DyMMM-LEAPS: An ML-based framework for modulating evenness and stability in synthetic microbial communities

There have been a growing number of computational strategies to aid in the design of synthetic microbial consortia. A framework to identify regions in parametric space to maximize two essential properties, evenness and stability, is critical. In this study, we introduce DyMMM-LEAPS (dynamic multispecies metabolic modeling-locating evenness and stability in large parametric space), an extension of the DyMMM framework. Our method explores the large parametric space of genetic circuits in synthetic microbial communities to identify regions of evenness and stability. Due to the high computational costs of exhaustive sampling, we utilize adaptive sampling and surrogate modeling to reduce the number of simulations required to map the vast space. Our framework predicts engineering targets and computes their operating ranges to maximize the probability of the engineered community to have high evenness and stability. We demonstrate our approach by simulating five cocultures and one three-strain culture with different social interactions (cooperation, competition, and predation) employing quorum-sensing-based genetic circuits. In addition to guiding circuit tuning, our pipeline gives an opportunity for a detailed analysis of pockets of evenness and stability for the circuit under investigation, which can further help dissect the relationship between the two properties. DyMMM-LEAPS is easily customizable and can be expanded to a larger community with more complex interactions.

Metagenomic-Metabolomic Mining of Kinema, a Naturally Fermented Soybean Food of the Eastern Himalayas

Kinema is a popular sticky fermented soybean food of the Eastern Himalayan regions of North East India, east Nepal, and south Bhutan. We hypothesized that some dominant bacteria in kinema may contribute to the formation of targeted and non-targeted metabolites for health benefits; hence, we studied the microbiome-metabolite mining of kinema. A total of 1,394,094,912 bp with an average of 464,698,304 ± 120,720,392 bp was generated from kinema metagenome, which resulted in the identification of 47 phyla, 331 families, 709 genera, and 1,560 species. Bacteria (97.78%) were the most abundant domain with the remaining domains of viruses, eukaryote, and archaea. Firmicutes (93.36%) was the most abundant phylum with 280 species of Bacillus, among which Bacillus subtilis was the most dominant species in kinema followed by B. glycinifermentans, B. cereus, B. licheniformis, B. thermoamylovorans, B. coagulans, B. circulans, B. paralicheniformis, and Brevibacillus borstelensis. Predictive metabolic pathways revealed the abundance of genes associated with metabolism (60.66%), resulting in 216 sub-pathways. A total of 361 metabolites were identified by metabolomic analysis (liquid chromatography-mass spectrophotometry, LC-MS). The presence of metabolites, such as chrysin, swainsonine, and 3-hydroxy-L-kynurenine (anticancer activity) and benzimidazole (antimicrobial, anticancer, and anti-HIV activities), and compounds with immunomodulatory effects in kinema supports its therapeutic potential. The correlation between the abundant species of Bacillus and primary and secondary metabolites was constructed with a bivariate result. This study proves that Bacillus spp. contribute to the formation of many targeted and untargeted metabolites in kinema for health-promoting benefits.

Utility of autologous fecal microbiota transplantation and elucidation of microbiota in diversion colitis

Objectives

Diversion colitis (DC) is an inflammatory disorder caused by interruption of the fecal stream and subsequent nutrient deficiency from luminal bacteria. The utility of fecal microbiota transplantation (FMT) for DC was recently investigated; however, the precise pathogenesis of this condition remains unclear. This study aimed to evaluate the utility of autologous FMT in DC and to determine the related changes in the intestinal microbiota.

Methods

Autologous FMT was performed to reestablish the intestinal microbiota in five patients (average age, 64.6 ± 8.3 years) with DC. They underwent double‐ended colostomy. We assessed the diverted colon by endoscopy and evaluated the microbiota before and after FMT using the 16S rRNA gene sequencing method.

Results

All five patients had mild inflammation (ulcerative colitis endoscopic index of severity [UCEIS] 2–3) in the diverted colon based on the colonoscopic findings. Three patients presented with symptoms, such as tenesmus, mucoid stool, and bloody stool. With FMT treatment, all patients achieved endoscopic remission (UCEIS score of 0 or 1) and symptomatic improvement. We observed a significantly decreased α‐diversity in DC patients compared to healthy controls. The frequency of aerobic bacteria, such as Enterobacteriaceae, in the diverted colon decreased after autologous FMT.

