Direct interaction network inference for compositional data via codaloss

2020 ◽  
Vol 18 (06) ◽  
pp. 2050037
Author(s):  
Liang Chen ◽  
Shun He ◽  
Yuyao Zhai ◽  
Minghua Deng

16S rRNA gene sequencing and whole microbiome sequencing make it possible and stable to quantitatively analyze the composition of microbial communities and the relationship among microbial communities, microbes, and hosts. One essential step in the analysis of microbiome compositional data is inferring the direct interaction network among microbial species, bringing to light the potential underlying mechanism that regulates interaction in their communities. However, standard statistical analysis may obtain spurious results due to compositional nature of microbiome data; therefore, network recovery of microbial communities remains challenging. Here, we propose a novel loss function called codaloss for direct microbes interaction network estimation under the sparsity assumptions. We develop an alternating direction optimization algorithm to obtain sparse solution of codaloss as estimator. Compared to other state-of-the-art methods, our model makes less assumptions about the microbial networks. The simulation and real microbiome data results show that our method outperforms other methods in network inference. An implementation of codaloss is available from https://github.com/xuebaliang/Codaloss .

2019 ◽  
Vol 35 (18) ◽  
pp. 3404-3411 ◽  
Author(s):  
Huili Yuan ◽  
Shun He ◽  
Minghua Deng

Abstract Motivation With the development of high-throughput sequencing techniques for 16S-rRNA gene profiling, the analysis of microbial communities is becoming more and more attractive and reliable. Inferring the direct interaction network among microbial communities helps in the identification of mechanisms underlying community structure. However, the analysis of compositional data remains challenging by the relative information conveyed by such data, as well as its high dimensionality. Results In this article, we first propose a novel loss function for compositional data called CD-trace based on D-trace loss. A sparse matrix estimator for the direct interaction network is defined as the minimizer of lasso penalized CD-trace loss under positive-definite constraint. An efficient alternating direction algorithm is developed for numerical computation. Simulation results show that CD-trace compares favorably to gCoda and that it is better than sparse inverse covariance estimation for ecological association inference (SPIEC-EASI) (hereinafter S-E) in network recovery with compositional data. Finally, we test CD-trace and compare it to the other methods noted above using mouse skin microbiome data. Availability and implementation The CD-trace is open source and freely available from https://github.com/coamo2/CD-trace under GNU LGPL v3. Supplementary information Supplementary data are available at Bioinformatics online.


2021 ◽  
Vol 12 ◽  
Author(s):  
Marc Crampon ◽  
Coralie Soulier ◽  
Pauline Sidoli ◽  
Jennifer Hellal ◽  
Catherine Joulian ◽  
...  

The demand for energy and chemicals is constantly growing, leading to an increase of the amounts of contaminants discharged to the environment. Among these, pharmaceutical molecules are frequently found in treated wastewater that is discharged into superficial waters. Indeed, wastewater treatment plants (WWTPs) are designed to remove organic pollution from urban effluents but are not specific, especially toward contaminants of emerging concern (CECs), which finally reach the natural environment. In this context, it is important to study the fate of micropollutants, especially in a soil aquifer treatment (SAT) context for water from WWTPs, and for the most persistent molecules such as benzodiazepines. In the present study, soils sampled in a reed bed frequently flooded by water from a WWTP were spiked with diazepam and oxazepam in microcosms, and their concentrations were monitored for 97 days. It appeared that the two molecules were completely degraded after 15 days of incubation. Samples were collected during the experiment in order to follow the dynamics of the microbial communities, based on 16S rRNA gene sequencing for Archaea and Bacteria, and ITS2 gene for Fungi. The evolution of diversity and of specific operating taxonomic units (OTUs) highlighted an impact of the addition of benzodiazepines, a rapid resilience of the fungal community and an evolution of the bacterial community. It appeared that OTUs from the Brevibacillus genus were more abundant at the beginning of the biodegradation process, for diazepam and oxazepam conditions. Additionally, Tax4Fun tool was applied to 16S rRNA gene sequencing data to infer on the evolution of specific metabolic functions during biodegradation. It finally appeared that the microbial community in soils frequently exposed to water from WWTP, potentially containing CECs such as diazepam and oxazepam, may be adapted to the degradation of persistent contaminants.


