Unveiling microbial diversity: harnessing long-read sequencing technology.

Long-read sequencing has recently transformed metagenomics, enhancing strain-level pathogen characterization, enabling accurate and complete metagenome-assembled genomes, and improving microbiome taxonomic classification and profiling. These advancements are not only due to improvements in sequencing accuracy, but also happening across rapidly changing analysis methods. In this Review, we explore long-read sequencing's profound impact on metagenomics, focusing on computational pipelines for genome assembly, taxonomic characterization and variant detection, to summarize recent advancements in the field and provide an overview of available analytical methods to fully leverage long reads. We provide insights into the advantages and disadvantages of long reads over short reads and their evolution from the early days of long-read sequencing to their recent impact on metagenomics and clinical diagnostics. We further point out remaining challenges for the field such as the integration of methylation signals in sub-strain analysis and the lack of benchmarks.

Sensitivity and consistency of long- and short-read metagenomics and epicPCR for the detection of antibiotic resistance genes and their bacterial hosts in wastewater.

Wastewater surveillance is a powerful tool to assess the risks associated with antibiotic resistance in communities. One challenge is selecting which analytical tool to deploy to measure risk indicators, such as antibiotic resistance genes (ARGs) and their respective bacterial hosts. Although metagenomics is frequently used for analyzing ARGs, few studies have compared the performance of long-read and short-read metagenomics in identifying which bacteria harbor ARGs in wastewater. Furthermore, for ARG host detection, untargeted metagenomics has not been compared to targeted methods such as epicPCR. Here, we 1) evaluated long-read and short-read metagenomics as well as epicPCR for detecting ARG hosts in wastewater, and 2) investigated the host range of ARGs across the wastewater treatment plant (WWTP) to evaluate host proliferation. Results highlighted long-read revealed a wider range of ARG hosts compared to short-read metagenomics. Nonetheless, the ARG host range detected by long-read metagenomics only represented a subset of the hosts detected by epicPCR. The ARG-host linkages across the influent and effluent of the WWTP were characterized. Results showed the ARG-host phylum linkages were relatively consistent across the WWTP, whereas new ARG-host species linkages appeared in the WWTP effluent. The ARG-host linkages of several clinically relevant species found in the effluent were identified.

Adverse effects of plastic leachate and its component 2,4-DTBP on the early development of zebrafish embryos.

Plastic waste has become a global environmental problem threatening the health of aquatic organisms especially via leachate. In this study, the test of zebrafish embryo showed adverse effects of leachate from some agricultural mulching films after UV light aging for 60 h. A typical phenolic antioxidant 2,4-di-tert-butylphenol (2,4-DTBP) was detected in the leachate and tested further for the zebrafish embryo biotoxicity. The microplastic leachate (6, 8 g/L, mass concentration measured by weight of plastic) increased the death and malformation rates, and reduced the hatching rate, heart rate, and body length of zebrafish larvae in the 96-hour early development period. Similar adverse effects were also caused by the 2,4-DTBP (0.01, 0.1, 1.0 mg/L, corresponding to 0.049, 0.49, and 4.85 µM) to some degree but could not completely explain the significant influences caused by the plastic leachate. Transcriptome analysis of zebrafish embryos exposed to the 2,4-DTBP for 96 h showed that the protein, fat, and carbohydrate digestion and absorption pathways, pancreatic secretion, PPAR signaling pathway, tryptophan metabolism, and adipocytokine signaling pathway were considerably down-regulated, but the cholesterol metabolism pathway was up-regulated in larval zebrafish. The altered transcriptional expression of mRNA at early development stage (96 h post fertilization) of zebrafish suggested that the 2,4-DTBP caused reduction of digestive capacity and pancreatic secretory function, and adversely affected processes associated with energy metabolism and glycolipid metabolism of larval zebrafish. This study helps us further understanding the effects of plastic leachate on the early development of fishes.

Olivar: automated variant aware primer design for multiplex tiled amplicon sequencing of pathogens.