Conclusions

This study was the first to show that the microbiota in the diverted colon was significantly affected by autologous FMT. Since interruption of the fecal stream is central to the development of DC, FMT can be considered a promising treatment.

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.

Increase in muscle mass associated with the prebiotic effects of 1-kestose in super-elderly patients with sarcopenia

Sarcopenia causes functional disorders and decreases the quality of life. Thus, it has attracted substantial attention in the aging modern world. Dysbiosis of the intestinal microbiota is associated with sarcopenia; however, it remains unclear whether prebiotics change the microbiota composition and result in the subsequent recovery of muscle atrophy in elderly patients with sarcopenia. This study aimed to assess the effects of prebiotics in super-elderly patients with sarcopenia. We analyzed the effects of 1-kestose on the changes in the intestinal microbiota and body composition using a next-generation sequencer and a multi-frequency bioimpedance analysis device. The Bifidobacterium longum population was significantly increased in the intestine after 1-kestose administration. In addition, in all six patients after 12 weeks of 1-kestose administration, the skeletal muscle mass index was greater, and the body fat percentage was lower. This is the first study to show that administration of a prebiotic increased the population of B. longum in the intestinal microbiota and caused recovery of muscle atrophy in super-elderly patients with sarcopenia.

Bacterial and fungal communities and their predictive functional profiles in kinema, a naturally fermented soybean food of India, Nepal and Bhutan

Bacterial and fungal communities in kinema, a naturally fermented soybean food of the Eastern Himalayan regions of India, Nepal and Bhutan were profiled by high-throughout sequence analysis. Firmicutes (78.4%) was the most abundant phylum in kinema, followed by Proteobacteria (14.76%) and other phyla. Twenty seven species of Bacillus were detected, among which Bacillus subtilis (28.70%) was the most abundant bacterium, followed by B. licheniformis, B. thermoamylovorans, B. cereus, Ignatzschineria larvae, Corynebacterium casei, B. sonorensis, Proteus vulgaris, Brevibacillus borstelensis, Thermoactinomyces vulgaris, Lactobacillus fermentum and Ignatzschineria indica. Ascomycota was the most abundant fungal phylum in kinema. Wallemia canadensis, Penicillium spp., Aspergillus spp., Exobasidium spp., Arthrocladium spp., Aspergillus penicillioides, Mortierella spp., Rhizopus arrhizus and Mucor circinelloides, were major moulds, and Pichia sporocuriosa, Trichosporon spp., Saccharomycopsis malanga and Rhodotorula cycloclastica were abundant yeasts in kinema. We detected 277 species of bacteria among which, 99.09% were culturable and 0.91% were unculturable; and 80 fungal species among which, 33.72% were culturable and 66.28% were unculturable. Several unique bacterial genera to each country were observed, whereas no unique fungal genus was observed in kinema. Maximum coverage of sequencing depth was observed in all samples. Based on PCA plot, close relation was observed between samples of India and Nepal, whereas samples of Bhutan was clearly distinctive. Predictive functional features of bacterial and fungi related to metabolisms were inferred by the KEGG Orthology and MetaCyc databases, respectively.

Evaluation of intestinal microbiota, short-chain fatty acids, and immunoglobulin a in diversion colitis

It is reported that an increase in aerobic bacteria, a lack of short-chain fatty acids (SCFAs), and immune disorders in the diverted colon are major causes of diversion colitis. However, the precise pathogenesis of this condition remains unclear. The aim of the present study was to examine the microbiota, intestinal SCFAs, and immunoglobulin A (IgA) in the diverted colon. Eight patients underwent operative procedures for colostomies. We assessed the diverted colon using endoscopy and obtained intestinal samples from the diverted colon and oral colon in these patients. We analyzed the microbiota and SCFAs of the intestinal samples. The bacterial communities were investigated using a 16S rRNA gene sequencing method. The microbiota demonstrated a change in the proportion of some species, especially Lactobacillus, which significantly decreased in the diverted colon at the genus level. We also showed that intestinal SCFA values were significantly decreased in the diverted colon. Furthermore, intestinal IgA levels were significantly increased in the diverted colon. This study was the first to show that intestinal SCFAs were significantly decreased and intestinal IgA was significantly increased in the diverted colon. Our data suggest that SCFAs affect the microbiota and may play an immunological role in diversion colitis.