2021 ◽  
Vol 15 ◽  
Author(s):  
Xue Gong ◽  
Cheng Huang ◽  
Xun Yang ◽  
Jianjun Chen ◽  
Juncai Pu ◽  
...  

The microbiota–gut–brain axis has been considered to play an important role in the development of depression, but the underlying mechanism remains unclear. The gastrointestinal tract is home to trillions of microbiota and the colon is considered an important site for the interaction between microbiota and host, but few studies have been conducted to evaluate the alterations in the colon. Accordingly, in this study, we established a chronic social defeated stress (CSDS) mice model of depression. We applied 16S rRNA gene sequencing to assess the gut microbial composition and gas and liquid chromatography–mass spectroscopy to identify fecal metabolites and colonic lipids, respectively. Meanwhile, we used Spearman’s correlation analysis method to evaluate the associations between the gut microbiota, fecal metabolites, colonic lipids, and behavioral index. In total, there were 20 bacterial taxa and 18 bacterial taxa significantly increased and decreased, respectively, in the CSDS mice. Further, microbial functional prediction demonstrated a disturbance of lipid, carbohydrate, and amino acid metabolism in the CSDS mice. We also found 20 differential fecal metabolites and 36 differential colonic lipids (in the category of glycerolipids, glycerophospholipids, and sphingolipids) in the CSDS mice. Moreover, correlation analysis showed that fecal metabolomic signature was associated with the alterations in the gut microbiota composition and colonic lipidomic profile. Of note, three lipids [PC(16:0/20:4), PG(22:6/22:6), and PI(18:0/20:3), all in the category of glycerophospholipids] were significantly associated with anxiety- and depression-like phenotypes in mice. Taken together, our results indicated that the gut microbiota might be involved in the pathogenesis of depression via influencing fecal metabolites and colonic glycerophospholipid metabolism.


2020 ◽  
Author(s):  
Jeffrey Marlow ◽  
Rachel Spietz ◽  
Keun-Young Kim ◽  
Mark Ellisman ◽  
Peter Girguis ◽  
...  

AbstractCoastal salt marshes are key sites of biogeochemical cycling and ideal systems in which to investigate the community structure of complex microbial communities. Here, we clarify structural-functional relationships among microorganisms and their mineralogical environment, revealing previously undescribed metabolic activity patterns and precise spatial arrangements within salt marsh sediment. Following 3.7-day in situ incubations with a non-canonical amino acid that was incorporated into new biomass, samples were embedded and analyzed by correlative fluorescence and electron microscopy to map the microscale arrangements of anabolically active and inactive organisms alongside mineral grains. Parallel sediment samples were examined by fluorescence-activated cell sorting and 16S rRNA gene sequencing to link anabolic activity to taxonomic identity. Both approaches demonstrated a rapid decline in the proportion of anabolically active cells with depth into salt marsh sediment, from ∼60% in the top cm to 10-25% between 2-7 cm. From the top to the bottom, the most prominent active community members shifted from sulfur cycling phototrophic consortia, to sulfate-reducing bacteria likely oxidizing organic compounds, to fermentative lineages. Correlative microscopy revealed more abundant (and more anabolically active) organisms around non-quartz minerals including rutile, orthoclase, and plagioclase. Microbe-mineral relationships appear to be dynamic and context-dependent arbiters of biogeochemical cycling.Statement of SignificanceMicroscale spatial relationships dictate critical aspects of a microbiome’s inner workings and emergent properties, such as evolutionary pathways, niche development, and community structure and function. However, many commonly used methods in microbial ecology neglect this parameter – obscuring important microbe-microbe and microbe-mineral interactions – and instead employ bulk-scale methodologies that are incapable of resolving these intricate relationships.This benchmark study presents a compelling new approach for exploring the anabolic activity of a complex microbial community by mapping the precise spatial configuration of anabolically active organisms within mineralogically heterogeneous sediment through in situ incubation, resin embedding, and correlative fluorescence and electron microscopy. In parallel, active organisms were identified through fluorescence-activated cell sorting and 16S rRNA gene sequencing, enabling a powerful interpretive framework connecting location, identity, activity, and putative biogeochemical roles of microbial community members.We deploy this novel approach in salt marsh sediment, revealing quantitative insights into the fundamental principles that govern the structure and function of sediment-hosted microbial communities. In particular, at different sediment horizons, we observed striking changes in the proportion of anabolically active cells, the identities of the most prominent active community members, and the nature of microbe-mineral affiliations. Improved approaches for understanding microscale ecosystems in a new light, such as those presented here, reveal environmental parameters that promote or constrain metabolic activity and clarify the impact that microbial communities have on our world.


