Next-Generation DNA Sequencing Methods

Exploring Relevant mRNAs and miRNAs in Injured Urethral Tissues of Rats with High-Throughput Sequencing

Acute urethral injuries caused by urethral endoscopy and other mechanical injuries are the main reasons for secondary infection and late urethral stricture. However, there are no studies to explore the transcriptomic changes in urethral injury and the molecular mechanism of urethral injury, which is important for the treatment and cure of urethral injury. Therefore, we used RNA-seq and sRNA-seq profiles from normal and injured urethral tissues to identify and characterize differentially expressed mRNAs and miRNAs. In total, we found 166 differentially expressed mRNAs, of which 69 were upregulated, and 97 were downregulated in injured urethral tissues. The differentially expressed mRNAs were mainly involved in the positive regulation of epithelial cell differentiation, focal adhesion, cell adhesion molecules, protein activation cascade, complement activation, complement and coagulation cascades, and chemokine-mediated signaling pathway. Additionally, we found six upregulated and four downregulated miRNAs, respectively, in the injured urethral tissues. Notably, their target genes were involved in the vascular endothelial growth factor receptor 2 binding, PI3k-Akt signaling pathway, and Notch signaling pathway. In summary, our results suggest that the cell damage response induced by mechanical injury activates the pathological immune response in a variety of ways in injured urethral tissues.

Whole Genome Sequencing Contributions and Challenges in Disease Reduction Focused on Malaria

Malaria elimination remains an important goal that requires the adoption of sophisticated science and management strategies in the era of the COVID-19 pandemic. The advent of next generation sequencing (NGS) is making whole genome sequencing (WGS) a standard today in the field of life sciences, as PCR genotyping and targeted sequencing provide insufficient information compared to the whole genome. Thus, adapting WGS approaches to malaria parasites is pertinent to studying the epidemiology of the disease, as different regions are at different phases in their malaria elimination agenda. Therefore, this review highlights the applications of WGS in disease management, challenges of WGS in controlling malaria parasites, and in furtherance, provides the roles of WGS in pursuit of malaria reduction and elimination. WGS has invaluable impacts in malaria research and has helped countries to reach elimination phase rapidly by providing required information needed to thwart transmission, pathology, and drug resistance. However, to eliminate malaria in sub-Saharan Africa (SSA), with high malaria transmission, we recommend that WGS machines should be readily available and affordable in the region.

Circular RNAs in cardiovascular diseases

In eukaryotes, precursor mRNAs (pre-mRNAs) produce a unique class of biologically active molecules namely circular RNAs (circRNAs) with a covalently closed-loop structure via back-splicing. Because of this unconventional circular form, circRNAs exhibit much higher stability than linear RNAs due to the resistance to exonuclease degradation and thereby play exclusive cellular regulatory roles. Recent studies have shown that circRNAs are widely expressed in eukaryotes and display tissue- and disease-specific expression patterns, including in the cardiovascular system. Although numerous circRNAs are discovered by in silico methods, a limited number of circRNAs have been studied. This review intends to summarize the current understanding of the characteristics, biogenesis, and functions of circRNAs and delineate the practical approaches for circRNAs investigation. Moreover, we discuss the emerging roles of circRNAs in cardiovascular diseases.

High migratory propensity constitutes a single stock of an exploited cutlassfish species in the Northwest Pacific: A microsatellite approach

Cutlassfishes, also known as hairtails, include multiple predatory fishes of the family Trichiuridae. They constitute a top marine fish commodity globally, yet the knowledge about their composition and intraspecific genetic structures is still limited. Trichiurus japonicus accounts for a major amount in the northwest Pacific fishery. Previous studies based on mitochondrial DNA markers reported incongruences in its population structure, hence prompting the need for high-resolution markers and avoiding possible shortcomings in its management. Here we genotyped ten novel de novo-assembled transcriptome-derived microsatellite markers on a total of 150 samples across five major fishing grounds (encompassing latitudes 22-39°N). These markers presented a high number of alleles and heterozygosity compared to other marine fishes, corresponding to the large effective population size of ~20,000 per location and cohort differentiation. Population structuring analyses suggested T. japonicus to be a homogenous well-mixed population. This configuration is likely attributed to the majority of its effective population migrates across locations, and the absence of oceanographic barriers at the continental shelves. Qingdao with reportedly high ocean productivity could be a genetic pseudosink based on the high heterozygosity and migratory preference. Moreover, the results of sign tests suggest that T. japonicus experienced a recent bottleneck likely concurrent with historical glaciation events. Further, we demonstrated satisfactory cross-amplifications of our markers on several congeners, indicating a great promise to use these markers to study the population genetics of trichiurids. Together, our findings will serve as an essential groundwork for enhancing resource conservation and management of cutlassfishes.

Therapeutic impact and routine application of next‑generation sequencing: A single institute study

Genomic sequencing of tumor tissues provides information on actionable gene aberrations that have diagnostic and therapeutic significance and may guide clinical management through the use of targeted therapies. The indications for these techniques and their possible limitations for application in daily practice should be established as a priority. In the present study, a group of patients with few suitable therapeutic options who were eligible for a next-generation sequencing (NGS) analysis were analyzed, and the molecular targets identified and their therapeutic impact are described. A series of 26 patients treated at the Virgen Macarena Hospital for whom an NGS study was requested between January 2017 and December 2019 were reviewed. Actionable molecular alterations were identified in 20 of the cases, and 4 patients received NGS-guided treatment. NGS techniques represent a novel opportunity for guiding treatment in cancer patients. Patients with few therapeutic alternatives, either due to diagnosis, atypical evolution or resistance to standard therapy, may be suitable candidates.

Genome and Comparative Transcriptome Dissection Provide Insights Into Molecular Mechanisms of Sclerotium Formation in Culinary-Medicinal Mushroom Pleurotus tuber-regium

Pleurotus tuber-regium is an edible and medicinal sclerotium-producing mushroom. The sclerotia of this mushroom also serve as food and folk medicine. Based on the description of its monokaryon genome, sequenced with Illumina and PacBio sequencing technologies, comparative transcriptomic analysis using RNA sequencing (RNA-seq) was employed to study its mechanism of sclerotium formation. The de novo assembled genome is 35.82 Mb in size with a N50 scaffold size of 4.29 Mb and encodes 12,173 putative proteins. Expression analysis demonstrated that 1,146 and 1,249 genes were upregulated and downregulated with the formation of sclerotia, respectively. The differentially expressed genes were associated with substrate decomposition, the oxidation-reduction process, cell wall synthesis, and other biological processes in P. tuber-regium. These genomic and transcriptomic resources provide useful information for the mechanism underlying sclerotium formation in P. tuber-regium.

