https://tsukuba.repo.nii.ac.jp/record/2001613/files/FEMSME_96-5SI.pdf

Biofilms: hot spots of horizontal gene transfer (HGT) in aquatic environments, with a focus on a new HGT mechanism

Probing the Mobilome: Discoveries in the Dynamic Microbiome.

Antibiotic resistance genes of emerging concern in municipal and hospital wastewater from a major Swedish city.

Human milk is considered the most valuable form of nutrition for infants for their growth, development and function. So far, there are still some cases where feeding human milk is not feasible. As a result, the market for infant formula is widely increasing, and formula feeding become an alternative or substitute for breastfeeding. The nutritional value of the formula can be improved by adding functional bioactive compounds like probiotics, prebiotics, human milk oligosaccharides, vitamins, minerals, taurine, inositol, osteopontin, lactoferrin, gangliosides, carnitine etc. For processing of infant formula, diverse thermal and non-thermal technologies have been employed. Infant formula can be either in powdered form, which requires reconstitution with water or in ready-to-feed liquid form, among which powder form is readily available, shelf-stable and vastly marketed. Infants’ gut microbiota is a complex ecosystem and the nutrient composition of infant formula is recognized to have a lasting effect on it. Likewise, the gut microbiota establishment closely parallels with host immune development and growth. Therefore, it must be contemplated as an important factor for consideration while developing formulas. In this review, we have focused on the formulation and manufacturing of safe and nutritious infant formula equivalent to human milk or aligning with the infant’s needs and its ultimate impact on infants’ gut microbiota.

/pdf/a-comprehensive-review-on-infant-formula-nutritional-and-3dz2frzc.pdf

A comprehensive review on infant formula: nutritional and functional constituents, recent trends in processing and its impact on infants’ gut microbiota

Genome-centric metagenomics insights into functional divergence and horizontal gene transfer of denitrifying bacteria in anammox consortia.

Horizontal Gene Transfer (HGT) is the process of transferring genetic materials between species. Through sharing genetic materials, microorganisms in the human microbiota form a network. The network can provide insights into understanding the microbiota. Here, we constructed the HGT networks from the gut microbiota sequencing data and performed network analysis to characterize the HGT networks of gut microbiota. We constructed the HGT network and perform the network analysis to two typical gut microbiota datasets, a 283-sample dataset of Mother-to-Child and a 148-sample dataset of longitudinal inflammatory bowel disease (IBD) metagenome. The results indicated that (1) the HGT networks are scale-free. (2) The networks expand their complexities, sizes, and edge numbers, accompanying the early stage of lives; and microbiota established in children shared high similarity as their mother (p-value = 0.0138), supporting the transmission of microbiota from mother to child. (3) Groups harbor group-specific network edges, and network communities, which can potentially serve as biomarkers. For instances, IBD patient group harbors highly abundant communities of Proteobacteria (p-value = 0.0194) and Actinobacteria (p-value = 0.0316); children host highly abundant communities of Proteobacteria (p-value = 2.8785
$$e^{-5}$$

) and Actinobacteria (p-value = 0.0015), and the mothers host highly abundant communities of Firmicutes (p-value = 8.0091
$$e^{-7}$$

). IBD patient networks contain more HGT edges in pathogenic genus, including Mycobacterium, Sutterella, and Pseudomonas. Children’s networks contain more edges from Bifidobacterium and Escherichia. Hence, we proposed the HGT network constructions from the gut microbiota sequencing data. The HGT networks capture the host state and the response of microbiota to the environmental and host changes, and they are essential to understand the human microbiota.

