High-throughput Sequencing Technologies Research Articles

Diversity and Composition of Soil Acidobacterial Communities in Different Temperate Forest Types of Northeast China

To gain an in-depth understanding of the diversity and composition of soil Acidobacteria in five different forest types in typical temperate forest ecosystems and to explore their relationship with soil nutrients. The diversity of soil Acidobacteria was determined by high-throughput sequencing technology. Soil Acidobacteria’s alpha-diversity index and soil nutrient content differed significantly among different forest types. β-diversity and the composition of soil Acidobacteria also varied across forest types. Acidobacterial genera, such as Acidobacteria_Gp1, Acidobacteria_Gp4, and Acidobacteria_Gp17, play key roles in different forests. The RDA analyses pointed out that the soil pH, available nitrogen (AN), carbon to nitrogen (C/N) ratio, available phosphorus (AP), total carbon (TC), and total phosphorus (TP) were significant factors affecting soil Acidobacteria in different forest types. In this study, the diversity and composition of soil Acidobacteria under different forest types in a temperate forest ecosystem were analyzed, revealing the complex relationship between them and soil physicochemical properties. These findings not only enhance our understanding of soil microbial ecology but also provide important guidance for ecological conservation and restoration strategies for temperate forest ecosystems.

[Analysis of the results for genetic disease screening among 1 000 newborns from Huzhou].

To analyze the types and distribution of pathogenic variants for neonatal genetic diseases in Huzhou, Zhejiang Province. One thousand neonates (48 ~ 42 h after birth) born to Huzhou region were selected as the study subjects. Dry blood spot samples were collected from the newborns, and targeted capture high-throughput sequencing was carried out for pathogenic genes underlying 542 inherited diseases. Candidate variants were verified by Sanger sequencing. Among the 1 000 newborns, the male to female ratio was 1.02 : 1.00. No pathogenic variants were detected in 253 cases, whilst 747 cases were found to carry at least one pathogenic variant, which yielded a carrier rate of 74.7%. The most frequently involved pathogenic gene was FLG, followed by GJB2, UGT1A1, USH2A and DUOX2. The variants were classified as homozygous, compound heterozygous, and hemizygous variants. Based on the guidelines from the American College of Medical Genetics and Genomics (ACMG), 213 neonates were verified to have carried pathogenic and/or likely pathogenic variants, with a positive rate of 21.3%. The most commonly involved genes had included UGT1A1, FLG, GJB2, MEFV and G6PD. Newborn screening based on high-throughput sequencing technology can expand the scope of screening and improve the positive predictive value. Genetic counseling based on the results can improve the patients' medical care and reduce neonatal mortality and childhood morbidity, while provide assistance to family members' health management and reproductive decisions.

Bacterial–fungal interactions and response to heavy metal contamination of soil in agricultural areas

IntroductionLong-term heavy metal contamination of soil affects the structure and function of microbial communities. The aim of our study was to investigate the effect of soil heavy metal contamination on microorganisms and the impact of different heavy metal pollution levels on the microbial interactions.MethodsWe collected soil samples and determined soil properties. Microbial diversity was analyzed in two groups of samples using high-throughput sequencing technology. Additionally, we constructed microbial networks to analyze microbial interactions.ResultsThe pollution load index (PLI) < 1 indicates that the area is not polluted. 1 < PLI < 2 represents moderate pollution. PLI was 1.05 and 0.14 for the heavy metal contaminated area and the uncontaminated area, respectively. Cd, Hg, Pb, Zn, and Cu were identified as the major contaminants in the contaminated area, with the contamination factors were 30.35, 11.26, 5.46, 5.19, and 2.46, respectively. The diversities and compositions of the bacterial community varied significantly between the two groups. Compared to the uncontaminated area, the co-occurrence network between bacterial and fungal species in the contaminated area was more complex. The keystone taxa of the co-occurrence network in the contaminated area were more than those in the uncontaminated area and were completely different from it.DiscussionHeavy metal concentrations played a crucial role in shaping the difference in microbial community compositions. Microorganisms adapt to long-term and moderate levels of heavy metal contamination through enhanced interactions. Bacteria resistant to heavy metal concentrations may play an important role in soils contaminated with moderate levels of heavy metals over long periods of time.