Probing Genome-Scale Model Reveals Metabolic Capability and Essential Nutrients for Growth of Probiotic Limosilactobacillus reuteri KUB-AC5

Limosilactobacillus reuteri KUB-AC5 displays the hallmark features of probiotic properties for food and feed industries. Optimization of cultivation condition for the industrial production is important to reach cell concentration and cost reduction. Considering the strain-specific growth physiology, metabolic capability, and essential nutrients of L. reuteri KUB-AC5, the genome-scale metabolic model (GSMM) of L. reuteri KUB-AC5 was developed. Hereby, the GSMM of iTN656 was successfully constructed which contained 656 genes, 831 metabolites, and 953 metabolic reactions. The iTN656 model could show a metabolic capability under various carbon sources and guide potentially 14 essential single nutrients (e.g., vitamin B complex and amino acids) and 2 essential double nutrients (pairwise glutamine-glutamate and asparagine-aspartate) for L. reuteri KUB-AC5 growth through single and double omission analysis. Promisingly, the iTN656 model was further integrated with transcriptome data suggesting that putative metabolic routes as preferable paths e.g., sucrose uptake, nucleotide biosynthesis, urea cycle, and glutamine transporter for L. reuteri KUB-AC5 growth. The developed GSMM offers a powerful tool for multi-level omics analysis, enabling probiotic strain optimization for biomass overproduction on an industrial scale.

Bubble-enhanced ultrasonic microfluidic chip for rapid DNA fragmentation

DNA fragmentation is an essential process in developing genetic sequencing strategies, genetic research, as well as for the diagnosis of diseases with a genetic signature like cancer. Efficient on-chip DNA fragmentation protocols would be beneficial to process integration and open new opportunities for microfluidics in genetic applications. Here we present an acoustic microfluidic chip comprising an array of ultrasound-actuated microbubbles located at dedicated positions adjacent to a channel containing the DNA sample solution. The efficiency of the on-chip DNA fragmentation process arises mainly from tensile forces generated by acoustic streaming near the oscillating bubble interfaces, as well as a synergistic effect of streaming stress and ultrasonic cavitation. Acoustic microstreaming and the pressure distribution in the DNA channel were assessed by finite element simulation. We characterized the bubble-enhanced effect by measuring gene fragment size distributions with respect to different ultrasound parameters. For optimized on-chip conditions, purified lambda (λ) DNA (48.5 kbp) could be disrupted to fragments with an average size of 2 kbp after 30 s and down to 300 bp after 90 s. Mouse genomic DNA (1.4 kbp) fragmentation size decreased to 500 bp in 30 s and reduced further to 250 bp in 90 s. Bubble-induced fragmentation was more than 3 times faster than without bubbles. On-chip performance and process yield were found to be comparable to a sophisticated high-end commercial system. In this view, our new bubble-enhanced microfluidic approach is a promising tool for current and next generation sequencing platforms with high efficiency and good capacity. Moreover, the availability of an efficient on-chip DNA fragmentation process opens perspectives for implementing full molecular protocols on a single microfluidic platform.

Bacterial Diversity, Organic Acid, and Flavor Analysis of Dacha and Ercha Fermented Grains of Fen Flavor Baijiu

Fen flavor Baijiu needs two rounds of fermentation, which will obtain Dacha after initial fermentation and Ercha after secondary fermentation. The quality of Baijiu is closely related to the microbes within fermented grains. However, the bacterial diversity in Dacha and Ercha fermented grains of Fen flavor Baijiu has not been reported. In the present study, the structure and diversity of bacteria communities within fermented grains of Fen flavor Baijiu were analyzed and evaluated using MiSeq platform's HTS with a sequencing target of the V3-V4 region of the 16S rRNA gene. Through the analysis of physical and chemical indexes and electronic senses, the relationship between bacterial flora, organic acid, taste, and aroma in fermented grains was clarified. The results indicated that Lactobacillus was the main bacteria in Dacha, and the mean relative content was 97.53%. The bacteria within Ercha samples were Pseudomonas and Bacillus, mean relative content was 37.16 and 28.02%, respectively. The diversity of bacterial communities in Ercha samples was significantly greater than that in Dacha samples. The correlation between Lactobacillus and organic acids, especially lactic acid, led to the difference between Dacha and Ercha organic acids, which also made the pH value of Dacha lower and the sour taste significantly higher than Ercha. Lactobacillus was significantly positively correlated with a variety of aromas, which made Dacha the response value of aromas higher. In addition, Bacillus had a significant positive correlation with bitterness and aromatic compounds, which led to a higher response value of bitterness in Ercha and made it present an aromatic aroma. This study provides an in-depth analysis of the difference between different stages of Fen flavor Baijiu, and theoretical support for the standard production and improvement in quality of Fen flavor Baijiu in the future.

BRCA1/BRCA2 mutation spectrum analysis in South Asia: a systematic review

Objective

Breast cancer (BC) is the most common form of cancer among Asian females. Mutations in the BRCA1/BRCA2 genes are often observed in BC cases and largely increase the lifetime risk of having BC. Because of the paucity of high-quality data on the molecular spectrum of BRCA mutations in South Asian populations, we aimed to explore these mutations among South Asian countries.

Methods

A systematic literature search was performed for the BRCA1 and BRCA2 gene mutation spectrum using electronic databases such as PubMed, EMBASE, and Google Scholar. Twenty studies were selected based on specific inclusion and exclusion criteria.

Results

The 185delAG (c.68_69del) mutation in exon 2 of BRCA1 was the most common recurrent mutation and founder mutation found. Various intronic variants, variants of unknown significance, large genomic rearrangements, and polymorphisms were also described in some studies.

Conclusions

The South Asian population has a wide variety of genetic mutations of BRCA1 and BRCA2 that differ according to countries and ethnicities. A stronger knowledge of various population-specific mutations in these cancer susceptibility genes can help provide efficient strategies for genetic testing.

Advances in Metagenomics and Its Application in Environmental Microorganisms

Metagenomics is a new approach to study microorganisms obtained from a specific environment by functional gene screening or sequencing analysis. Metagenomics studies focus on microbial diversity, community constitute, genetic and evolutionary relationships, functional activities, and interactions and relationships with the environment. Sequencing technologies have evolved from shotgun sequencing to high-throughput, next-generation sequencing (NGS), and third-generation sequencing (TGS). NGS and TGS have shown the advantage of rapid detection of pathogenic microorganisms. With the help of new algorithms, we can better perform the taxonomic profiling and gene prediction of microbial species. Functional metagenomics is helpful to screen new bioactive substances and new functional genes from microorganisms and microbial metabolites. In this article, basic steps, classification, and applications of metagenomics are reviewed.

Review of -omics studies on mosquito-borne viruses of the Flavivirus genus

Arboviruses are transmitted by arthropods (arthropod-borne virus) which can be mosquitoes or other hematophagous arthropods, in which their life cycle occurs before transmission to other hosts. Arboviruses such as Dengue, Zika, Saint Louis Encephalitis, West Nile, Yellow Fever, Japanese Encephalitis, Rocio and Murray Valley Encephalitis viruses are some of the arboviruses transmitted biologically among vertebrate hosts by blood-taking vectors, mainly Aedes and Culex sp., and are associated with neurological, viscerotropic, and hemorrhagic reemerging diseases, posing as significant health and socioeconomic concern, as they become more and more adaptive to new environments, to arthropods vectors and human hosts. One of the main families that include mosquito-borne viruses is Flaviviridae, and here, we review the case of the Flavivirus genus, which comprises the viruses cited above, using a variety of research approaches published in literature, including genomics, transcriptomics, proteomics, metabolomics, etc., to better understand their structures as well as virus-host interactions, which are essential for development of future antiviral therapies.

