Correlation detection strategies in microbial data sets vary widely in sensitivity and precision

Effects of herbicide mixtures on the diversity and composition of microbial community and nitrogen cycling function on agricultural soil: A field experiment in Northeast China

Herbicide mixtures application is a widespread and effective practice in modern agriculture; however, a knowledge gap exists regarding the potential ecotoxicological effects of herbicide mixtures in agricultural systems. Here, the effects of various doses of herbicide mixtures (atrazine, nicosulfuron, and mesotrione) under different varieties of maize cultivation on the structure and function of microbial communities and soil chemical parameters were clarified through field experiments. The results showed that the application of herbicide mixtures increased the bacterial and fungal community alpha diversity at jointing and maturity, indicating a prolonged effect of the herbicide mixtures. Moreover, herbicide mixtures alter the composition of bacterial and fungal communities, with sensitive taxa suppressed and herbicide-tolerant taxa enriched. The herbicide mixtures significantly reduced the abundances of Bacillus even at lower doses, but Penicillum was enriched. FAPROTAX analysis and quantitative PCR (qPCR) results showed that herbicide mixtures inhibited the soil nitrogen-cycle process and related genes AOA-amoA, AOB-amoA, and nifH at maize seedling stage. Moreover, network analysis showed that low concentrations of the herbicide mixtures increased bacterial interactions while high concentrations inhibited them, which indicated that the network complexity may be herbicide concentration dependent. A synthetic community (SynCom) consisting of six bacterial strains was established for the biodegradation of the herbicide mixtures based on the analysis of the bacterial network, which resulted in an increase in the degradation efficiency of nicosulfuron by 15.90%. Moreover, potted maize experiment showed that the addition of the SynCom alleviated the toxic effects of herbicide mixtures on the plants. In summary, this study provides a comprehensive perspective for assessing the ecological risk at taxonomic and functional levels and the biodegradation approach of herbicide mixtures residue on agricultural soils in Northeastern China.

A blueprint for contemporary studies of microbiomes

This editorial piece co-authored by the Senior Editors at Microbiome aims to highlight current challenges in the field of environmental and host-associated microbiome research. We also take the opportunity to clarify our expectations for the articles submitted to the journal. At Microbiome, we are seeking studies that provide either new mechanistic insights into the role of microbiomes in health and environmental systems or substantial conceptual or technical advances. Manuscripts need to meet high standards of language accuracy, quality of microbiome analyses, and data and protocol availability, including detailed reporting of wet-lab and in silico protocols, all of which can critically enhance transparency and reproducibility. We think that such efforts are essential to push the boundaries of our knowledge on microbiomes in a concerted, international effort.

Breast milk stabilizes bacterial communities in the large intestine even after weaning

The development and maintenance of a balanced microbiota is crucial for human health. Milk contains immune factors that not only protect offspring from infectious diseases but also play an important role in promoting the development and maintenance of the microbiota. However, the persisting effects of milk-derived immune factors on the maintenance of the microbiota after weaning have not been carefully examined. In this study, a cross-fostering model was employed using immunocompetent (IC) and immunodeficient (ID) mice in which one-half of the pups born from two dams were replaced. As a result, breast milk from the IC dam (IC milk) affected the development of the microbiota during lactation and maintained it even after weaning in the large intestine of the ID pups. The large intestinal microbiota of ID pups raised on IC milk remained similar to that of normal IC pups. Under normal conditions, the genus Mucispirillum was closely associated with other bacteria, forming a diverse bacterial community in the large intestine. In the small intestine, there were no differences in the microbiota before weaning, regardless of whether IC or ID milk was consumed. By contrast, significant differences were observed in the small intestinal microbiota between IC and ID mice after weaning; however, this was dependent on the immune-related characteristics of offspring (rather than milk-derived immune factors). These results indicate that breast milk plays an important role in the large (not small) intestine of offspring to create and maintain a diverse microbiota with a balanced bacterial network even after weaning.