Characteristics of the phyllosphere microbial community and its relationship with major aroma precursors during the tobacco maturation process

Numerous bacteria, fungi and other microorganisms in the tobacco phyllosphere interstellar area participate in the physiological metabolism of plants by interacting with the host. However, there is currently little research on the characteristics of tobacco phyllosphere microbial communities, and the correlation between tobacco phyllosphere microbial communities and phyllosphere factor indicators is still unknown. Therefore, high-throughput sequencing technology based on the 16S rRNA/ITS1 gene was used to explore the diversity and composition characteristics of tobacco phyllosphere bacterial and fungal communities from different maturation processes, and to identify marker genera that distinguish phyllosphere microbial communities. In this study, the correlations between tobacco phyllosphere bacterial and fungal communities and the precursors of major aroma compounds were explored. The results showed that as the tobacco plants matured, the density of glandular trichomes on the tobacco leaves gradually decreased. The surface physicochemical properties of tobacco leaves also undergo significant changes. In addition, the overall bacterial alpha diversity in the tobacco phyllosphere area increased with maturation, while the overall fungal alpha diversity decreased. The beta diversity of bacteria and fungi in the tobacco phyllosphere area also showed significant differences. Specifically, with later top pruning time, the relative abundances of Acidisoma, Ralstonia, Bradyrhizobium, Alternaria and Talaromyces gradually increased, while the relative abundances of Pseudomonas, Filobassidium, and Tausonia gradually decreased. In the bacterial community, Acidisoma, Ralstonia, Bradyrhizobium, and Alternaria were significantly positively correlated with tobacco aroma precursors, with significant negative correlations with tobacco phyllosphere trichome morphology, while Pseudomonas showed the opposite pattern; In the fungal community, Filobasidium and Tausonia were significantly negatively correlated with tobacco aroma precursors, and significantly positively correlated with tobacco phyllosphere trichome morphology, while Alternaria showed the opposite pattern. In conclusion, the microbiota (bacteria and fungi) and aroma precursors of the tobacco phyllosphere change significantly as tobacco matures. The presence of Acidisoma, Ralstonia, Bradyrhizobium and Alternaria in the phyllosphere microbiota of tobacco may be related to the aroma precursors of tobacco.

FastCCLasso: a fast and efficient algorithm for estimating correlation matrix from compositional data.

The composition and structure of microbial communities on the body surface are closely related to human health. The interaction relationship among microbes can help us understand the formation of the microecological environment and the biological mechanism by which microorganisms influence host health. With the help of high-throughput sequencing technologies, microbial abundances in a natural environment can be directly measured without the isolation of microorganisms in culture. Sequencing experiments in microbiome studies can measure the relative abundance of microbes, which is called compositional data. Although there are already many methods for correlation analysis for compositional data, the computation time or accuracy still needs to be improved for current microbiome studies. We develop a fast and efficient algorithm, called fastCCLasso, based on a penalized weighted least squares for inferring the correlation structure of microbes from compositional data in microbiome studies. We perform a large number of numerical experiments and the simulation results show that fastCCLasso outperforms its competitors in edge detection for inferring the correlation network. We also apply fastCCLasso for estimating microbial networks in microbiome studies and fastCCLasso provides a conservative network with comparable false discovery counts that are derived from shuffled data. FastCCLasso is open source and freely available from https://github.com/ShenZhang-Statistics/fastCCLasso under GNU LGPL v3.

A Review on Bridging Molecular Biology and Ecological Dynamics through Integrative Approaches in Zoology