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Anaerobic bacteria like Lactobacillus significantly decreased in the diverted colon.

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Fecal short-chain fatty acids significantly decreased in the diverted colon.

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Acetic acid also decreased significantly in the diverted colon.

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Fecal immunoglobulin A levels were significantly increased in the diverted colon.

Deconvolution of heterogeneous tumor samples using partial reference signals

Deconvolution of heterogeneous bulk tumor samples into distinct cellular populations is an important yet challenging problem, particularly when only partial references are available. A common approach to dealing with this problem is to deconvolve the mixed signals using available references and leverage the remaining signal as a new cell component. However, as indicated in our simulation, such an approach tends to over-estimate the proportions of known cell types and fails to detect novel cell types. Here, we propose PREDE, a partial reference-based deconvolution method using an iterative non-negative matrix factorization algorithm. Our method is verified to be effective in estimating cell proportions and expression profiles of unknown cell types based on simulated datasets at a variety of parameter settings. Applying our method to TCGA tumor samples, we found that proportions of pure cancer cells better indicate different subtypes of tumor samples. We also detected several cell types for each cancer type whose proportions successfully predicted patient survival. Our method makes a significant contribution to deconvolution of heterogeneous tumor samples and could be widely applied to varieties of high throughput bulk data. PREDE is implemented in R and is freely available from GitHub (https://xiaoqizheng.github.io/PREDE).

Tumor tissues are mixtures of different cell types. Identification and quantification of constitutional cell types within tumor tissues are important tasks in cancer research. The problem can be readily solved using regression-based methods if reference signals are available. But in most clinical applications, only partial references are available, which significantly reduces the deconvolution accuracy of the existing regression-based methods. In this paper, we propose a partial-reference based deconvolution model, PREDE, integrating the non-negative matrix factorization framework with an iterative optimization strategy. We conducted comprehensive evaluations for PREDE using both simulation and real data analyses, demonstrating better performance of our method than other existing methods.

Transfer index, NetUniFrac and some useful shortest path-based distances for community analysis in sequence similarity networks

MOTIVATION

Phylogenetic trees and the methods for their analysis have played a key role in many evolutionary, ecological and bioinformatics studies. Alternatively, phylogenetic networks have been widely used to analyze and represent complex reticulate evolutionary processes which cannot be adequately studied using traditional phylogenetic methods. These processes include, among others, hybridization, horizontal gene transfer, and genetic recombination. Nowadays, sequence similarity and genome similarity networks have become an efficient tool for community analysis of large molecular datasets in comparative studies. These networks can be used for tackling a variety of complex evolutionary problems such as the identification of horizontal gene transfer events, the recovery of mosaic genes and genomes, and the study of holobionts.

RESULTS

The shortest path in a phylogenetic tree is used to estimate evolutionary distances between species. We show how the shortest path concept can be extended to sequence similarity networks by defining five new distances, NetUniFrac, Spp, Spep, Spelp and Spinp, and the Transfer index, between species communities present in the network. These new distances can be seen as network analogs of the traditional UniFrac distance used to assess dissimilarity between species communities in a phylogenetic tree, whereas the Transfer index is intended for estimating the rate and direction of gene transfers, or species dispersal, between different phylogenetic, or ecological, species communities. Moreover, NetUniFrac and the Transfer index can be computed in linear time with respect to the number of edges in the network. We show how these new measures can be used to analyze microbiota and antibiotic resistance gene similarity networks.