Archaeal diversity in the microbiomes of four wild bird species

Archaea are generally low-abundance members of the vertebrate microbiota that require specific PCR primers to be detected in metabarcoding studies, and the robust intraspecific sample size is necessary for well-supported conclusions about archaeal diversity. Using 16S rRNA gene amplicons generated using both Archaea-Specific and Universal primers, we investigated prokaryotic diversity in 110 fecal samples from four wild bird species from four different orders: Anna's Hummingbird (Calypte anna), Saltmarsh Sparrow (Ammospiza caudacuta), Ruddy Turnstone (Arenaria interpres), and Canada Goose (Branta canadensis). Our aim was to test the hypotheses that Archaea-Specific primers would offer higher resolution of archaeal diversity and that the four ecologically distinct host species would have distinct archaeal communities. Archaea-Specific primers resulted in increases in archaeal richness and detection of Archaea in all four birds compared to the Universal primers. The ammonia-oxidizing archaeal order Nitrososphaerales was detected in all four host species, and methanogenic orders were enriched in samples from Canada Geese. In Bacteria-Archaea co-occurrence networks, Archaea-Specific primers found many more significant interactions than the Universal primers alone. Methanogenic archaeal orders dominated the microbiota in Canada Geese and were found to a lesser extent in the other host species, suggesting an important functional role of methanogens in Canada Geese. Overall, this study advances our knowledge of the archaeal component of the microbiome in wild birds and provides insight into the potential functional roles Archaea play in studies of avian gastrointestinal microbiota.

IMPORTANCE

Archaea may be persistent members of host-associated microbiomes across diverse host taxa; their detection has been limited due to their low abundance and the inadequacy of Universal primers. Large-scale studies of Archaea in vertebrate microbiomes have historically had low intraspecific sample sizes for bird species and had conflicting results. This study demonstrates the improved capability of the Archaea-Specific primers to detect archaeal diversity in diverse avian host species compared to the widely used Universal primers. We also identified both shared and species-specific archaeal taxa across four ecologically distinct avian host species from four different orders with implications for functional importance. Future studies interested in comprehensively cataloging prokaryotic diversity in avian microbiomes using amplicon-based sequencing methods should include Archaea-Specific primers to adequately probe archaeal diversity.

Seasonal lake ice cover drives the restructuring of bacteria-archaea and bacteria-fungi interdomain ecological networks across diverse habitats

The coexistence of different microbial communities is fundamental to the sustainability of many ecosystems, yet our understanding of the relationships among microbial communities in plateau cold-region lakes affected by seasonal ice cover remains limited. This research involved investigating three lakes in the Inner Mongolia segment of the Yellow River basin during frozen and unfrozen periods in two habitats: water bodies and sediments. The research examined the composition and function of bacteria, archaea, and fungi across different times and habitats within the basin, their response to environmental variables in water and sediment, and inter-domain interactions between bacteria-archaea and bacteria-fungi were compared using interdomain ecological network (IDEN). The findings indicate significant variations in the structures of bacterial, archaeal, and fungal communities across different periods and habitats, with the pH of the water body being a crucial environmental variable affecting microbial community composition. In the frozen period, the functionality of microbial communities, especially in terms of energy metabolism, was significantly impacted, with water bodies experiencing more pronounced effects than sediments. Archaea and fungi significantly contribute to the stability of bacterial communities across various habitats, especially in ice-covered conditions, where stronger associations between bacterial communities, archaea, and fungi promote the microbial communities' adaptability to cold stress. Furthermore, our results indicate that the primary environmental variable influencing the structure of IDENs is the nutrient salt content in both water bodies and sediments. This study broadens our understanding of the responses and feedback mechanisms of inter-domain microbial interactions in lakes influenced by seasonal ice cover.

Disruption and adaptation: infant gut microbiota's dynamic response to SARS-CoV-2 infection

BACKGROUND

The responses of the infant gut microbiota to infection significantly disrupt the natural intrahost evolutionary processes of the microbiome. Here, we collected a 16-month longitudinal cohort of infant gut microbiomes affected by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Then, we developed a multicriteria approach to identify core interaction network driving community dynamics under environmental disturbances, which we termed the Conserved Variated Interaction Group (CVIgroup).

RESULTS

The CVIgroup showed significant advantages on pinpointing a sparse set associated with the disturbances, as validated both our own and publicly available datasets. Leveraging the Oxford Nanopore Technology, we found this group facilitates the ecosystem's adaptation to environmental disruptions by enhancing the mobility of mobile genetic elements, including the reinforcement of the twin-arginine translocation pathway in response to increased virulence factors. Furthermore, the CVIgroup serves as an effective indicator of ecosystem health. The timescale for the gut microbiota's adaptation extends beyond 10 months. Members of the CVIgroup, such as Bacteroides thetaiotaomicron and Faecalibacterium, exhibit varying degrees of genomic structural variants, which contribute to guiding the community toward a new stable state rather than returning to its original configuration.