The integration of molecular biology with ecological dynamics has emerged as a transformative approach in zoology, enhancing our understanding of biodiversity, ecosystem health, and the adaptive responses of species to environmental changes. This review synthesizes key developments and methodological innovations at the intersection of molecular biology and ecological dynamics, highlighting the application of DNA barcoding, environmental DNA (eDNA) analyses, molecular phylogenetics, and advanced computational models in elucidating complex biological interactions and evolutionary patterns. Significant advancements include the use of high-throughput sequencing technologies and CRISPR-Cas systems that have expanded our ability to explore genetic diversity and manipulate genetic material for conservation purposes. The review discusses the predictive capabilities of integrative models that combine genetic with ecological data, offering insights into species resilience and ecosystem stability under varying environmental scenarios. Challenges in data integration, such as issues of scale, complexity, and the necessity for interdisciplinary cooperation, are critically examined. Technical limitations related to data management and ethical considerations in the use of genetic information are also explored. Looking forward, the review identifies emerging technologies and their potential impacts on ecological and conservation biology, emphasizing the need for policies that support sustainable management and conservation strategies. This review underscores the profound impact of integrating molecular biology with ecological dynamics, which not only enhances our scientific understanding but also provides practical frameworks for addressing global environmental challenges.

Evaluation of enrofloxacin in the Chinese soft-shelled turtle (Pelodiscus sinensis) based on the biochemical, histopathological and intestinal microbiota responses

Enrofloxacin (ENR) is a veterinary medicine extensively used in aquatic farming, the abuses of antibacterials can lead to drug residues and toxicity to aquatic animals. Nevertheless, its toxicological effects on the Pelodiscus sinensis remain unclear. The objective of this work was to study the toxic effects of ENR on male P. sinensis following multiple oral administration. For hematological, antioxidant status, histological and ultrastructural analyzed, male P. sinensis were orally administrated with ENR for 5 consecutive days, each at the dose of 15 (recommended dose, 1×), 45 (3 times the recommended dose, 3×), and 75 (5 times the recommended dose, 5×) mg/kg body weight (BW) daily. The effect of ENR on P. sinensis by changes in the intestinal microbiota using high-throughput sequencing technology was also investigated. Samples of blood and fresh tissue (liver, spleen, intestine, muscle, spermary, testes, heart, lung, and kidney) were collected after 24 h of drug withdrawal. The results showed that multiple oral administration of ENR induced immunosuppression (lowered total proteins, alkaline phosphatase, and albumin), hepatic-renal dysfunction (elevated aspartate transaminase, alanine aminotransferase, urea nitrogen, K+ and Ca2+), and lowering the hepatic and splenic antioxidant functions (higher MDA, and declines in the activities of SOD, CAT, and NOS) in 3× ENR-dosed or 5× ENR-dosed groups. Noteworthy, histopathological and ultrastructural examination of tissue samples revealed adverse effects with increasing oral dosage of ENR. The 16S sequencing analysis showed that the ENR treatment can greatly change the intestinal microbial diversity and abundance in P. sinensis, but more probiotics such as Cetobacterium, Lactococcus, and Paraclostridium were retained in the short-term period. The findings may provide new insights into the toxicity of ENR to P. sinensis, and help to determine the dose to ensure the safety of aquatic food products.

[Effects of Water Level Fluctuations and Vegetation Restoration on Soil Prokaryotic Microbial Community Structure in the Riparian Zone of the Three Gorges Reservoir].

Riparian zones are typical fragile and sensitive ecological areas. Fluctuations in water level are the main factor affecting the soil environment in these zones, and vegetation restoration is considered an important means of soil conservation there. However, the interactive effects of water level fluctuations and vegetation restoration on the soil microbial community structure in the reservoir riparian zone remain unclear. Therefore, we selected abandoned grassland and artificial forestland at different water level elevations as research objects in the riparian zone of the Three Gorges Reservoir. We used 16S rRNA high-throughput sequencing technology to explore the composition and diversity of soil prokaryotic microbial communities and investigated the main environmental factors driving the soil microbial community structure. The results showed that the α diversity of soil prokaryotes was the highest at the low water level of the riparian zone. The Pielou_e index, Shannon index, and Simpson index at the 163 m elevation were significantly higher than those at the 168 m elevation, and the Chao1 index and Shannon index were significantly higher than those at the 173 m elevation. However, no significant difference was found in the soil microbial community α diversity between abandoned grassland and artificial forestland. At the same time, water level fluctuations and vegetation restoration had significant effects on the community composition of soil prokaryotic microorganisms, and there were significant differences in biomarker categories in different study sites. Notably, the effects of vegetation restoration types on the soil prokaryotic microbial community structure were stronger than that of water level fluctuations. In addition, the results of hierarchical segmentation showed that soil pH was the main driving factor for the change in soil prokaryotic microbial community structure in the Three Gorges Reservoir. These results deepen our understanding of the variations in microbial community structure in the reservoir riparian zone and provide scientific reference for the restoration and reconstruction of the riparian zone ecosystem.