Omics Technologies for High-Throughput-Screening of Cell-Biomaterial Interactions

Recent research effort in biomaterial development has largely focused on engineering bio-instructive materials to stimulate specific cell signaling. Assessing the biological performance of these materials using time-consuming and trial-and-error traditional...

Comparative evaluation of subgingival microbiome in healthy periodontium and gingivitis using next-generation sequencing technology: A case–control study

Background:

Human dental plaque is a complex microbial community containing millions of species. Gingivitis is a dysregulated immune-inflammatory response induced by dysbiotic plaque biofilm that interrupts symbiosis. The emergence of next-generation sequencing with 16S rRNA gene has greatly contributed in understanding the complexity of microbiota. However, studies focusing on microbiome in gingivitis are limited. The whole bacterial community is important in causing periodontal disease than a small number of periodontal pathogens. In this study, we attempted to profile the subgingival microbiome from individuals with healthy gingiva and in patients with gingivitis using next-generation sequencing technology.

Materials and Methods:

Subgingival plaque samples from 15 healthy periodontium (Group I) and 15 gingivitis (Group II) were collected and 16s rRNA sequencing was done in Illumina Solexa Sequencer. Data analysis using 16s metagenomics tool from BaseSpace onsite operational taxonomic units was assigned to each sequence using HOMD database. Individual variation in the microbiome of the subgingival samples between the two groups was also evaluated.

Results:

The comparison of top 20 species between Group I and Group II revealed no significant species group between them. Synergistetes was absent in Group I samples but found in Group II. At the genus level, HACEK group species were found in both the groups, while Dialister and Aneroglobus were found abundantly in the Group II.

Conclusion:

The presence of unique genera and species seen in Group II samples could point toward a dysbiotic shift that could be taking place in the subgingival environment leading to gingivitis.

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.

Shotgun sequence-based metataxonomic and predictive functional profiles of Pe poke, a naturally fermented soybean food of Myanmar

Pe poke is a naturally fermented sticky soybean food of Myanmar. The present study was aimed to profile the whole microbial community structure and their predictive gene functionality of pe poke samples prepared in different fermentation periods viz. 3 day (3ds), 4 days (4ds), 5 days (5ds) and sun-dried sample (Sds). The pH of samples was 7.6 to 8.7, microbial load was 2.1-3.9 x 108 cfu/g with dynamic viscosity of 4.0±1.0 to 8.0±1.0cP. Metataxonomic profile of pe poke samples showed different domains viz. bacteria (99.08%), viruses (0.65%), eukaryota (0.08%), archaea (0.03%) and unclassified sequences (0.16%). Firmicutes (63.78%) was the most abundant phylum followed by Proteobacteria (29.54%) and Bacteroidetes (5.44%). Bacillus thermoamylovorans was significantly abundant in 3ds and 4ds (p<0.05); Ignatzschineria larvae was significantly abundant in 5ds (p<0.05), whereas, Bacillus subtilis was significantly abundant in Sds (p <0.05). A total of 172 species of Bacillus was detected. In minor abundance, the existence of bacteriophages, archaea, and eukaryotes were also detected. Alpha diversity analysis showed the highest Simpson's diversity index in Sds comparable to other samples. Similarly, a non-parametric Shannon's diversity index was also highest in Sds. Good's coverage of 0.99 was observed in all samples. Beta diversity analysis using PCoA showed no significant clustering. Several species were shared between samples and many species were unique to each sample. In KEGG database, a total number of 33 super-pathways and 173 metabolic sub-pathways were annotated from the metagenomic Open Reading Frames. Predictive functional features of pe poke metagenome revealed the genes for the synthesis and metabolism of wide range of bioactive compounds including various essential amino acids, different vitamins, and enzymes. Spearman's correlation was inferred between the abundant species and functional features.

Genomic signatures of a major adaptive event in the pathogenic fungus Melampsora larici-populina

The recent availability of genome-wide sequencing techniques has allowed systematic screening for molecular signatures of adaptation, including in nonmodel organisms. Host–pathogen interactions constitute good models due to the strong selective pressures that they entail. We focused on an adaptive event which affected the poplar rust fungus Melampsora larici-populina when it overcame a resistance gene borne by its host, cultivated poplar. Based on 76 virulent and avirulent isolates framing narrowly the estimated date of the adaptive event, we examined the molecular signatures of selection. Using an array of genome scan methods based on different features of nucleotide diversity, we detected a single locus exhibiting a consistent pattern suggestive of a selective sweep in virulent individuals (excess of differentiation between virulent and avirulent samples, linkage disequilibrium, genotype–phenotype statistical association, and long-range haplotypes). Our study pinpoints a single gene and further a single amino acid replacement which may have allowed the adaptive event. Although our samples are nearly contemporary to the selective sweep, it does not seem to have affected genome diversity further than the immediate vicinity of the causal locus, which can be explained by a soft selective sweep (where selection acts on standing variation) and by the impact of recombination in mitigating the impact of selection. Therefore, it seems that properties of the life cycle of M. larici-populina, which entails both high genetic diversity and outbreeding, has facilitated its adaptation.

TiMEG: an integrative statistical method for partially missing multi-omics data

Multi-omics data integration is widely used to understand the genetic architecture of disease. In multi-omics association analysis, data collected on multiple omics for the same set of individuals are immensely important for biomarker identification. But when the sample size of such data is limited, the presence of partially missing individual-level observations poses a major challenge in data integration. More often, genotype data are available for all individuals under study but gene expression and/or methylation information are missing for different subsets of those individuals. Here, we develop a statistical model TiMEG, for the identification of disease-associated biomarkers in a case-control paradigm by integrating the above-mentioned data types, especially, in presence of missing omics data. Based on a likelihood approach, TiMEG exploits the inter-relationship among multiple omics data to capture weaker signals, that remain unidentified in single-omic analysis or common imputation-based methods. Its application on a real tuberous sclerosis dataset identified functionally relevant genes in the disease pathway.

Molecular Genetic Techniques in Biomarker Analysis Relevant for Drugs Centrally Approved in Europe

On the basis of scientific evidence, information on the option, recommendation or requirement to test for pharmacogenetic or pharmacogenomic biomarkers is incorporated in the Summary of Product Characteristics of an increasing number of drugs in Europe. A screening of the Genetic Testing Registry (GTR) showed that a variety of molecular genetic testing methods is currently offered worldwide in testing services with regard to according drugs and biomarkers. Thereby, among the methodology indicated in the screened GTR category ‘Molecular Genetics’, next-generation sequencing is applied for identification of the largest proportion of evaluated biomarkers that are relevant for therapeutic management of centrally approved drugs in Europe. However, sufficient information on regulatory clearances, clinical utility, analytical and clinical validity of applied methods is rarely provided.