2021 ◽  
Vol 12 ◽  
Author(s):  
Sarah Zecchin ◽  
Simona Crognale ◽  
Patrizia Zaccheo ◽  
Stefano Fazi ◽  
Stefano Amalfitano ◽  
...  

Arsenic mobilization in groundwater systems is driven by a variety of functionally diverse microorganisms and complex interconnections between different physicochemical factors. In order to unravel this great ecosystem complexity, groundwaters with varying background concentrations and speciation of arsenic were considered in the Po Plain (Northern Italy), one of the most populated areas in Europe affected by metalloid contamination. High-throughput Illumina 16S rRNA gene sequencing, CARD-FISH and enrichment of arsenic-transforming consortia showed that among the analyzed groundwaters, diverse microbial communities were present, both in terms of diversity and functionality. Oxidized inorganic arsenic [arsenite, As(III)] was the main driver that shaped each community. Several uncharacterized members of the genus Pseudomonas, putatively involved in metalloid transformation, were revealed in situ in the most contaminated samples. With a cultivation approach, arsenic metabolisms potentially active at the site were evidenced. In chemolithoautotrophic conditions, As(III) oxidation rate linearly correlated to As(III) concentration measured at the parental sites, suggesting that local As(III) concentration was a relevant factor that selected for As(III)-oxidizing bacterial populations. In view of the exploitation of these As(III)-oxidizing consortia in biotechnology-based arsenic bioremediation actions, these results suggest that contaminated aquifers in Northern Italy host unexplored microbial populations that provide essential ecosystem services.


2020 ◽  
Vol 88 (12) ◽  
Author(s):  
Eric L. Brown ◽  
Heather T. Essigmann ◽  
Kristi L. Hoffman ◽  
Noah W. Palm ◽  
Sarah M. Gunter ◽  
...  

ABSTRACT Mucosal surfaces like those present in the lung, gut, and mouth interface with distinct external environments. These mucosal gateways are not only portals of entry for potential pathogens but also homes to microbial communities that impact host health. Secretory immunoglobulin A (SIgA) is the single most abundant acquired immune component secreted onto mucosal surfaces and, via the process of immune exclusion, shapes the architecture of these microbiomes. Not all microorganisms at mucosal surfaces are targeted by SIgA; therefore, a better understanding of the SIgA-coated fraction may identify the microbial constituents that stimulate host immune responses in the context of health and disease. Chronic diseases like type 2 diabetes are associated with altered microbial communities (dysbiosis) that in turn affect immune-mediated homeostasis. 16S rRNA gene sequencing of SIgA-coated/uncoated bacteria (IgA-Biome) was conducted on stool and saliva samples of normoglycemic participants and individuals with prediabetes or diabetes (n = 8/group). These analyses demonstrated shifts in relative abundance in the IgA-Biome profiles between normoglycemic, prediabetic, or diabetic samples distinct from that of the overall microbiome. Differences in IgA-Biome alpha diversity were apparent for both stool and saliva, while overarching bacterial community differences (beta diversity) were also observed in saliva. These data suggest that IgA-Biome analyses can be used to identify novel microbial signatures associated with diabetes and support the need for further studies exploring these communities. Ultimately, an understanding of the IgA-Biome may promote the development of novel strategies to restructure the microbiome as a means of preventing or treating diseases associated with dysbiosis at mucosal surfaces.


