Network construction and structure detection with metagenomic count data

The short-term impact of comprehensive caries treatment on the supragingival microbiome of severe early childhood caries

BACKGROUND

Children affected by severe early childhood caries (S-ECC) usually need comprehensive caries treatment due to the extensive of caries. How the oral microbiome changes after caries therapy within the short-term warrant further study.

AIM

This study aimed to investigate the short-term impact of comprehensive caries treatment on the supragingival plaque microbiome of S-ECC children.

DESIGN

Thirty-three children aged 2-4 years with severe caries (dt > 7) were recruited. Comprehensive caries treatment was performed under general anesthesia in one session and included restoration, pulp treatment, extraction, and fluoride application. Supragingival plaque was sampled pre- and 1-month posttreatment. The genomic DNA of the supragingival plaque was extracted, and bacterial 16S ribosomal RNA gene sequencing was performed.

RESULTS

Our data showed that the microbial community evenness significantly decreased posttreatment. Furthermore, comprehensive caries treatment led to more diverse microbial structures among the subjects. The interbacterial interactions reflected by the microbial community's co-occurrence network tended to be less complex posttreatment. Caries treatment increased the relative abundance of Corynebacterium matruchotii, Corynebacterium durum, Actinomyces naeslundii, and Saccharibacteria HMT-347, as well as Aggregatibacter HMT-458 and Haemophilus influenzae. Meanwhile, the relative abundance of Streptococcus mutans, three species from Leptotrichia, Neisseria bacilliformis, and Provotella pallens significantly decreased posttreatment.

CONCLUSION

Our results suggested that comprehensive caries treatment may contribute to the reconstruction of a healthier supragingival microbiome.

Keystone microbial taxa organize micropollutant-related modules shaping the microbial community structure in estuarine sediments

The fluctuation of environmental conditions drives the structure of microbial communities in estuaries, highly dynamic ecosystems. Microorganisms inhabiting estuarine sediments play a key role in ecosystem functioning. They are well adapted to the changing conditions, also threatened by the presence of pollutants. In order to determine the environmental characteristics driving the organization of the microbial assemblages, we conducted a seasonal survey along the Adour Estuary (Bay of Biscay, France) using 16S rRNA gene Illumina sequencing. Microbial diversity data were combined with a set of chemical analyses targeting metals and pharmaceuticals. Microbial communities were largely dominated by Proteobacteria (41 %) and Bacteroidota (32 %), showing a strong organization according to season, with an important shift in winter. The composition of microbial communities showed spatial distribution according to three main areas (upstream, middle, and downstream estuary) revealing the influence of the Adour River. Further analyses indicated that the microbial community was influenced by biogeochemical parameters (C_org/N_org and δ¹³C) and micropollutants, including metals (As, Cu, Mn, Sn, Ti, and Zn) and pharmaceuticals (norfloxacin, oxolinic acid and trimethoprim). Network analysis revealed specific modules, organized around keystone taxa, linked to a pollutant type, providing information of paramount importance to understand the microbial ecology in estuarine ecosystems.

Clustering Single-Cell RNA-Seq Data with Regularized Gaussian Graphical Model

Single-cell RNA-seq (scRNA-seq) is a powerful tool to measure the expression patterns of individual cells and discover heterogeneity and functional diversity among cell populations. Due to variability, it is challenging to analyze such data efficiently. Many clustering methods have been developed using at least one free parameter. Different choices for free parameters may lead to substantially different visualizations and clusters. Tuning free parameters is also time consuming. Thus there is need for a simple, robust, and efficient clustering method. In this paper, we propose a new regularized Gaussian graphical clustering (RGGC) method for scRNA-seq data. RGGC is based on high-order (partial) correlations and subspace learning, and is robust over a wide-range of a regularized parameter λ. Therefore, we can simply set λ=2 or λ=log(p) for AIC (Akaike information criterion) or BIC (Bayesian information criterion) without cross-validation. Cell subpopulations are discovered by the Louvain community detection algorithm that determines the number of clusters automatically. There is no free parameter to be tuned with RGGC. When evaluated with simulated and benchmark scRNA-seq data sets against widely used methods, RGGC is computationally efficient and one of the top performers. It can detect inter-sample cell heterogeneity, when applied to glioblastoma scRNA-seq data.