[Bacterial Community Structure of Typical Lake Sediments in Yinchuan City and Its Response to Heavy Metals].

Lake wetlands are extremely important and special ecosystems, which are important for regional water resource storage, environmental protection, and biodiversity maintenance. Sediment bacteria are an important component of lake ecosystems and are a major driver of biogeochemical cycling in lakes. In order to investigate the community structure of bacteria in typical lake sediments in Yinchuan City and their influencing factors, three typical lakes in Yinchuan City (Yuehai Lake, Mingcui Lake, and Xiniu Lake) were selected for the study and surface sediments were collected in January, April, July, and October 2021. The composition of the sediment bacterial community was examined using 16S rDNA high-throughput sequencing technology, and the response relationships between them and heavy metals were explored. The results showed that the ecological hazard coefficient for heavy metals in the sediments of three typical lakes in Yinchuan City was far less than 40, and the ecological hazard index was far less than 150, all of which indicated a minor ecological hazard. There were no significant differences in bacterial community diversity among the three lakes, but there were significant variations in diversity among the lakes in different seasons and significant differences in community composition. The dominant phyla (top three in terms of relative abundance) in Yuehai Lake, Mingcui Lake, and Xiniu Lake were Proteobacteria, Bacteroidetes, and Chloroflexi. The dominant lower orders were Gammaproteobacteria, Alphaproteobacteria, and Deltaproteobacteria. The main divergent species that occurred at the phylum level in typical lakes in Yinchuan were Proteobacteria, Bacteroidetes, Euryarchaeota, Firmicutes, Actinobacteria, and Acidobacteria. The sediment bacterial community structure of Yuehai Lake was significantly correlated with Cu, Fe, Mn, Zn, As, and Pb; the sediment bacterial community structure of Lake Mingcui was significantly correlated with Fe, Pb, and Cr; and the sediment bacterial community structure of Xiniu Lake was not significantly correlated with heavy metals. The types and contents of sediment heavy metals had a significant effect on the bacterial community structure of sediments in Yinchuan Yuehai Lake and Mingcui Lake and were important environmental factors that caused changes in the bacterial community structure of lake sediments.

[Characteristics of Epiphytic Bacterial Community on Submerged Macrophytes in Water Environment Supplemented with Reclaimed Water].

Biofilms attached to submerged macrophytes play an important role in improving the water quality of the water environment supplemented with reclaimed water. In order to explore the effects of reclaimed water quality and submerged macrophyte species on the characteristics of an epiphytic bacterial community, different types of submerged macrophytes were selected as research objects in this study. 16S rRNA high-throughput sequencing technology was used on the epiphytic bacteria and the surrounding environmental samples to analyze the bacterial community structure and functional genes. The results showed that approximately 20%-35% of the nitrogen and phosphorus nutrients were absorbed and utilized in the water environment supplemented with reclaimed water. However, the COD, turbidity, and chroma of the downstream water were significantly increased. The bacterial community of the biofilms attached to submerged macrophytes was significantly different from that in the surrounding environment (soil, sediment, and water body) and in the activated sludge that was treated by reclaimed water. In terms of bacterial community diversity, the richness and diversity were significantly lower than those of soil and sediment but higher than those of plankton bacteria in water. In terms of bacterial community composition, dominant genera and corresponding abundances were also different from those of other samples. The main dominant bacterial genera were Sphingomonas, Aeromonas, Pseudomonas, and Acinetobacter, accounting for 7%-40%, respectively. Both macrophyte species and the quality of reclaimed water (BOD5, TN, NH4+-N, and TP) could affect the bacterial community. However, the effect of water quality of the bacterial community was greater than that of macrophytes species. Additionally, the quality of reclaimed water also affected the abundance of functional genes in the bacterial community, and the relative abundance of nitrogen and phosphorus cycling functional genes was higher in areas with higher nitrogen and phosphorus concentrations.