The core microbiome is responsible for volatile silicon and organic compounds degradation during anoxic lab scale biotrickling filter performance

Volatile silicon compounds present in the biogas of anaerobic digesters can cause severe problems in the energy recovery systems, inducing costly damages. Herein, the microbial community of a lab-scale biotrickling filter (BTF) was studied while testing its biodegradation capacity on octamethylcyclotetrasiloxane (D4) and decamethylcyclopentasiloxane (D5), in the presence of toluene, limonene and hexane. The reactor performance was tested at different empty bed residence times (EBRT) and packing materials. Community structure was analysed by bar-coded amplicon sequencing of the 16S rRNA gene. Microbial diversity and richness were higher in the inoculum and progressively decreased during BTF operation (Simpson's diversity index changing from 0.98-0.90 and Richness from 900 to 200 OTUs). Minimum diversity was found when reactor was operated at relatively low EBRT (7.3 min) using a multicomponent feed. The core community was composed of 36 OTUs (accounting for 55% of total sequences). Packing material played a key role in the community structure. Betaproteobacteriales were dominant in the presence of lava rock and were partially substituted by Corynebacteriales and Rhizobiales when activated carbon was added to the BTF. Despite these changes, a stable and resilient core microbiome was selected defining a set of potentially degrading bacteria for siloxane bioremoval as a complementary alternative to non-regenerative adsorption onto activated carbon.

Development and characterization of novel microsatellite markers in Tilia amurensis Rupr. using next-generation sequencing

BACKGROUND

Tilia amurensis (Malvaceae) is a deciduous broad-leaved tree distributed in Korea, China, and Japan. T. amurensis is used as a honey tree and also as a material for furniture, carving, and pulp. This study aimed to develop and characterize novel microsatellite markers using next-generation sequencing (NGS) of T. amurensis.

METHODS AND RESULTS

NGS analysis using GS-FLX Titanium obtained 629,273 reads, of which 15,795 contigs were assembled with an average length of 830 bp. A total of 4774 microsatellite regions were detected in 3602 of 15,795 contigs. In total, 360 primer sets were designed based on the microsatellite regions. Among them, 15 primer sets were selected as reproducible polymorphic markers and were characterized for three populations of T. amurensis in Korea. The average number of alleles (N_A) was 3.5 in Mt. Hambaek (HB), 3.7 in Mt. Odae (OD), and 3.8 in Mt. Sobaek (SB). The average observed heterozygosity (H_O) and expected heterozygosity (H_E) values were 0.497 and 0.370 in the HB population, 0.470 and 0.372 in the OD population, and 0.524 and 0.410 in the SB population, respectively. The average polymorphic information content (PIC) value of the 15 microsatellite markers was 0.686.

CONCLUSIONS

The novel microsatellite markers will be useful for further studies on genetic diversity evaluation to conserve the genetic resources and natural populations of T. amurensis.

A virtual sequencer reveals the dephasing patterns in error-correction code DNA sequencing

An error-correction code (ECC) sequencing approach has recently been reported to effectively reduce sequencing errors by interrogating a DNA fragment with three orthogonal degenerate sequencing-by-synthesis (SBS) reactions. However, similar to other non-single-molecule SBS methods, the reaction will gradually lose its synchronization within a molecular colony in ECC sequencing. This phenomenon, called dephasing, causes sequencing error, and in ECC sequencing, induces distinctive dephasing patterns. To understand the characteristic dephasing patterns of the dual-base flowgram in ECC sequencing and to generate a correction algorithm, we built a virtual sequencer in silico. Starting from first principles and based on sequencing chemical reactions, we simulated ECC sequencing results, identified the key factors of dephasing in ECC sequencing chemistry and designed an effective dephasing algorithm. The results show that our dephasing algorithm is applicable to sequencing signals with at least 500 cycles, or 1000-bp average read length, with acceptably low error rate for further parity checks and ECC deduction. Our virtual sequencer with our dephasing algorithm can further be extended to a dichromatic form of ECC sequencing, allowing for a potentially much more accurate sequencing approach.

The complex microbiome of caries-active and caries-free supragingival plaques in permanent dentition

Background and Aim

Dental caries is one of the most common diseases seen in the oral cavity in all periods of deciduous, mixed, and permanent dentition. A comprehensive study of the oral microbiome is required to understand its polymicrobial etiology. The aim of this study was to reveal the plaque microbiome of caries-active and caries-free adults.

Materials and Methods

A total of 52 samples were collected from 26 caries-active patients and 26 caries-free controls. Dental supragingival plaque samples were collected from each subject and the bacterial 16S rDNA, expanded V3-V4 region, was amplified using next generation sequencing.

Results

The core microbiome was defined with 235 shared bacteria in genus level, and among all microbiome 14.8% of all bacteria showed significant difference (P < 0.05). The bacteria responsible of caries may be listed as Anaeroglobus, Atopobium, Bifidobacterium, Centipeda, Cryptobacterium, Desulfobulbus, Filifactor, Howardella, Lactobacillus, Leptotrichiaceae (unclassified), Megasphaera, Mycoplasma, Olsenella, Phocaeicola, Propionibacterium, Pseudoramibacter, Scardovia, Schwartzia, Treponema, and Veillonellaceae (unclassified).

Conclusion

The present study provides comprehensive knowledge of the microbiological etiology of caries in permanent dentition.

Immunochromatography-Application Example and POCT Type Genetic Testing

Membrane-based rapid test reagents including immunochromatography are widely used in clinical practice. Recently, high-sensitive reagents based on the immunochromatography method, such as silver amplification method and time resolved fluorescence method for influenza testing, has been developed and early confirmation of infection can be achieved. Furthermore, genetic testing, automated all the steps from extraction till detection, is getting popular. Genetic testing of mycoplasma by Smart Gene Myco system and Coronavirus disease 2019 (COVID-19) test is a good example of membrane-based rapid test reagents. This system uses silica particle-containing membrane filter and enable to shorten the assay time by automates pre-treatment process for removing contamination substances in the sample which affect polymerase-chain-reaction amplification. We hope utilized genetic testing application will help quick confirmation of COVID-19 positive patient and prevent the collapse of medical system under COVID-19 development.

Bacterial and fungal communities of traditional fermented Chinese soybean paste (Doujiang) and their properties

Soybean paste (Doujiang) is one of the traditional fermented foods from China, fermented by various microorganisms. However, the microflora of Doujiang keeps little known. In this study, the microbial communities of seven kinds of representative Doujiang samples were investigated by both culture-independent and culture-dependent methods. We found that core OTUs among seven Doujiang samples were mainly from Bacillus, Pseudomonas, Candida, and Aspergillus according to Illumina sequencing. Every type of Doujiang sample harbored a different composition of microbial community. Doujiang LSJ and LBJ had the highest bacterial and fungal richness and diversity, respectively. The structure of microbial community was remarkably correlated with Doujiang properties-pH, and the content of total protein, soluble protein, amino acid, and total sugar (p < .05). Bacillus spp. were most frequently isolated bacterial species. Fungi of Monascus, Candida, and Aspergillus were also isolated. Eleven microbial strains showed high protease activities to degrade corn proteins, which can form obvious transparent hydrolytic circles in corn gluten meal medium plates. Therefore, microbial communities were supposed to tightly connect to Doujiang type and properties. It is possible to apply potential protein-degrading microbial strains to corn byproducts for protein production in the future study.