AVAILABILITY AND IMPLEMENTATION

Our NetFrac program, implemented in R and C, along with its source code, is freely available on Github at the following URL address: https://github.com/XPHenry/Netfrac.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Differences in foot skin microbiomes between patients with type 2 diabetes and healthy individuals

Impaired immunity and changes in the microenvironment in patients with diabetes might influence the composition of the cutaneous microbiome. However, data on the cutaneous microbiome of these patients are scarce. This study compared the fungal and bacterial components of the skin microbiome between patients with type 2 diabetes mellitus (DM) and healthy individuals. We obtained skin swab samples from the plantar forefoot of 17 patients with DM and 18 healthy individuals to conduct a cross-sectional study. The samples were profiled with culture-independent sequencing of the V3 to V4 regions of the bacterial 16S rRNA gene and the fungal ITS2 region, followed by direct DNA extraction and molecular polymerase chain reaction (PCR). We observed a differential cutaneous microbiome, especially for fungi, in patients with type 2 diabetes compared to that in healthy controls. Trichophyton rubrum was more abundant in DM samples. The Shannon diversity index for fungi was lower in the DM patients. Principal coordinate analysis plots and permutational multivariate analysis of variance (PERMANOVA) tests based on Bray-Curtis distances between samples supported the association of the fungal microbiome with DM at the species level. The results suggest that clinicians should pay attention to both fungi and bacteria and provide appropriate prevention and therapeutic strategies for diabetic cutaneous complications including diabetic foot ulcers. These data also contribute to future research associated with diabetes and cutaneous microbiomes.

Oral microbiome alterations of healthy volunteers with proton pump inhibitor

BACKGROUND AND AIM

Acid suppressive agents including proton pump inhibitors (PPIs) are used as first-line treatment for various acid-related gastrointestinal disorders. Although known to profoundly reduce gastric acid production, their influence on inhibition of acid secretion as part of the function of the gastrointestinal tract microbiome remains to be elucidated. The aim of the present study was to examine the effects of PPI usage on oral and gut microbiota in healthy volunteers.

METHODS

Ten healthy adult volunteers receiving no medications were enrolled. We obtained fecal, saliva, and periodontal pocket fluid samples from the subjects before and after 4 weeks of once daily administrations of 20-mg esomeprazole. The effects of PPI administration on bacterial communities were investigated using a 16S rRNA gene sequencing method.

RESULTS

Species richness (alpha diversity) was significantly different among the salivary, periodontal pocket, and fecal samples. Furthermore, the measurements for UniFrac distances, despite inter-individual variations (beta diversity), of the microbiota structure of saliva and periodontal pocket and feces samples were clearly separated from each other. The salivary samples showed significant differences between alpha and beta diversity measurements before and after administration of the PPI for 4 weeks. Meanwhile, taxon-based analysis indicated that PPI administration raised the ratio of Streptococcus organisms in fecal samples, suggesting a potentially unfavorable effect leading to gut microbiota alteration. Moreover, alterations of the microbiota in the oral carriage microbiome along with bacterial overgrowth (Streptococcus) and decreases in distinct bacterial species (Neisseria and Veillonella) were observed.

CONCLUSIONS

These results suggest that PPIs cause both oral and gut microbiota alterations.

The musk chemical composition and microbiota of Chinese forest musk deer males

Male musk deer secrete musk from the musk gland located between their naval and genitals. Unmated male forest musk deer generate a greater amount of musk than mated males, potentially allowing them to attract a greater number of females. In this study, we used gas chromatography and mass spectrometry (GC/MS) to explore musk chemical composition of the musk pods of captive mated and unmated sexually mature Chinese forest musk deer and used next-generation sequencing to intensively survey the bacterial communities within them. Analysis of the chemical composition of the musk showed that unmated males have more muscone and cholesterol. Features of the musk16S rRNA gene showed that mated Chinese forest musk deer have both a greater Shannon diversity (p < 0.01) and a greater number of estimated operational taxonomic units than unmated ones; many bacterial genera were overrepresented in unmated Chinese forest musk deer males. Members of these genera might be involved in musk odor fermentation. PICRUSt analysis revealed that metabolic pathways such as aldosterone-regulated sodium reabsorption, metabolism of terpenoids and polyketides, flavone and flavonol biosynthesis, and isoflavonoid biosynthesis were enriched in the musk of unmated Chinese forest musk deer males.