Database construction and comparative genomics analysis of genes involved in nutritional metabolic diseases in fish

Nutritional metabolic diseases in fish frequently arise in the setting of intensive aquaculture. The etiology and pathogenesis of these conditions involve energy metabolic disorders influenced by both internal genetic factors and external environmental conditions. The exploration of genes associated with nutritional and metabolic disorder has sparked considerable interest within both the aquaculture scientific community and the industry. High-throughput sequencing technology offers researchers extensive genetic information. Effectively mining, analyzing, and securely storing this data is crucial, especially for advancing disease prevention and treatment strategies. Presently, the exploration and application of gene databases concerning nutritional and metabolic disorders in fish are at a nascent stag. Therefore, this study focused on the model organism zebrafish and five primary economic fish species as the subjects of investigation. Using information from KEGG, OMIM, and existing literature, a novel gene database associated with nutritional metabolic diseases in fish was meticulously constructed. This database encompassed 4583 genes for Danio rerio, 6287 for Cyprinus carpio, 3289 for Takifugu rubripes, 3548 for Larimichthys crocea, 3816 for Oreochromis niloticus, and 5708 for Oncorhynchus mykiss. Through a comparative systems biology approach, we discerned a relatively high conservation of genes linked to nutritional metabolic diseases across these fish species, with over 54.9 % of genes being conserved throughout all six species. Additionally, the analysis pinpointed the existence of 13 species-specific genes within the genomes of large yellow croaker, tilapia, and rainbow trout. These genes exhibit the potential to serve as novel candidate targets for addressing nutritional metabolic diseases.

Effect of Moringa oleifera leaf polysaccharide on the composition of intestinal microbiota in mice with dextran sulfate sodium-induced ulcerative colitis

Moringa oleifera (M. oleifera) is a natural plant that has excellent nutritional and medicinal potential. M. oleifera leaves (MOL) contain several bioactive compounds. The aim of this study was to evaluate the potential effect of MOL polysaccharide (MOLP) on intestinal flora in dextran sulfate sodium (DSS)-induced ulcerative colitis (UC) mice. DSS-induced colitis was deemed to be a well-characterized experimental colitis model for investigating the protective effect of drugs on UC. In this study, we stimulated the experimental mice with DSS 4% for 7 days and prepared the high dose of MOLP (MOLP-H) in order to evaluate its effect on intestinal flora in DSS-induced UC mice, comparing three experimental groups, including the control, DSS model, and DSS + MOLP-H (100 mg/kg/day). At the end of the experiment, feces were collected, and the changes in intestinal flora in DSS-induced mice were analyzed based on 16S rDNA high throughput sequencing technology. The results showed that the Shannon, Simpson, and observed species indices of abundance decreased in the DSS group compared with the control group. However, the indices mentioned above were increased in the MOLP-H group. According to beta diversity analysis, the DSS group showed low bacterial diversity and the distance between the control and MOLP-H groups, respectively. In addition, compared with the control group, the relative abundance of Firmicutes in the DSS group decreased and the abundance of Helicobacter increased, while MOLP-H treatment improves intestinal health by enhancing the number of beneficial organisms, including Firmicutes, while reducing the number of pathogenic organisms, such as Helicobacter. In conclusion, these findings suggest that MOLP-H may be a viable prebiotic with health-promoting properties.

Protein function prediction through multi-view multi-label latent tensor reconstruction

BackgroundIn last two decades, the use of high-throughput sequencing technologies has accelerated the pace of discovery of proteins. However, due to the time and resource limitations of rigorous experimental functional characterization, the functions of a vast majority of them remain unknown. As a result, computational methods offering accurate, fast and large-scale assignment of functions to new and previously unannotated proteins are sought after. Leveraging the underlying associations between the multiplicity of features that describe proteins could reveal functional insights into the diverse roles of proteins and improve performance on the automatic function prediction task.ResultsWe present GO-LTR, a multi-view multi-label prediction model that relies on a high-order tensor approximation of model weights combined with non-linear activation functions. The model is capable of learning high-order relationships between multiple input views representing the proteins and predicting high-dimensional multi-label output consisting of protein functional categories. We demonstrate the competitiveness of our method on various performance measures. Experiments show that GO-LTR learns polynomial combinations between different protein features, resulting in improved performance. Additional investigations establish GO-LTR’s practical potential in assigning functions to proteins under diverse challenging scenarios: very low sequence similarity to previously observed sequences, rarely observed and highly specific terms in the gene ontology.ImplementationThe code and data used for training GO-LTR is available at https://github.com/aalto-ics-kepaco/GO-LTR-prediction.