Metabolic potential of microbial community and distribution mechanism of Staphylococcus species during broad bean paste fermentation

Although the microbial diversity and structure in bean-based fermented foods have been widely studied, systematic studies on functional microbiota and mechanism of community forms in multi-microbial fermentation systems were still lacking. In this work, the metabolic pathway and functional potential of microbial community in broad bean paste (BBP) were investigated by metagenomics approach, and Staphylococcus, Bacillus, Weissella, Aspergillus and Zygosaccharomyces were found to be the potential predominant populations responsible for substrate alteration and flavor biosynthesis. Among them, Staphylococcus was the most abundant and widespread functional microbe, and closely related Staphylococcus species were diverse and ubiquitously distributed, with the opportunistic pathogen S. gallinarum being the most abundant Staphylococcus specie isolated from BBP. To explain the dominance status of S. gallinarum and species distributions of Staphylococcus genus, we tested the effects of abiotic and biotic factors on three Staphylococcus species using a tractable BBP model, demonstrating that adaptation to environmental conditions (environmental parameters and other functional microbes) led to the dominant position and species coexistence of Staphylococcus, and congeneric competition among Staphylococcus species further shaped ecological distributions of closely related Staphylococcus species. In general, this work revealed the metabolic potential of microbial community and distribution mechanism of Staphylococcus species during BBP fermentation, which could help traditional factories to more precisely control the safety and quality of bean-based fermented foods.

Circular RNAs: characteristics, biogenesis, mechanisms and functions in liver cancer

BACKGROUND

Hepatocellular carcinoma (HCC) is one of the most common malignancies globally. Despite aggressive and multimodal treatment regimens, the overall survival of HCC patients remains poor. MAIN: Circular RNAs (circRNAs) are noncoding RNAs (ncRNAs) with covalently closed structures and tissue- or organ-specific expression patterns in eukaryotes. They are highly stable and have important biological functions, including acting as microRNA sponges, protein scaffolds, transcription regulators, translation templates and interacting with RNA-binding protein. Recent advances have indicated that circRNAs present abnormal expression in HCC tissues and that their dysregulation contributes to HCC initiation and progression. Furthermore, researchers have revealed that some circRNAs might serve as diagnostic biomarkers or drug targets in clinical settings. In this review, we systematically evaluate the characteristics, biogenesis, mechanisms and functions of circRNAs in HCC and further discuss the current shortcomings and potential directions of prospective studies on liver cancer-related circRNAs.

CONCLUSION

CircRNAs are a novel class of ncRNAs that play a significant role in HCC initiation and progression, but their internal mechanisms and clinical applications need further investigation.

Comparative transcriptome analysis of a fan-shaped inflorescence in pineapple using RNA-seq

Pineapple plant usually has a capitulum. However, a fan-shaped inflorescence was exceptionally evolved in pineapple, having multiple crown buds. In order to reveal the molecular mechanisms of the formation of the fan-shaped inflorescence, fruit traits and the transcriptional differences between the fan-shaped inflorescence and the wild-shaped inflorescence pineapples were analyzed in three tissues, i.e., the flower stem apex, the base of the inflorescence, and the inflorescence axis. The weight (i.e., individual yield) of fan-shaped fruit is 4.5 times that of wild-shaped fruit；and non-significant difference in soluble solids, soluble sugar, titratable acid, and Vitamin C was found. Between the fan-shaped inflorescence and wild-shaped inflorescence, a total of 5370 differentially expressed genes were identified across the three tissues. Of these genes, there were 489 overlapping differentially expressed genes in all three tissue comparisons. Between the two pineapples, functional analysis indicated that 444 transcription factors and 206 inflorescence development-related genes were differentially expressed in at least one tissue comparison, while 45 transcription factors and 21 inflorescence development-related genes were overlapped across three tissues. Among the 489 overlapping differentially expressed genes in the three tissue comparisons, excluding the inflorescence development-related genes and transcription factors, 80 of them revealed a higher percentage of involvement in the biological processes relating to response to auxin, and reproductive processes. RNA-seq value and real-time quantitative PCR analysis exhibited the similar gene expression patterns in the three tissues. Our result provided novel cues for understanding the molecular mechanisms of the formation of the fan-shaped inflorescence in pineapple, making a valuable resource for the study of plant breeding and the speciation of pineapple.

A primer on machine learning techniques for genomic applications

High throughput sequencing technologies have enabled the study of complex biological aspects at single nucleotide resolution, opening the big data era. The analysis of large volumes of heterogeneous "omic" data, however, requires novel and efficient computational algorithms based on the paradigm of Artificial Intelligence. In the present review, we introduce and describe the most common machine learning methodologies, and lately deep learning, applied to a variety of genomics tasks, trying to emphasize capabilities, strengths and limitations through a simple and intuitive language. We highlight the power of the machine learning approach in handling big data by means of a real life example, and underline how described methods could be relevant in all cases in which large amounts of multimodal genomic data are available.

Using single-cell sequencing technology to detect circulating tumor cells in solid tumors

Circulating tumor cells are tumor cells with high vitality and high metastatic potential that invade and shed into the peripheral blood from primary solid tumors or metastatic foci. Due to the heterogeneity of tumors, it is difficult for high-throughput sequencing analysis of tumor tissues to find the genomic characteristics of low-abundance tumor stem cells. Single-cell sequencing of circulating tumor cells avoids interference from tumor heterogeneity by comparing the differences between single-cell genomes, transcriptomes, and epigenetic groups among circulating tumor cells, primary and metastatic tumors, and metastatic lymph nodes in patients' peripheral blood, providing a new perspective for understanding the biological process of tumors. This article describes the identification, biological characteristics, and single-cell genome-wide variation in circulating tumor cells and summarizes the application of single-cell sequencing technology to tumor typing, metastasis analysis, progression detection, and adjuvant therapy.

0s and 1s in marine molecular research: a regional HPC perspective

High-performance computing (HPC) systems have become indispensable for modern marine research, providing support to an increasing number and diversity of users. Pairing with the impetus offered by high-throughput methods to key areas such as non-model organism studies, their operation continuously evolves to meet the corresponding computational challenges. Here, we present a Tier 2 (regional) HPC facility, operating for over a decade at the Institute of Marine Biology, Biotechnology, and Aquaculture of the Hellenic Centre for Marine Research in Greece. Strategic choices made in design and upgrades aimed to strike a balance between depth (the need for a few high-memory nodes) and breadth (a number of slimmer nodes), as dictated by the idiosyncrasy of the supported research. Qualitative computational requirement analysis of the latter revealed the diversity of marine fields, methods, and approaches adopted to translate data into knowledge. In addition, hardware and software architectures, usage statistics, policy, and user management aspects of the facility are presented. Drawing upon the last decade's experience from the different levels of operation of the Institute of Marine Biology, Biotechnology, and Aquaculture HPC facility, a number of lessons are presented; these have contributed to the facility's future directions in light of emerging distribution technologies (e.g., containers) and Research Infrastructure evolution. In combination with detailed knowledge of the facility usage and its upcoming upgrade, future collaborations in marine research and beyond are envisioned.