Mobilization of Antibiotic Resistance: Are Current Approaches for Colocalizing Resistomes and Mobilomes Useful?

Antimicrobial resistance (AMR) poses a global human and animal health threat, and predicting AMR persistence and transmission remains an intractable challenge. Shotgun metagenomic sequencing can help overcome this by enabling characterization of AMR genes within all bacterial taxa, most of which are uncultivatable in laboratory settings. Shotgun sequencing, therefore, provides a more comprehensive glance at AMR "potential" within samples, i.e., the "resistome." However, the risk inherent within a given resistome is predicated on the genomic context of various AMR genes, including their presence within mobile genetic elements (MGEs). Therefore, resistome risk stratification can be advanced if AMR profiles are considered in light of the flanking mobilizable genomic milieu (e.g., plasmids, integrative conjugative elements (ICEs), phages, and other MGEs). Because such mediators of horizontal gene transfer (HGT) are involved in uptake by pathogens, investigators are increasingly interested in characterizing that resistome fraction in genomic proximity to HGT mediators, i.e., the "mobilome"; we term this "colocalization." We explored the utility of common colocalization approaches using alignment- and assembly-based techniques, on clinical (human) and agricultural (cattle) fecal metagenomes, obtained from antimicrobial use trials. Ordination revealed that tulathromycin-treated cattle experienced a shift in ICE and plasmid composition versus untreated animals, though the resistome was unaffected during the monitoring period. Contrarily, the human resistome and mobilome composition both shifted shortly after antimicrobial administration, though this rebounded to pre-treatment status. Bayesian networks revealed statistical AMR-MGE co-occurrence in 19 and 2% of edges from the cattle and human networks, respectively, suggesting a putatively greater mobility potential of AMR in cattle feces. Conversely, using Mobility Index (MI) and overlap analysis, abundance of de novo-assembled contigs supporting resistomes flanked by MGE increased shortly post-exposure within human metagenomes, though > 40 days after peak dose such contigs were rare (∼2%). MI was not substantially altered by antimicrobial exposure across all cattle metagenomes, ranging 0.5-4.0%. We highlight that current alignment- and assembly-based methods estimating resistome mobility yield contradictory and incomplete results, likely constrained by approach-specific data inputs, and bioinformatic limitations. We discuss recent laboratory and computational advancements that may enhance resistome risk analysis in clinical, regulatory, and commercial applications.

Construction, comparison and evolution of networks in life sciences and other disciplines

Network approaches have become pervasive in many research fields. They allow for a more comprehensive understanding of complex relationships between entities as well as their group-level properties and dynamics. Many networks change over time, be it within seconds or millions of years, depending on the nature of the network. Our focus will be on comparative network analyses in life sciences, where deciphering temporal network changes is a core interest of molecular, ecological, neuropsychological and evolutionary biologists. Further, we will take a journey through different disciplines, such as social sciences, finance and computational gastronomy, to present commonalities and differences in how networks change and can be analysed. Finally, we envision how borrowing ideas from these disciplines could enrich the future of life science research.

Efficient Regularized Regression with L0 Penalty for Variable Selection and Network Construction

Variable selections for regression with high-dimensional big data have found many applications in bioinformatics and computational biology. One appealing approach is the L₀ regularized regression which penalizes the number of nonzero features in the model directly. However, it is well known that L₀ optimization is NP-hard and computationally challenging. In this paper, we propose efficient EM (L₀EM) and dual L₀EM (DL₀EM) algorithms that directly approximate the L₀ optimization problem. While L₀EM is efficient with large sample size, DL₀EM is efficient with high-dimensional (n ≪ m) data. They also provide a natural solution to all L_p   p ∈ [0,2] problems, including lasso with p = 1 and elastic net with p ∈ [1,2]. The regularized parameter λ can be determined through cross validation or AIC and BIC. We demonstrate our methods through simulation and high-dimensional genomic data. The results indicate that L₀ has better performance than lasso, SCAD, and MC+, and L₀ with AIC or BIC has similar performance as computationally intensive cross validation. The proposed algorithms are efficient in identifying the nonzero variables with less bias and constructing biologically important networks with high-dimensional big data.