High-Throughput Sequencing Technology Assisted Investigation of the Correlation between Intestinal Flora, Serum Biochemistry, Blood Lipids, and Tumour Markers in Patients with Gastric Cancer and Healthy Plateau Residents.

Aim The goal is to use high-throughput sequencing technology to compare and study the structure and variety of intestinal flora in people with gastric cancer and healthy people in the Qinghai-Tibet Plateau. Background Recent research has connected gut flora structure to numerous disorders. Metabolites, endotoxins, and immunomodulatory modulation might cause gastrointestinal or other systemic diseases and affect gastric cancer treatment and prognosis. We used the correlation study to uncover biomarkers associated with good intestinal flora and gastric cancer groups on the plateau to investigate their involvement in gastric cancer development. Objectives To investigate the possible links between intestinal flora and gastric cancer in the Qinghai Plateau region using a variety of clinical phenotypic data and to investigate the flora that may be linked to gastric cancer. Methods The 22 Qinghai Province tertiary hospital gastric cancer patients and 30 healthy people had their fresh faeces collected. To examine intestinal flora diversity and composition, 52 patients underwent 16S rDNA high-throughput gene sequencing of intestinal bacteria. The correlation between clinical phenotypes and the top 15 dominant intestinal flora at the phylum level was analyzed. Results The difference in total protein TP between the healthy group and the gastric cancer group was statistically significant (P<0.001). Globulin was significantly different (P<0.05), TC of total cholesterol was significantly different (P<0.05). High-density lipoprotein showed statistical significance (P<0.05).The difference in low-density lipoprotein was statistically significant (P<0.001). Alpha-fetoprotein was significantly different (P<0.05). CA72-4 carbohydrate antigen (P<0.05). Conclusion There were significant differences in total protein, globulin, total cholesterol, high density lipoprotein, low-density lipoprotein, alpha-fetoprotein and carbohydrate antigen CA72-4 in patients with gastric cancer in the plateau area compared with the healthy group, and the different clinical variables were correlated with intestinal flora at some phylum and genus levels.

Q-Learning Guided Mutational Harris Hawk Optimizer for High-dimensional Gene Data Feature Selection

With the widespread application of high-throughput sequencing technology in recent years, the scale of high-dimensional gene sequence datasets has rapidly expanded. However, due to the high-dimensional nature of gene sequence data, researchers face the challenge of processing such complex data. One common preprocessing technique that can improve performance is feature selection, which selects the most relevant features from the original dataset, reducing its dimensionality. However, feature selection is often an NP-hard problem, and medical feature selection is mainly affected by the multiple attributes of features, which affect classification accuracy. In order to improve the generality of the algorithm, we propose RLHHO, which solves the feature selection problem of various medical gene datasets by combining Q-learning-guided mutation strategies. We also created binary RLHHO (bRLHHO) through conversion functions and evaluated its performance on 12 datasets in the UCI repository. The experimental results show that bRLHHO is superior to the original HHO in improving classification accuracy and reducing the number of selected features. When processing high-dimensional medical gene datasets with over 1000 dimensions, bRLHHO can achieve good accuracy with fewer features. In summary, compared with other algorithms, including the original Harris Hawks Optimization (HHO), our proposed improved version of HHO, RLHHO, can handle various feature selection datasets and exhibits superior performance.

Advancing metagenome-assembled genome-based pathogen identification: unraveling the power of long-read assembly algorithms in Oxford Nanopore sequencing.