Tiled amplicon sequencing has served as an essential tool for tracking the spread and evolution of pathogens. Over 2 million complete SARS-CoV-2 genomes are now publicly available, most sequenced and assembled via tiled amplicon sequencing. While computational tools for tiled amplicon design exist, they require downstream manual optimization both computationally and experimentally, which is slow and costly. Here we present Olivar, a first step towards a fully automated, variant-aware design of tiled amplicons for pathogen genomes. Olivar converts each nucleotide of the target genome into a numeric risk score, capturing undesired sequence features that should be avoided. In a direct comparison with PrimalScheme, we show that Olivar has fewer SNPs overlapping with primers and predicted PCR byproducts. We also compared Olivar head-to-head with ARTIC v4.1, the most widely used primer set for SARS-CoV-2 sequencing, and show Olivar yields similar read mapping rates (~90%) and better coverage to the manually designed ARTIC v4.1 amplicons. We also evaluated Olivar on real wastewater samples and found that Olivar had up to 3-fold higher mapping rates while retaining similar coverage. In summary, Olivar automates and accelerates the generation of tiled amplicons, even in situations of high mutation frequency and/or density. Olivar is available as a web application at https://olivar.rice.edu. Olivar can also be installed locally as a command line tool with Bioconda. Source code, installation guide and usage are available at https://github.com/treangenlab/Olivar.

Cell cycle of microalga Isochrysis galbana arrested by neurotoxin ß-N-methylamino-l-alanine and corresponding molecular mechanisms.

The phycotoxin ß-N-methylamino-l-alanine (BMAA) has attracted attention due to its risks to marine organisms and human health. In this study, approximately 85 % of synchronized cells of the marine microalga Isochrysis galbana were arrested at the cell cycle G1 phase by BMAA at 6.5 µM for a 24-h exposure. The concentration of chlorophyll a (Chl a) gradually decreased, while the maximum quantum yield of PSII (Fv/Fm), the maximum relative electron transport rate (rETRmax), light utilization efficiency (α) and half-saturated light irradiance (Ik) reduced early and recovered gradually in I. galbana exposed to BMAA in 96-h batch cultures. Transcriptional expression of I. galbana analyzed at 10, 12, and 16 h disclosed multiple mechanisms of BMAA to suppress the microalgal growth. Production of ammonia and glutamate was limited by the down-regulation of nitrate transporters, glutamate synthase, glutamine synthetase, cyanate hydrolase, and formamidase. Diverse extrinsic proteins related to PSII, PSI, cytochrome b6f complex, and ATPase were influenced by BMAA at transcriptional level. Suppression of the DNA replication and mismatch repair pathways increased the accumulation of misfolded proteins, which was reflected by the up-regulated expression of proteasome to accelerate proteolysis. This study improves our understanding of the chemical ecology impacts of BMAA in marine ecosystems.

Comprehensive analysis and accurate quantification of unintended large gene modifications induced by CRISPR-Cas9 gene editing.

Most genome editing analyses to date are based on quantifying small insertions and deletions. Here, we show that CRISPR-Cas9 genome editing can induce large gene modifications, such as deletions, insertions, and complex local rearrangements in different primary cells and cell lines. We analyzed large deletion events in hematopoietic stem and progenitor cells (HSPCs) using different methods, including clonal genotyping, droplet digital polymerase chain reaction, single-molecule real-time sequencing with unique molecular identifier, and long-amplicon sequencing assay. Our results show that large deletions of up to several thousand bases occur with high frequencies at the Cas9 on-target cut sites on the HBB (11.7 to 35.4%), HBG (14.3%), and BCL11A (13.2%) genes in HSPCs and the PD-1 (15.2%) gene in T cells. Our findings have important implications to advancing genome editing technologies for treating human diseases, because unintended large gene modifications may persist, thus altering the biological functions and reducing the available therapeutic alleles.

The third international hackathon for applying insights into large-scale genomic composition to use cases in a wide range of organisms.

In October 2021, 59 scientists from 14 countries and 13 U.S. states collaborated virtually in the Third Annual Baylor College of Medicine & DNANexus Structural Variation hackathon. The goal of the hackathon was to advance research on structural variants (SVs) by prototyping and iterating on open-source software. This led to nine hackathon projects focused on diverse genomics research interests, including various SV discovery and genotyping methods, SV sequence reconstruction, and clinically relevant structural variation, including SARS-CoV-2 variants. Repositories for the projects that participated in the hackathon are available at https://github.com/collaborativebioinformatics.