Network construction and structure detection with metagenomic count data

Background

The human microbiome plays a critical role in human health. Massive amounts of metagenomic data have been generated with advances in next-generation sequencing technologies that characterize microbial communities via direct isolation and sequencing. How to extract, analyze, and transform these vast amounts of data into useful knowledge is a great challenge to bioinformaticians. Microbial biodiversity research has focused primarily on taxa composition and abundance and less on the co-occurrences among different taxa. However, taxa co-occurrences and their relationships to environmental and clinical conditions are important because network structure may help to understand how microbial taxa function together.

Results

We propose a systematic robust approach for bacteria network construction and structure detection using metagenomic count data. Pairwise similarity/distance measures between taxa are proposed by adapting distance measures for samples in ecology. We also extend the sparse inverse covariance approach to a sparse inverse of a similarity matrix from count data for network construction. Our approach is efficient for large metagenomic count data with thousands of bacterial taxa. We evaluate our method with real and simulated data. Our method identifies true and biologically significant network structures efficiently.

Conclusions

Network analysis is crucial for detecting subnetwork structures with metagenomic count data. We developed a software tool in MATLAB for network construction and biologically significant module detection. Software MetaNet can be downloaded from http://biostatistics.csmc.edu/MetaNet/.

Phylogenetics and the human microbiome

The human microbiome is the ensemble of genes in the microbes that live inside and on the surface of humans. Because microbial sequencing information is now much easier to come by than phenotypic information, there has been an explosion of sequencing and genetic analysis of microbiome samples. Much of the analytical work for these sequences involves phylogenetics, at least indirectly, but methodology has developed in a somewhat different direction than for other applications of phylogenetics. In this article, I review the field and its methods from the perspective of a phylogeneticist, as well as describing current challenges for phylogenetics coming from this type of work.

Interactions in the microbiome: communities of organisms and communities of genes

A central challenge in microbial community ecology is the delineation of appropriate units of biodiversity, which can be taxonomic, phylogenetic, or functional in nature. The term 'community' is applied ambiguously; in some cases, the term refers simply to a set of observed entities, while in other cases, it requires that these entities interact with one another. Microorganisms can rapidly gain and lose genes, potentially decoupling community roles from taxonomic and phylogenetic groupings. Trait-based approaches offer a useful alternative, but many traits can be defined based on gene functions, metabolic modules, and genomic properties, and the optimal set of traits to choose is often not obvious. An analysis that considers taxon assignment and traits in concert may be ideal, with the strengths of each approach offsetting the weaknesses of the other. Individual genes also merit consideration as entities in an ecological analysis, with characteristics such as diversity, turnover, and interactions modeled using genes rather than organisms as entities. We identify some promising avenues of research that are likely to yield a deeper understanding of microbial communities that shift from observation-based questions of 'Who is there?' and 'What are they doing?' to the mechanistically driven question of 'How will they respond?'

Convergence of gut microbiomes in myrmecophagous mammals

Mammals have diversified into many dietary niches. Specialized myrmecophagous (ant- and termite-eating) placental mammals represent a textbook example of evolutionary convergence driven by extreme diet specialization. Armadillos, anteaters, aardvarks, pangolins and aardwolves thus provide a model system for understanding the potential role of gut microbiota in the convergent adaptation to myrmecophagy. Here, we expand upon previous mammalian gut microbiome studies by using high-throughput barcoded Illumina sequencing of the 16S rRNA gene to characterize the composition of gut microbiota in 15 species representing all placental myrmecophagous lineages and their close relatives from zoo- and field-collected samples. We confirm that both diet and phylogeny drive the evolution of mammalian gut microbiota, with cases of convergence in global composition, but also examples of phylogenetic inertia. Our results reveal specialized placental myrmecophages as a spectacular case of large-scale convergence in gut microbiome composition. Indeed, neighbour-net networks and beta-diversity plots based on UniFrac distances show significant clustering of myrmecophagous species (anteaters, aardvarks and aardwolves), even though they belong to phylogenetically distant lineages representing different orders. The aardwolf, which diverged from carnivorous hyenas only in the last 10 million years, experienced a convergent shift in the composition of its gut microbiome to become more similar to other myrmecophages. These results confirm diet adaptation to be a major driving factor of convergence in gut microbiome composition over evolutionary timescales. This study sets the scene for future metagenomic studies aiming at evaluating potential convergence in functional gene content in the microbiomes of specialized mammalian myrmecophages.