biosynthesis pathways, MAPK signaling pathway and Wnt signaling pathway. Finally, the expression patterns of 19 unigenes were validated by using quantitative real-time polymerase chain reaction (qRT-PCR). This study illustrates a potential molecular mechanism on the unsynchronized male and female development of the O. macrolepis during the reproduction period in June and provides a theoretical basis for future artificial reproduction.

A Mini-Review Regarding the Clinical Outcomes of In Vitro Fertilization (IVF) Following Pre-Implantation Genetic Testing (PGT)-Next Generation Sequencing (NGS) Approach

Background: PGT-based NGS revolutionized the field of reproductive medicine, becoming an integrated component within current assisted reproductive technology (ART) protocols. Methods: We searched the literature published in the last half a decade in four databases (PubMed/Medline, ISI Web of Knowledge, ScienceDirect, and Scopus) between 2018 and 2022. Results: A total of 1388 articles were filtered, from which 60 met, initially, the eligibility criteria, but only 42 were included (≥100 patients/couples—62,465 patients and 6628 couples in total) in the present mini-review. In total, forty-two (70.0%) reported reproductive outcomes, while eighteen (30.0%) had distinct objectives. Furthermore, n = 1, 1.66% of the studies focused on PGT, n = 1, 1.66% on pre-implantation genetic testing for monogenic disorders (PGT-M), n = 3, 5.0% on pre-implantation genetic testing for structural rearrangements (PGT-SR) and n = 55, 91.66% on pre-implantation genetic testing for aneuploidies (PGT-A). Conclusions: PGT using NGS proved to be an excellent companion that folds within the current ascending tendency among couples that require specialty care. We strongly encourage future studies to provide a systematic overview expanded at a larger scale on the role of the PGT-NGS.

scDLC: a deep learning framework to classify large sample single-cell RNA-seq data

Background

Using single-cell RNA sequencing (scRNA-seq) data to diagnose disease is an effective technique in medical research. Several statistical methods have been developed for the classification of RNA sequencing (RNA-seq) data, including, for example, Poisson linear discriminant analysis (PLDA), negative binomial linear discriminant analysis (NBLDA), and zero-inflated Poisson logistic discriminant analysis (ZIPLDA). Nevertheless, few existing methods perform well for large sample scRNA-seq data, in particular when the distribution assumption is also violated.

Results

We propose a deep learning classifier (scDLC) for large sample scRNA-seq data, based on the long short-term memory recurrent neural networks (LSTMs). Our new scDLC does not require a prior knowledge on the data distribution, but instead, it takes into account the dependency of the most outstanding feature genes in the LSTMs model. LSTMs is a special recurrent neural network, which can learn long-term dependencies of a sequence.

Conclusions

Simulation studies show that our new scDLC performs consistently better than the existing methods in a wide range of settings with large sample sizes. Four real scRNA-seq datasets are also analyzed, and they coincide with the simulation results that our new scDLC always performs the best. The code named “scDLC” is publicly available at https://github.com/scDLC-code/code.

Supplementary Information

The online version contains supplementary material available at (10.1186/s12864-022-08715-1).

A Pipeline for the Development of Microsatellite Markers using Next Generation Sequencing Data

Background: Also known as Simple Sequence Repetitions (SSRs), microsatellites are profoundly informative molecular markers and powerful tools in genetics and ecology studies on plants. Objective: This research presents a workflow for developing microsatellite markers using genome skimming. Methods: The pipeline was proposed in several stages that must be performed sequentially: obtaining DNA sequences, identifying microsatellite regions, designing primers, and selecting candidate microsatellite regions to develop the markers. Our pipeline efficiency was analyzed using Illumina sequencing data from the non-model tree species Pterodon emarginatus Vog. Results: The pipeline revealed 4,382 microsatellite regions and drew 7,411 pairs of primers for P. emarginatus. However, a much larger number of microsatellite regions with the potential to develop markers were discovered from our pipeline. We selected 50 microsatellite regions with high potential for developing markers and organized 29 microsatellite regions in sets for multiplex PCR. Conclusion: The proposed pipeline is a powerful tool for fast and efficient development of microsatellite markers on a large scale in several species, especially nonmodel plant species.

Detecting Genetic Variation of Colonizing Streptococcus agalactiae Genomes in Humans: A Precision Protocol

Deciphering the genotypic diversity of within-individual pathogens and verifying the evolutionary model can help elucidate resistant genotypes, virulent subpopulations, and the mechanism of opportunistic pathogenicity. However, observed polymorphic mutations (PMs) are rare and difficult to be detected in the “dominant-lineage” model of bacterial infection due to the low frequency. The four pooled group B Streptococcus (GBS) samples were collected from the genital tracts of healthy pregnant women, and the pooled samples and the isogenic controls were genomically sequenced. Using the PMcalling program, we detected the PMs in samples and compared the results between two technical duplicates, GBS-M001T and GBS-M001C. Tested with simulated datasets, the PMcalling program showed high sensitivity especially in low-frequency PMs and reasonable specificity. The genomic sequence data from pooled samples of GBS colonizing carrier pregnant women were analyzed, and few high-frequency PMs and some low-frequency PMs were discovered, indicating a dominant-lineage evolution model. The PMs mainly were nonsynonymous and enriched in quorum sensing, glycolysis/gluconeogenesis, ATP-binding cassette (ABC) transporters, etc., suggesting antimicrobial or environmental selective pressure. The re-analysis of the published Burkholderia dolosa data showed a diverse-community model, and only a few low-frequency PMs were shared between different individuals. Genes of general control non-repressible 5-related N-acetyltransferases family, major facilitator superfamily (MFS) transporter, and ABC transporter were positive selection candidates. Our findings indicate an unreported nature of the dominant-lineage model of GBS colonization in healthy women, and a formerly not observed mutation pool in a colonized microbial community, possibly maintained by selection pressure.

Advances in multiplex molecular detection technologies for harmful algae

As the eutrophication of natural water bodies becomes more and more serious, the frequency of outbreaks of harmful algal blooms (HABs) mainly formed by harmful algae also increases. HABs have become a global ecological problem that poses a serious threat to human health and food safety. Therefore, it is extremely important to establish methods that can rapidly detect harmful algal species for early warning of HABs. The traditional morphology-based identification method is inefficient and inaccurate. In recent years, the rapid development of molecular biology techniques has provided new ideas for the detection of harmful algae and has become a research hotspot. The current molecular detection methods for harmful algal species mainly include fluorescence in situ hybridization, sandwich hybridization, and quantitative PCR (qPCR), but all of these methods can only detect single harmful algal species at a time. The establishment of methods for the simultaneous detection of multiple harmful algal species has become a new trend in the development of molecular detection technology because various harmful algal species may coexist in the natural water environment. The established molecular techniques for multiple detections of harmful algae mainly include gene chip, multiplex PCR, multiplex qPCR, massively parallel sequencing, antibody chip, and multiple isothermal amplification. This review mainly focuses on the principles, advantages and disadvantages, application progress, and application prospects of these multiple detection technologies, aiming at providing effective references not only for the fisheries but also for economic activities, environment, and human health.