The Precipitation Behavior of a Cu-Ni-Si Alloy with Cr Addition Prepared by Heating-Cooling Combined Mold (HCCM) Continuous Casting.

A Cu-Ni-Si alloy containing (Ni + Si) ≥ 5 wt.%, with the addition of Cr, is fabricated by HCCM continuous casting and two steps of aging treatment. The evolution of the microstructures and precipitations, as well as the effect of Cr atoms, is studied in this paper. An excellent combination of mechanical property (hardness HV 250-270) and electrical conductivity (46-47 %IACS) is obtained by the first step aging at 500 °C for 0.25 h and the second step aging at 450 °C for 1 h. The cold rolling and aging process are directly conducted on the solution treated specimens fabricated by HCCM continuous casting process without hot deformation, since the excellent homogeneity of matrix is obtained by solution treatment with δ-Ni2Si precipitates dissolved. It is found that the formation of discontinuous precipitation is suppressed by the formation of Cr3Si cores of 5-10 nm before the formation δ-Ni2Si. Then, the nucleation and growth of δ-Ni2Si precipitates occurs around the boundaries of these Cr3Si cores, leading to an enhanced nucleation rate. This study provides a promising direction for the design and optimization of Cu-Ni-Si alloys based on the further understanding of the effect of the addition of Cr.

KOMB: K-core based de novo characterization of copy number variation in microbiomes.

Characterizing metagenomes via kmer-based, database-dependent taxonomic classification has yielded key insights into underlying microbiome dynamics. However, novel approaches are needed to track community dynamics and genomic flux within metagenomes, particularly in response to perturbations. We describe KOMB, a novel method for tracking genome level dynamics within microbiomes. KOMB utilizes K-core decomposition to identify Structural variations (SVs), specifically, population-level Copy Number Variation (CNV) within microbiomes. K-core decomposition partitions the graph into shells containing nodes of induced degree at least K, yielding reduced computational complexity compared to prior approaches. Through validation on a synthetic community, we show that KOMB recovers and profiles repetitive genomic regions in the sample. KOMB is shown to identify functionally-important regions in Human Microbiome Project datasets, and was used to analyze longitudinal data and identify keystone taxa in Fecal Microbiota Transplantation (FMT) samples. In summary, KOMB represents a novel graph-based, taxonomy-oblivious, and reference-free approach for tracking CNV within microbiomes. KOMB is open source and available for download at https://gitlab.com/treangenlab/komb.

Prevalence and distribution of domoic acid and cyclic imines in bivalve mollusks from Beibu Gulf, China.

Beibu Gulf is an important shellfish aquaculture area in the northwest of the South China Sea, China. In this study, the toxin profile and spatial-temporal distribution of domoic acid (DA) and 10 lipophilic phycotoxins were systematically analyzed in the bivalve mollusks collected in Beibu Gulf from October 2018 to October 2020. Neurotoxin DA was first detected in the mollusks from the investigative regions with a prevalence of 17.7%, peaking at 401 µg kg-1. Cyclic imines (CIs) including gymnodimine-A (GYM-A, 46.6%) and 13-desmethyl-spirolide-C (SPX1, 15.8%) predominated the lipophilic phycotoxins in shellfish, peaking at 10.1 µg kg-1 and 19.6 µg kg-1, respectively. Gymnodimine-A partially accompanied by SPX1 was detected in all batches of shellfish samples, suggesting that Alexandrium ostenfeldii and Karenia selliformis were possible sources of CIs-group toxins in Beibu Gulf. During the investigative period, relatively higher levels of DA occurred in shellfishes from March to August, while slightly higher contents of CIs in mollusks appeared in October and December. Spatial distribution of the targeted phycotoxins demonstrated that shellfishes tended to accumulate relatively higher contents of toxins in Lianzhou, Qinzhou and Tieshan bays.

Vulcan: Improved long-read mapping and structural variant calling via dual-mode alignment.