Oxford Nanopore sequencing is one of the high-throughput sequencing technologies that facilitates the reconstruction of metagenome-assembled genomes (MAGs). This study aimed to assess the potential of long-read assembly algorithms in Oxford Nanopore sequencing to enhance the MAG-based identification of bacterial pathogens using both simulated and mock communities. Simulated communities were generated to mimic those on fresh spinach and in surface water. Long reads were produced using R9.4.1+SQK-LSK109 and R10.4 + SQK-LSK112, with 0.5, 1, and 2 million reads. The simulated bacterial communities included multidrug-resistant Salmonella enterica serotypes Heidelberg, Montevideo, and Typhimurium in the fresh spinach community individually or in combination, as well as multidrug-resistant Pseudomonas aeruginosa in the surface water community. Real data sets of the ZymoBIOMICS HMW DNA Standard were also studied. A bioinformatic pipeline (MAGenie, freely available at https://github.com/jackchen129/MAGenie) that combines metagenome assembly, taxonomic classification, and sequence extraction was developed to reconstruct draft MAGs from metagenome assemblies. Five assemblers were evaluated based on a series of genomic analyses. Overall, Flye outperformed the other assemblers, followed by Shasta, Raven, and Unicycler, while Canu performed least effectively. In some instances, the extracted sequences resulted in draft MAGs and provided the locations and structures of antimicrobial resistance genes and mobile genetic elements. Our study showcases the viability of utilizing the extracted sequences for precise phylogenetic inference, as demonstrated by the consistent alignment of phylogenetic topology between the reference genome and the extracted sequences. R9.4.1+SQK-LSK109 was more effective in most cases than R10.4+SQK-LSK112, and greater sequencing depths generally led to more accurate results.IMPORTANCEBy examining diverse bacterial communities, particularly those housing multiple Salmonella enterica serotypes, this study holds significance in uncovering the potential of long-read assembly algorithms to improve metagenome-assembled genome (MAG)-based pathogen identification through Oxford Nanopore sequencing. Our research demonstrates that long-read assembly stands out as a promising avenue for boosting precision in MAG-based pathogen identification, thus advancing the development of more robust surveillance measures. The findings also support ongoing endeavors to fine-tune a bioinformatic pipeline for accurate pathogen identification within complex metagenomic samples.

Effects of yak rumen anaerobic fungus Orpinomyces sp. YF3 fermented on in vitro wheat straw fermentation and microbial communities in dairy goat rumen fluid, with and without fungal flora.

Rumen fungi play an essential role in the breakdown of dietary fibrous components, facilitating the provision of nutrients and energy to the host animals. This study investigated the fermentation characteristics and effects on rumen microbiota of yak rumen anaerobic fungus Orpinomyces sp. YF3 in goat rumen fluid, both with and without fungal flora, utilizing anaerobic fermentation bottles. Crushed and air-dried wheat straw served as the fermentation substrate, and cycloheximide was used to eradicate microorganisms from the rumen fluid of dairy goats. The experiment compromised four treatment groups (2×2 factorial design): control (C); yak fungus group (CF, Orpinomyces sp. YF3); goat fungi eliminated group (CA, antibiotic: 0.25 mg/mL cycloheximide); goat fungi eliminated+yak fungus group (CAF). Each treatment had six replicates. Fermentation characteristics and microbial composition of the fermentation media were analyzed using one-way analysis of variance and high-throughput sequencing technology. The findings revealed that in the Orpinomyces sp. YF3 addition group (CF and CAF groups), there were significant increases in ammonia nitrogen concentration by 70%, total volatile fatty acids (VFA) by 53%, as well as acetate, isobutyrate, and valerate concentrations, and the ratio of acetate to propionate (p < 0.05), while the propionate proportion declined by 13%, alongside a reduction of butyrate concentration (p < 0.05). Similarly, in the CF and CAF groups, there were a notable increase in the relative abundance of Bacteroidota, Synergistota, Desulfobacterota, Actinobacteria, and Fusobacteriota, alongside a decrease in the relative abundance of Fibrobacterota and Proteobacteria (p < 0.05). Bacteria exhibiting increased relative abundance were positively correlated with the activity of carboxymethyl cellulase and avicelase, total VFA concentration, and acetate proportion, while showing a negatively correlation with propionate proportion. In conclusion, supplementing rumen fermentation media with yak rumen anaerobic fungus Orpinomyces sp. YF3 led to an increase in bacteria associated with fibre degradation and acetic acid production, a decrease in propionate-producing bacteria, enhanced the activity of plant cell wall degrading enzymes, and promoted cellulose degradation, ultimately elevating total VAF concentration and acetate proportion. This presents a novel approach to enhance roughage utilization in ruminants.