/pdf/understanding-horizontal-gene-transfer-network-in-human-gut-1zxxbm5qwj.pdf

Understanding Horizontal Gene Transfer network in human gut microbiota

Introduction Ankylosing spondylitis (AS), a chronic autoimmune disease, has been linked to the gut bacteriome. Methods To investigate the characteristics of the gut virome in AS, we profiled the gut viral community of 193 AS patients and 59 healthy subjects based on a metagenome-wide analysis of fecal metagenomes from two publicly available datasets. Results AS patients revealed a significant decrease in gut viral richness and a considerable alteration of the overall viral structure. At the family level, AS patients had an increased abundance of Gratiaviridae and Quimbyviridae and a decreased abundance of Drexlerviridae and Schitoviridae. We identified 1,004 differentially abundant viral operational taxonomic units (vOTUs) between patients and controls, including a higher proportion of AS-enriched Myoviridae viruses and control-enriched Siphoviridae viruses. Moreover, the AS-enriched vOTUs were more likely to infect bacteria such as Flavonifractor, Achromobacter, and Eggerthellaceae, whereas the control-enriched vOTUs were more likely to be Blautia, Ruminococcus, Collinsella, Prevotella, and Faecalibacterium bacteriophages. Additionally, some viral functional orthologs differed significantly in frequency between the AS-enriched and control-enriched vOTUs, suggesting the functional role of these AS-associated viruses. Moreover, we trained classification models based on gut viral signatures to discriminate AS patients from healthy controls, with an optimal area under the receiver operator characteristic curve (AUC) up to 0.936, suggesting the clinical potential of the gut virome for diagnosing AS. Discussion This work provides novel insight into the AS gut virome, and the findings may guide future mechanistic and therapeutic studies for other autoimmune diseases.

/pdf/alterations-in-the-gut-virome-in-patients-with-ankylosing-jhqgurrb.pdf

Alterations in the gut virome in patients with ankylosing spondylitis

Horizontal Gene Transfer (HGT) refers to the transfer of genetic materials between organisms through mechanisms other than parent-offspring inheritance. HGTs may affect human health through a large number of microorganisms, especially the gut microbiomes which the human body harbors. The transferred segments may lead to complicated local genome structural variations. Details of the local genome structure can elucidate the effects of the HGTs. In this work, we propose a graph-based method to reconstruct the local strains from the gut metagenomics data at the HGT sites. The method is implemented in a package named LEMON. The simulated results indicate that the method can identify transferred segments accurately on reference sequences of the microbiome. Simulation results illustrate that LEMON could recover local strains with complicated structure variation. Furthermore, the gene fusion points detected in real data near HGT breakpoints validate the accuracy of LEMON. Some strains reconstructed by LEMON have a replication time profile with lower standard error, which demonstrates HGT events recovered by LEMON is reliable. Through LEMON we could reconstruct the sequence structure of bacteria, which harbors HGT events. This helps us to study gene flow among different microbial species.

/pdf/lemon-a-method-to-construct-the-local-strains-at-horizontal-1wjg3amzer.pdf

LEMON: a method to construct the local strains at horizontal gene transfer sites in gut metagenomics

Horizontal Gene Transfer (HGT) refers to the sharing of genetic materials between distant species that are not in a parent-offspring relationship. The HGT insertion sites are important to understand the HGT mechanisms. Recent studies in main agents of HGT, such as transposon and plasmid, demonstrate that insertion sites usually hold specific sequence features. This motivates us to find a method to infer HGT insertion sites according to sequence features. In this paper, we propose a deep residual network, DeepHGT, to recognize HGT insertion sites. To train DeepHGT, we extracted about 1.55 million sequence segments as training instances from 262 metagenomic samples, where the ratio between positive instances and negative instances is about 1:1. These segments are randomly partitioned into three subsets: 80% of them as the training set, 10% as the validation set, and the remaining 10% as the test set. The training loss of DeepHGT is 0.4163 and the validation loss is 0.423. On the test set, DeepHGT has achieved the area under curve (AUC) value of 0.8782. Furthermore, in order to further evaluate the generalization of DeepHGT, we constructed an independent test set containing 689,312 sequence segments from another 147 gut metagenomic samples. DeepHGT has achieved the AUC value of 0.8428, which approaches the previous test AUC value. As a comparison, the gradient boosting classifier model implemented in PyFeat achieve an AUC value of 0.694 and 0.686 on the above two test sets, respectively. Furthermore, DeepHGT could learn discriminant sequence features; for example, DeepHGT has learned a sequence pattern of palindromic subsequences as a significantly (P-value=0.0182) local feature. Hence, DeepHGT is a reliable model to recognize the HGT insertion site. DeepHGT is the first deep learning model that can accurately recognize HGT insertion sites on genomes according to the sequence pattern.

/pdf/deep-learning-for-hgt-insertion-sites-recognition-3bfiv5bd39.pdf

Deep learning for HGT insertion sites recognition

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Papers

Understanding Horizontal Gene Transfer network in human gut microbiota

Alterations in the gut virome in patients with ankylosing spondylitis

LEMON: a method to construct the local strains at horizontal gene transfer sites in gut metagenomics

Deep learning for HGT insertion sites recognition