BACKGROUND: Long-read sequencing has enabled unprecedented surveys of structural variation across the entire human genome. To maximize the potential of long-read sequencing in this context, novel mapping methods have emerged that have primarily focused on either speed or accuracy. Various heuristics and scoring schemas have been implemented in widely used read mappers (minimap2 and NGMLR) to optimize for speed or accuracy, which have variable performance across different genomic regions and for specific structural variants. Our hypothesis is that constraining read mapping to the use of a single gap penalty across distinct mutational hot spots reduces read alignment accuracy and impedes structural variant detection. FINDINGS: We tested our hypothesis by implementing a read-mapping pipeline called Vulcan that uses two distinct gap penalty modes, which we refer to as dual-mode alignment. The high-level idea is that Vulcan leverages the computed normalized edit distance of the mapped reads via minimap2 to identify poorly aligned reads and realigns them using the more accurate yet computationally more expensive long-read mapper (NGMLR). In support of our hypothesis, we show that Vulcan improves the alignments for Oxford Nanopore Technology long reads for both simulated and real datasets. These improvements, in turn, lead to improved accuracy for structural variant calling performance on human genome datasets compared to either of the read-mapping methods alone. CONCLUSIONS: Vulcan is the first long-read mapping framework that combines two distinct gap penalty modes for improved structural variant recall and precision. Vulcan is open-source and available under the MIT License at https://gitlab.com/treangenlab/vulcan.

deGSM: Memory Scalable Construction Of Large Scale de Bruijn Graph.

The de Bruijn graph, a fundamental data structure to represent and organize genome sequence, plays important roles in various kinds of sequence analysis tasks. With the rapid development of HTS data and ever-increasing number of assembled genomes, there is a high demand to construct the very large de Bruijn graph for sequences up to Tera-base-pair level. Current approaches may have unaffordable memory footprints to handle such a large de Bruijn graph. We propose a lightweight parallel de Bruijn graph construction approach: de Bruijn Graph Constructor in Scalable Memory (deGSM). The main idea of deGSM is to efficiently construct the Burrows-Wheeler Transformation (BWT) of the unipaths of the de Bruijn graph in constant RAM space and transform the BWT into the original unitigs. The experimental results demonstrate that, just with a commonly available machine, deGSM is able to handle very large genome sequence(s), e.g., the contigs (305 Gbp) and scaffolds (1.1 Tbp) recorded in GenBank database and Picea abies HTS dataset (9.7 Tbp). Moreover, deGSM also has faster or comparable construction speed compared with state-of-the-art approaches. With its high scalability and efficiency, deGSM has enormous potential in many large scale genomics studies. The deGSM is publicly available at: https://github.com/hitbc/deGSM.

SpliVert: A Protein Multiple Sequence Alignment Refinement Method Based on Splitting-Splicing Vertically.

BACKGROUND: Multiple Sequence Alignment (MSA) is a fundamental task in bioinformatics and is required for many biological analysis tasks. The more accurate the alignments are, the more credible the downstream analyses. Most protein MSA algorithms realign an alignment to refine it by dividing it into two groups horizontally and then realign the two groups. However, this strategy does not consider that different regions of the sequences have different conservation; this property may lead to incorrect residue-residue or residue-gap pairs, which cannot be corrected by this strategy. OBJECTIVE: In this article, our motivation is to develop a novel refinement method based on splitting- splicing vertically. METHODS: Here, we present a novel refinement method based on splitting-splicing vertically, called SpliVert. For an alignment, we split it vertically into 3 parts, remove the gap characters in the middle, realign the middle part alone, and splice the realigned middle parts with the other two initial pieces to obtain a refined alignment. In the realign procedure of our method, the aligner will only focus on a certain part, ignoring the disturbance of the other parts, which could help fix the incorrect pairs. RESULTS: We tested our refinement strategy for 2 leading MSA tools on 3 standard benchmarks, according to the commonly used average SP (and TC) score. The results show that given appropriate proportions to split the initial alignment, the average scores are increased comparably or slightly after using our method. We also compared the alignments refined by our method with alignments directly refined by the original alignment tools. The results suggest that using our SpliVert method to refine alignments can also outperform direct use of the original alignment tools. CONCLUSION: The results reveal that splitting vertically and realigning part of the alignment is a good strategy for the refinement of protein multiple sequence alignments.

Fast read alignment with incorporation of known genomic variants.