2022 ◽  
Vol 10 (1) ◽  
pp. 170
Author(s):  
Andrey L. Rakitin ◽  
Shahjahon Begmatov ◽  
Alexey V. Beletsky ◽  
Dmitriy A. Philippov ◽  
Vitaly V. Kadnikov ◽  
...  

Large areas in the northern hemisphere are covered by extensive wetlands, which represent a complex mosaic of raised bogs, eutrophic fens, and aapa mires all in proximity to each other. Aapa mires differ from other types of wetlands by their concave surface, heavily watered by the central part, as well as by the presence of large-patterned string-flark complexes. In this paper, we characterized microbial diversity patterns in the surface peat layers of the neighboring string and flark structures located within the mire site in the Vologda region of European North Russia, using 16S rRNA gene sequencing. The microbial communities in raised strings were clearly distinct from those in submerged flarks. Strings were dominated by the Alpha- and Gammaproteobacteria. Other abundant groups were the Acidobacteriota, Bacteroidota, Verrucomicrobiota, Actinobacteriota, and Planctomycetota. Archaea accounted for only 0.4% of 16S rRNA gene sequences retrieved from strings. By contrast, they comprised about 22% of all sequences in submerged flarks and mostly belonged to methanogenic lineages. Methanotrophs were nearly absent. Other flark-specific microorganisms included the phyla Chloroflexi, Spirochaetota, Desulfobacterota, Beijerinckiaceae- and Rhodomicrobiaceae-affiliated Alphaproteobacteria, and uncultivated groups env.OPS_17 and vadinHA17 of the Bacteroidota. Such pattern probably reflects local anaerobic conditions in the submerged peat layers in flarks.


Animals ◽  
2020 ◽  
Vol 10 (2) ◽  
pp. 204 ◽  
Author(s):  
Kristoffer Relling Tysnes ◽  
Inga Leena Angell ◽  
Iselin Fjellanger ◽  
Sigrid Drageset Larsen ◽  
Silje Rebekka Søfteland ◽  
...  

Although our understanding of the role of the gut microbiota in different diseases is improving, our knowledge regarding how the gut microbiota affects functioning in healthy individuals is still limited. Here, we hypothesize that the gut microbiota could be associated with sled dog endurance-race performance. We investigated the gut microbiota in 166 fecal samples from 96 Alaskan Huskies, representing 16 teams participating in the 2016 Femund Race (400 km) in Norway, relating the microbiota composition to performance and metadata derived from questionnaires. For 16S rRNA gene sequencing-derived compositional data, we found a strong negative association between Enterobacteriaceae (dysbiosis-associated) and Clostridium hiranonis (normobiosis-associated). The teams with the best performances showed both the lowest levels of dysbiosis-associated bacteria prior to the race and the lowest change (decrease) in these bacteria after the race. Taken together, our results support the hypothesis that normobiosis-associated bacteria are involved in resilience mechanisms, potentially preventing growth of Enterobacteriaceae during the race.


2021 ◽  
pp. 1-13
Author(s):  
Gilda Varliero ◽  
Alexandra Holland ◽  
Gary L. A. Barker ◽  
Marian L. Yallop ◽  
Andrew G. Fountain ◽  
...  

Abstract Distant glacial areas are interconnected by a complex system of fractures and water channels which run in the glacier interior and characterize the englacial realm. Water can slowly freeze in these channels where the slow freezing excludes air bubbles giving the ice a clear aspect. This ice is uplifted to the surface ablation zone by glacial movements and can therefore be observed in the form of clear surface ice bands. We employed an indirect method to sample englacial water by coring these ice bands. We were able, for the first time, to compare microbial communities sampled from clear (i.e. frozen englacial water bands) and cloudy ice (i.e. meteoric ice) through 16S rRNA gene sequencing. Although microbial communities were primarily shaped and structured by their spatial distribution on the glacier, ice type was a clear secondary factor. One area of the glacier, in particular, presented significant microbial community clear/cloudy ice differences. Although the clear ice and supraglacial communities showed typical cold-adapted glacial communities, the cloudy ice had a less defined glacial community and ubiquitous environmental organisms. These results highlight the role of englacial channels in the microbial dispersion within the glacier and, possibly, in the shaping of glacial microbial communities.


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