HERON: A Novel Tool Enables Identification of Long, Weakly Enriched Genomic Domains in ChIP-seq Data

The explosive development of next-generation sequencing-based technologies has allowed us to take an unprecedented look at many molecular signatures of the non-coding genome. In particular, the ChIP-seq (Chromatin ImmunoPrecipitation followed by sequencing) technique is now very commonly used to assess the proteins associated with different non-coding DNA regions genome-wide. While the analysis of such data related to transcription factor binding is relatively straightforward, many modified histone variants, such as H3K27me3, are very important for the process of gene regulation but are very difficult to interpret. We propose a novel method, called HERON (HiddEn MaRkov mOdel based peak calliNg), for genome-wide data analysis that is able to detect DNA regions enriched for a certain feature, even in difficult settings of weakly enriched long DNA domains. We demonstrate the performance of our method both on simulated and experimental data.

Genomics pipelines to investigate susceptibility in whole genome and exome sequenced data for variant discovery, annotation, prediction and genotyping

Over the last few decades, genomics is leading toward audacious future, and has been changing our views about conducting biomedical research, studying diseases, and understanding diversity in our society across the human species. The whole genome and exome sequencing (WGS/WES) are two of the most popular next-generation sequencing (NGS) methodologies that are currently being used to detect genetic variations of clinical significance. Investigating WGS/WES data for the variant discovery and genotyping is based on the nexus of different data analytic applications. Although several bioinformatics applications have been developed, and many of those are freely available and published. Timely finding and interpreting genetic variants are still challenging tasks among diagnostic laboratories and clinicians. In this study, we are interested in understanding, evaluating, and reporting the current state of solutions available to process the NGS data of variable lengths and types for the identification of variants, alleles, and haplotypes. Residing within the scope, we consulted high quality peer reviewed literature published in last 10 years. We were focused on the standalone and networked bioinformatics applications proposed to efficiently process WGS and WES data, and support downstream analysis for gene-variant discovery, annotation, prediction, and interpretation. We have discussed our findings in this manuscript, which include but not are limited to the set of operations, workflow, data handling, involved tools, technologies and algorithms and limitations of the assessed applications.

Core of the saliva microbiome: an analysis of the MG-RAST data

Background

Oral microbiota is considered as the second most complex in the human body and its dysbiosis can be responsible for oral diseases. Interactions between the microorganism communities and the host allow establishing the microbiological proles. Identifying the core microbiome is essential to predicting diseases and changes in environmental behavior from microorganisms.

Methods

Projects containing the term “SALIVA”, deposited between 2014 and 2019 were recovered on the MG-RAST portal. Quality (Failed), taxonomic prediction (Unknown and Predicted), species richness (Rarefaction), and species diversity (Alpha) were analyzed according to sequencing approaches (Amplicon sequencing and Shotgun metagenomics). All data were checked for normality and homoscedasticity. Metagenomic projects were compared using the Mann–Whitney U test and Spearman's correlation. Microbiome cores were inferred by Principal Component Analysis. For all statistical tests, p < 0.05 was used.

Results

The study was performed with 3 projects, involving 245 Amplicon and 164 Shotgun metagenome datasets. All comparisons of variables, according to the type of sequencing, showed significant differences, except for the Predicted. In Shotgun metagenomics datasets the highest correlation was between Rarefaction and Failed (r =  − 0.78) and the lowest between Alpha and Unknown (r =  − 0.12). In Amplicon sequencing datasets, the variables Rarefaction and Unknown (r = 0.63) had the highest correlation and the lowest was between Alpha and Predicted (r =  − 0.03). Shotgun metagenomics datasets showed a greater number of genera than Amplicon. Propionibacterium, Lactobacillus, and Prevotella were the most representative genera in Amplicon sequencing. In Shotgun metagenomics, the most representative genera were Escherichia, Chitinophaga, and Acinetobacter.

Conclusions

Core of the salivary microbiome and genera diversity are dependent on the sequencing approaches. Available data suggest that Shotgun metagenomics and Amplicon sequencing have similar sensitivities to detect the taxonomic level investigated, although Shotgun metagenomics allows a deeper analysis of the microorganism diversity. Microbiome studies must consider characteristics and limitations of the sequencing approaches. Were identified 20 genera in the core of saliva microbiome, regardless of the health condition of the host. Some bacteria of the core need further study to better understand their role in the oral cavity.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12903-021-01719-5.

Empirical evaluation of methods for de novo genome assembly

Technologies for next-generation sequencing (NGS) have stimulated an exponential rise in high-throughput sequencing projects and resulted in the development of new read-assembly algorithms. A drastic reduction in the costs of generating short reads on the genomes of new organisms is attributable to recent advances in NGS technologies such as Ion Torrent, Illumina, and PacBio. Genome research has led to the creation of high-quality reference genomes for several organisms, and de novo assembly is a key initiative that has facilitated gene discovery and other studies. More powerful analytical algorithms are needed to work on the increasing amount of sequence data. We make a thorough comparison of the de novo assembly algorithms to allow new users to clearly understand the assembly algorithms: overlap-layout-consensus and de-Bruijn-graph, string-graph based assembly, and hybrid approach. We also address the computational efficacy of each algorithm's performance, challenges faced by the assem- bly tools used, and the impact of repeats. Our results compare the relative performance of the different assemblers and other related assembly differences with and without the reference genome. We hope that this analysis will contribute to further the application of de novo sequences and help the future growth of assembly algorithms.

Nanopore Technology and Its Applications in Gene Sequencing

In recent years, nanopore technology has become increasingly important in the field of life science and biomedical research. By embedding a nano-scale hole in a thin membrane and measuring the electrochemical signal, nanopore technology can be used to investigate the nucleic acids and other biomacromolecules. One of the most successful applications of nanopore technology, the Oxford Nanopore Technology, marks the beginning of the fourth generation of gene sequencing technology. In this review, the operational principle and the technology for signal processing of the nanopore gene sequencing are documented. Moreover, this review focuses on the applications using nanopore gene sequencing technology, including the diagnosis of cancer, detection of viruses and other microbes, and the assembly of genomes. These applications show that nanopore technology is promising in the field of biological and biomedical sensing.