BACKGROUND: Many genetic variants have been reported from sequencing projects due to decreasing experimental costs. Compared to the current typical paradigm, read mapping incorporating existing variants can improve the performance of subsequent analysis. This method is supposed to map sequencing reads efficiently to a graphical index with a reference genome and known variation to increase alignment quality and variant calling accuracy. However, storing and indexing various types of variation require costly RAM space. METHODS: Aligning reads to a graph model-based index including the whole set of variants is ultimately an NP-hard problem in theory. Here, we propose a variation-aware read alignment algorithm (VARA), which generates the alignment between read and multiple genomic sequences simultaneously utilizing the schema of the Landau-Vishkin algorithm. VARA dynamically extracts regional variants to construct a pseudo tree-based structure on-the-fly for seed extension without loading the whole genome variation into memory space. RESULTS: We developed the novel high-throughput sequencing read aligner deBGA-VARA by integrating VARA into deBGA. The deBGA-VARA is benchmarked both on simulated reads and the NA12878 sequencing dataset. The experimental results demonstrate that read alignment incorporating genetic variation knowledge can achieve high sensitivity and accuracy. CONCLUSIONS: Due to its efficiency, VARA provides a promising solution for further improvement of variant calling while maintaining small memory footprints. The deBGA-VARA is available at: https://github.com/hitbc/deBGA-VARA.

Long-Read Based Novel Sequence Insertion Detection With rCANID.

Novel sequence insertion (NSI) is an essential category of genome structural variations (SVs), which represents DNA segments absent from the reference genome assembly. It has important biological functions and strong correlation with phenotypes and diseases. The rapid development of long-read sequencing technologies provides the opportunities to discover NSIs more sensitively, since the much longer reads are helpful for the assembly and location of the novel sequences. However, most of state-of-the-art long-read based SV detection approaches are in generic design to detect various kinds of SVs, and they are either not suited to detect NSIs or computationally expensive. Herein, we propose read clustering and assembly-based novel insertion detection tool (rCANID). It applies tailored chimerically aligned and unaligned read clustering and lightweight local assembly methods to reconstruct inserted sequences with low computational cost. Benchmarks on both simulated and real datasets demonstrate that rCANID can discover NSIs sensitively and efficiently, especially for NSI events with long inserted sequences which is still a non-trivial task for state-of-the-art approaches. With its good NSI detection ability, rCANID is suited to be integrated into computational pipelines to play important roles in many cutting-edge genomics studies.

Comparative characterization of novel ene-reductases from cyanobacteria.

The growing importance of biocatalysis in the syntheses of enantiopure molecules results from the benefits of enzymes regarding selectivity and specificity of the reaction and ecological issues of the process. Ene-reductases (ERs) from the old yellow enzyme family have received much attention in the last years. These flavo-enzymes catalyze the trans-specific reduction of activated C=C bonds, which is an important reaction in asymmetric synthesis, because up to two stereogenic centers can be created in one reaction. However, limitations of ERs described in the literature such as their moderate catalytic activity and their strong preference for NADPH promote the search for novel ERs with improved properties. In this study, we characterized nine novel ERs from cyanobacterial strains belonging to different taxonomic orders and habitats. ERs were identified with activities towards a broad spectrum of alkenes. The reduction of maleimide was catalyzed with activities of up to 35.5 U mg(-1) using NADPH. Ketoisophorone and (R)-carvone, which were converted to the highly valuable compounds (R)-levodione and (2R,5R)-dihydrocarvone, were reduced with reaction rates of up to 2.2 U mg(-1) with NADPH. In contrast to other homologous ERs from the literature, NADH was accepted at moderate to high rates as well: Enzyme activities of up to 16.7 U mg(-1) were obtained for maleimide and up to 1.3 U mg(-1) for ketoisophorone and (R)-carvone. Additionally, excellent stereoselectivities were achieved in the reduction of (R)-carvone (97-99% de). In particular, AnabaenaER3 from Anabaena variabilis ATCC 29413 and AcaryoER1 from Acaryochloris marina MBIC 11017 were identified as useful biocatalysts. Therefore, novel ERs from cyanobacteria with high catalytic efficiency were added to the toolbox for the asymmetric reduction of alkenes.