Cyanobacterial bloom affects structural and functional succession of microbial communities in urban wetland sediments in the Qinghai Plateau

Abstract Algal blooms significantly affect microbial community in wetland ecosystems. However, little is known about the succession of sediment microbial community during algal blooms. This study aimed to investigate the temporal patterns of sediment bacterial community structure and function succession during algal blooms (March to May, 2022) with high-throughput sequencing technology. To this end, algal blooms were divided into the bloom stage (BS), decomposition stage (DS) and end stage (ES). The results showed that: (1) The algal blooms were dominated by Microcystis species within Cyanobacteria. Both phytoplankton abundance and biomass reached the peak in the BS, with 45.78×105 cells/L and 5.97 mg/L, respectively. (2) The alpha diversity indices of sediment bacterial community showed a monotonically increasing trend, with the lowest value in the BS. Cyanobacterial blooms reduced the relative abundance of Actinobacteria and Chloroflexi, and raised that of Firmicutes, Proteobacteria and Bacteroidetes. (3) Cyanobacterial blooms remarkably decreased the complexity of the co-occurrence network of the sediment bacterial community, which resisted extreme environmental conditions in the BS and DS by increasing positive relationships. (4) Mantel test showed that phytoplankton biomass, T, DO, and OC had significant effects on the sediment bacterial community. The results of this study are important for the ecological management of algal blooms through microorganisms.

Nanopore and Illumina sequencing reveal different viral populations from human gut samples.

The advent of viral metagenomics, or viromics, has improved our knowledge and understanding of global viral diversity. High-throughput sequencing technologies enable explorations of the ecological roles, contributions to host metabolism, and the influence of viruses in various environments, including the human intestinal microbiome. However, bacterial metagenomic studies frequently have the advantage. The adoption of advanced technologies like long-read sequencing has the potential to be transformative in refining viromics and metagenomics. Here, we examined the effectiveness of long-read and hybrid sequencing by comparing Illumina short-read and Oxford Nanopore Technology (ONT) long-read sequencing technologies and different assembly strategies on recovering viral genomes from human faecal samples. Our findings showed that if a single sequencing technology is to be chosen for virome analysis, Illumina is preferable due to its superior ability to recover fully resolved viral genomes and minimise erroneous genomes. While ONT assemblies were effective in recovering viral diversity, the challenges related to input requirements and the necessity for amplification made it less ideal as a standalone solution. However, using a combined, hybrid approach enabled a more authentic representation of viral diversity to be obtained within samples.

Elevated Altitude and Limestone Soil Promoted Fungal Diversity in Rhizosphere Soil of Sophora japonica

Soil microorganisms play a crucial role in mediating material transformation and nutrient cycling. However, little attention has been paid to the relationships between microbial communities and altitude and lithology in karst regions. Sophora japonica cv. Jinhuai is an officinal plant cultivated in karst areas, and there is a correlation between the dynamics of microbial community and ecological factors. This study examined the diversity of microbial communities in the rhizosphere of S. japonica under different lithologies and altitudes in karst regions of northern Guangxi, China using high-throughput sequencing technology. It was found that the bacterial community consisted of 37 phyla, including Proteobacteria. The fungal community mainly comprised 15 phyla, including Ascomycota. The fungal Shannon and Chao1 indices increased with altitude, while the bacterial Shannon index decreased. The fungal Shannon indices in limestone soil were higher than those in dolomite. The soil’s microbial Shannon and Chao1 indices were positively affected by pH, while the available phosphorus and potassium had the opposite effect. Research shows that altitude, lithology, pH, and available phosphorus were the crucial factors influencing the rhizosphere soil microbial community. This study provides references for understanding the relationship between plants and microorganisms and the microbial distribution strategy of rocky desertification habitats in the future.