Thread-based isotachophoresis for DNA extraction and purification from biological samples

A rapid, low-cost, and disposable microfluidic thread-based isotachophoresis method was developed for the purification and preconcentration of nucleic acids from biological samples, prior to their extraction and successful analysis using quantitative polymerase chain reaction (qPCR). This approach extracts and concentrates protein-free DNA from the terminating electrolyte buffer, via a continuous sampling approach, resulting in significant focussing of the extracted DNA upon a 6 cm length nylon thread. The platform was optimised using the preconcentration of a fluorescent dye, showing a 600-fold concentration capacity within <5 min. The system was then applied to the one-step extraction of lambda DNA - an E. coli bacteriophage - spiked into whole blood, exhibiting the exclusion of PCR inhibitors. The extraction efficiency from the thread material following concentration was consistent, between 94.4-113.9%. The determination of lambda DNA in whole blood was achieved within a linear range of 1.0-1 × 105 fg μL-1 (20-2 × 106 copies per μL). This technique demonstrates great potential for the development of thread-based affordable analytical and diagnostic devices based upon DNA and RNA isolation.

pH Changes Have a Profound Effect on Gene Expression, Hydrolytic Enzyme Production, and Dimorphism in Saccharomycopsis fibuligera

Saccharomycopsis fibuligera is an amylolytic yeast that plays an important role within nuruk (a traditional Korean fermentation starter) used for the production of makgeolli (Korean rice wine), which is characterized by high acidity. However, the effect of pH change (neutral to acidic) on the yeast cell to hyphal transition and carbohydrate-hydrolyzing enzyme activities for S. fibuligera has not been investigated yet. In this study, S. fibuligera strains were cultured under the different pH conditions, and the effect on the enzyme production and gene expression were investigated. An acidic pH induced a hyphal transition from yeast cell of S. fibuligera KPH12 and the hybrid strain KJJ81. In addition, both strains showed a gradual decrease in the ability to degrade starch and cellulose as the pH went down. Furthermore, a transcriptome analysis demonstrated that the pH decline caused global expression changes in genes, which were classified into five clusters. Among the differentially expressed genes (DEGs) under acidic pH, the downregulated genes were involved in protein synthesis, carbon metabolism, and RIM101 and cAMP-PKA signaling transduction pathways for the yeast-hyphal transition. A decrease in pH induced a dimorphic lifestyle switch from yeast cell formation to hyphal growth in S. fibuligera and caused a decrease in carbohydrate hydrolyzing enzyme production, as well as marked changes in the expression of genes related to enzyme production and pH adaptation. This study will help to elucidate the mechanism of adaptation of S. fibuligera to acidification that occur during the fermentation process of makgeolli using nuruk.

Cutaneous Melanoma Classification: The Importance of High-Throughput Genomic Technologies

Cutaneous melanoma is an aggressive tumor responsible for 90% of mortality related to skin cancer. In the recent years, the discovery of driving mutations in melanoma has led to better treatment approaches. The last decade has seen a genomic revolution in the field of cancer. Such genomic revolution has led to the production of an unprecedented mole of data. High-throughput genomic technologies have facilitated the genomic, transcriptomic and epigenomic profiling of several cancers, including melanoma. Nevertheless, there are a number of newer genomic technologies that have not yet been employed in large studies. In this article we describe the current classification of cutaneous melanoma, we review the current knowledge of the main genetic alterations of cutaneous melanoma and their related impact on targeted therapies, and we describe the most recent high-throughput genomic technologies, highlighting their advantages and disadvantages. We hope that the current review will also help scientists to identify the most suitable technology to address melanoma-related relevant questions. The translation of this knowledge and all actual advancements into the clinical practice will be helpful in better defining the different molecular subsets of melanoma patients and provide new tools to address relevant questions on disease management. Genomic technologies might indeed allow to better predict the biological - and, subsequently, clinical - behavior for each subset of melanoma patients as well as to even identify all molecular changes in tumor cell populations during disease evolution toward a real achievement of a personalized medicine.

Metagenomic analysis of black-stained plaques in permanent dentition

OBJECTIVES

We aimed to determine the aetiologic agent responsible for black staining of permanent dentition using next-generation sequencing and determine the relationship between caries and black stains.

MATERIALS AND METHODS

A total of 52 systemically healthy patients with black-stained and caries-free (n = 13), black-stained and carious (n = 13), black stain-free and caries-free (n = 13), and black stain-free and carious (n = 13) teeth were enrolled in the study. The International Caries Detection and Assessment System (ICDAS II) was used for caries classification. Between 08:00 and 10:00, supragingival plaque samples were collected after a minimum of 8-12 h of accumulation and DNA samples were isolated. The samples were processed using the ZymoBIOMICS™ Service. Bioinformatics analysis was performed using mothur at usegalaxy.org. Data were analysed statistically using the Pearson chi-square and Fisher tests.

RESULTS

The number of caries-free teeth (ICDAS 0, 1, and 2) was significantly higher in patients with black stains (p = 0.007).Capnocytophaga (4.8 %), Corynebacterium (3.9 %), and Neisseria (5.4 %) species were the most abundant among all black-stained plaques (carious and caries-free) (p < 0.05). Capnocytophaga (10.8 %), Cardiobacterium (3.6 %), and Rothia (1.72 %) species were detected in the black-stained plaques of caries-free patients (p < 0.05).

CONCLUSION

This study is one of the first studies examining the microbial composition of dental plaques with black staining in carious and caries-free adult patients using next generation sequencing technology. In the presence of black staining, plaques have an ultimate complex microbial structure. A lower caries burden was noted in the presence of black staining.

Category encoding method to select feature genes for the classification of bulk and single-cell RNA-seq data

Bulk and single-cell RNA-seq (scRNA-seq) data are being used as alternatives to traditional technology in biology and medicine research. These data are used, for example, for the detection of differentially expressed (DE) genes. Several statistical methods have been developed for the classification of bulk and single-cell RNA-seq data. These feature genes are vitally important for the classification of bulk and single-cell RNA-seq data. The majority of genes are not DE and they are thus irrelevant for class distinction. To improve the classification performance and save the computation time, removal of irrelevant genes is necessary. Removal will aid the detection of the important feature genes. Widely used schemes in the literature, such as the BSS/WSS (BW) method, assume that data are normally distributed and may not be suitable for bulk and single-cell RNA-seq data. In this article, a category encoding (CAEN) method is proposed to select feature genes for bulk and single-cell RNA-seq data classification. This novel method encodes categories by employing the rank of sequence samples for each gene in each class. Correlation coefficients are considered for gene and class with the rank of sample and a new rank of category. The highest gene correlation coefficients are considered feature genes, which are the most effective for classifying bulk and single-cell RNA-seq dataset. The sure screening method was also established for rank consistency properties of the proposed CAEN method. Simulation studies show that the classifier using the proposed CAEN method performs better than, or at least as well as, the existing methods in most settings. Existing real datasets were analyzed, with the results demonstrating superior performance of the proposed method over current competitors. The application has been coded into an R package named "CAEN" to facilitate wide use.

Panomics: New Databases for Advancing Cardiology

The multifactorial nature of cardiology makes it challenging to separate noisy signals from confounders and real markers or drivers of disease. Panomics, the combination of various omic methods, provides the deepest insights into the underlying biological mechanisms to develop tools for personalized medicine under a systems biology approach. Questions remain about current findings and anticipated developments of omics. Here, we search for omic databases, investigate the types of data they provide, and give some examples of panomic applications in health care. We identified 104 omic databases, of which 72 met the inclusion criteria: genomic and clinical measurements on a subset of the database population plus one or more omic datasets. Of those, 65 were methylomic, 59 transcriptomic, 41 proteomic, 42 metabolomic, and 22 microbiomic databases. Larger database sample sizes and longer follow-up are often better suited for panomic analyses due to statistical power calculations. They are often more complete, which is important when dealing with large biological variability. Thus, the UK BioBank rises as the most comprehensive panomic resource, at present, but certain study designs may benefit from other databases.

Next-generation microbiology: from comparative genomics to gene function

Microbiology is at a turning point in its 120-year history. Widespread next-generation sequencing has revealed genetic complexity among bacteria that could hardly have been imagined by pioneers such as Pasteur, Escherich and Koch. This data cascade brings enormous potential to improve our understanding of individual bacterial cells and the genetic basis of phenotype variation. However, this revolution in data science cannot replace established microbiology practices, presenting the challenge of how to integrate these new techniques. Contrasting comparative and functional genomic approaches, we evoke molecular microbiology theory and established practice to present a conceptual framework and practical roadmap for next-generation microbiology.

Translocation of a looped polymer threading through a nanopore

Recent experiments reported that the complicated translocation dynamics of a looped DNA chain through a nanopore can be detected by ionic current blockade profiles. Inspired by the experimental results, we systematically study the translocation dynamics of a looped polymer, formed by three building blocks of a loop in the middle and two tails of the same length connected with the loop, by using Langevin dynamics simulations. Based on two entering modes (tail-leading and loop-leading) and three translocation orders (loop-tail-tail, tail-loop-tail, and tail-tail-loop), the translocation of the looped polymer is classified into six translocation pathways, corresponding to different current blockade profiles. The probabilities of the six translocation pathways are dependent on the loop length, polymer length, and pore radius. Moreover, the translocation times of the entire polymer and the loop are investigated. We find that the two translocation times show different dependencies on the translocation pathways and on the lengths of the loop and the entire polymer.

Combined viral quasispecies diversity and hepatitis B core-related antigen predict off-nucleos(t)ide analog durability in HBeAg-negative patients

BACKGROUND

Viral quasispecies dynamics between pre- and post-nucleos(t)ide analog (NA) therapy remains unclear.

AIM

This study aimed to investigate the HBV quasispecies evolution and its relationship with durability of off-therapy responses in HBeAg-negative chronic hepatitis B (CHB) patients who stopped NA therapy.

METHODS

Fifty-four HBeAg-negative CHB patients who stopped NAs, including 19 virological controllers (VC) who maintained serum HBV DNA < 2000 IU/mL beyond 1-year off-therapy, and 35 virological relapsers (VR) experiencing virological relapse within 1-year off-therapy were recruited. Viral quasispecies was analyzed by deep sequencing. Hepatitis B core-related antigen (HBcrAg) and HBsAg were also measured.

RESULTS

VC had significantly higher baseline viral quasispecies diversity of the precore/core gene, measured by nucleotide diversity, than VR. Low baseline viral nucleotide diversity (< 0.01) and high HBcrAg (≧ 2.0 KU/mL), but not HBsAg, at end of treatment (EOT) were significantly associated with higher risk of 1-year virological relapse (hazard ratio [HR] 6.09 and 3.31, respectively). Combination of low baseline viral nucleotide diversity and high HBcrAg at EOT could identify patients at high risk (HR 15.82). Further analysis of the evolution of HBV whole genome showed that HBV nucleotide diversity negatively correlated with serum HBV DNA levels. Notably, the viral quasispecies diversity between pre- and post-NA treatment remained relatively unchanged.

CONCLUSION

Higher baseline HBV quasispecies diversity associates with more durable off-therapy viral suppression in HBeAg-negative CHB patients. Combination of baseline viral nucleotide diversity and HBcrAg at EOT can identify patients at high risk for virological relapse after stopping NAs.

Deep Learning for Reintegrating Biology

The goal of this vision paper is to investigate the possible role that advanced machine learning techniques, especially deep learning, could play in the reintegration of various biological disciplines. To achieve this goal, a series of operational, but admittedly very simplistic, conceptualizations have been introduced: Life has been taken as a multidimensional phenomenon that inhabits three physical dimensions (time, space, and scale) and biological research as establishing connection between different points in the domain of life. Each of these points hence denotes a position in time, space, and scale at which a life phenomenon of interest takes place. Using these conceptualizations, fragmentation of biology can be seen as the result of too few and especially too short-ranged connections. Reintegrating biology could then be accomplished by establishing more, longer ranged connections. Deep learning methods appear to be very well suited for addressing this particular need at this particular time. Not withstanding the numerous unsubstantiated claims regarding the capabilities of AI, deep learning networks represent a major advance in the ability to find complex relationships inside large data sets that would have not been accessible with traditional data analytic methods or to a human observer. In addition, ongoing advances in the automation of taking measurements from phenomena on all levels of biological organization, continue to increase the number of large quantitative data sets that are available. These increasingly common data sets could serve as anchor points for making long-range connections by virtue of deep learning. However, connections within the domain of life are likely to be structured in a highly nonuniform fashion and hence it is necessary to develop methods, e.g., theoretical, computational, and experimental, to determine linkage of biological data sets most likely to provide useful insights on a biological problem using deep learning. Finally, specific deep learning approaches and architectures should be developed to match the needs of reintegrating biology.

Parallel computing for genome sequence processing

The rapid increase of genome data brought by gene sequencing technologies poses a massive challenge to data processing. To solve the problems caused by enormous data and complex computing requirements, researchers have proposed many methods and tools which can be divided into three types: big data storage, efficient algorithm design and parallel computing. The purpose of this review is to investigate popular parallel programming technologies for genome sequence processing. Three common parallel computing models are introduced according to their hardware architectures, and each of which is classified into two or three types and is further analyzed with their features. Then, the parallel computing for genome sequence processing is discussed with four common applications: genome sequence alignment, single nucleotide polymorphism calling, genome sequence preprocessing, and pattern detection and searching. For each kind of application, its background is firstly introduced, and then a list of tools or algorithms are summarized in the aspects of principle, hardware platform and computing efficiency. The programming model of each hardware and application provides a reference for researchers to choose high-performance computing tools. Finally, we discuss the limitations and future trends of parallel computing technologies.

Measure microbial activity driven oxygen transfer in membrane aerated biofilm reactor from supply side

This short communication demonstrates for the first time a solely microbial activity driven oxygen influx across a microporous hollow fibre membrane via tracking changes in volume and gas composition of entrapped air supply. A U-shape manometer was used to directly reflect gas influx due to microbial activities. A pressure difference of several hundred pascal was created to draw oxygen while 25 mg-N/L of ammonium was oxidized into nitrite by active biofilm at a hydraulic retention time of 6 h. Calibrated and normalized gas compositions before and after the experiment were processed to unveil the gas exchange and estimate the actual oxygen influx across the membrane. A solely microbial activity driven oxygen influx of 10.7 mg O₂/m²/h was observed. Measuring oxygen transfer from supply side provides a more straight-forward perspective on the role of active biofilm in membrane aerated biofilm reactor. The capability of the microbial activity to uptake oxygen on its own could potentially lead to greater energy savings in some MABR applications when strict aeration control is not needed.