CONCLUSIONS

Collectively, the CVIgroup offers a snapshot of the gut microbiota's adaptive response to environmental disturbances. The disruption and subsequent adaptation of the gut microbiota in infants after COVID-19 infection underscores the necessity of re-evaluating reference standards in the context of the post-pandemic era. Video Abstract.

Cross-validation for training and testing co-occurrence network inference algorithms

BACKGROUND

Microorganisms are found in almost every environment, including soil, water, air and inside other organisms, such as animals and plants. While some microorganisms cause diseases, most of them help in biological processes such as decomposition, fermentation and nutrient cycling. Much research has been conducted on the study of microbial communities in various environments and how their interactions and relationships can provide insight into various diseases. Co-occurrence network inference algorithms help us understand the complex associations of micro-organisms, especially bacteria. Existing network inference algorithms employ techniques such as correlation, regularized linear regression, and conditional dependence, which have different hyper-parameters that determine the sparsity of the network. These complex microbial communities form intricate ecological networks that are fundamental to ecosystem functioning and host health. Understanding these networks is crucial for developing targeted interventions in both environmental and clinical settings. The emergence of high-throughput sequencing technologies has generated unprecedented amounts of microbiome data, necessitating robust computational methods for network inference and validation.

RESULTS

Previous methods for evaluating the quality of the inferred network include using external data, and network consistency across sub-samples, both of which have several drawbacks that limit their applicability in real microbiome composition data sets. We propose a novel cross-validation method to evaluate co-occurrence network inference algorithms, and new methods for applying existing algorithms to predict on test data. Our method demonstrates superior performance in handling compositional data and addressing the challenges of high dimensionality and sparsity inherent in real microbiome datasets. The proposed framework also provides robust estimates of network stability.

CONCLUSIONS

Our empirical study shows that the proposed cross-validation method is useful for hyper-parameter selection (training) and comparing the quality of inferred networks between different algorithms (testing). This advancement represents a significant step forward in microbiome network analysis, providing researchers with a reliable tool for understanding complex microbial interactions. The method's applicability extends beyond microbiome studies to other fields where network inference from high-dimensional compositional data is crucial, such as gene regulatory networks and ecological food webs. Our framework establishes a new standard for validation in network inference, potentially accelerating discoveries in microbial ecology and human health.

With a little help from my friends: importance of protist-protist interactions in structuring marine protistan communities in the San Pedro Channel

Marine protists form complex communities that are shaped by environmental and biological ecosystem properties, as well as ecological interactions between organisms. While all of these factors play a role in shaping protistan communities, the specific ways in which these properties and interactions influence protistan communities remain poorly understood. Fourteen years and 9 months of eukaryotic amplicon (18S-V4 rRNA gene) data collected monthly at the San Pedro Ocean Time-series (SPOT) station were used to evaluate the impacts that environmental and biological factors, and protist-protist interactions had on protistan community composition. Statistical analysis of the amplicon data revealed that seasonal patterns in protistan community composition were apparent, but that the environmental data collected through routine time-series sampling efforts could not explain most of the variability that was evident in the communities. To identify some of the protist-protist interactions that may have played a role in shaping protistan communities, ecological networks were constructed using the amplicon data and the network predictions were compared against a database of confirmed protist-protist interactions. The database comparisons revealed hundreds of established parasitic, predator-prey, photosymbiotic, and mutualistic relationships in the networks. Although many interactions were confirmed using the database, these confirmed interactions constituted only 2% of the interactions identified at the SPOT station, highlighting the need to better characterize protist-protist interactions in marine environments. Finally, the network-predicted interactions that were not found in the database were used to identify putative, novel protist-protist interactions that may have played a role in structuring the protistan communities at the SPOT station.

IMPORTANCE

Network analyses are commonly used to identify some of the ecological interactions that may be occurring between protists in the ocean; however, evaluating predictions obtained from these analyses remains difficult due to the large number of interactions that may be recovered and the limited amount of information available on protist-protist interactions in nature. In this study, ecological network analyses were conducted using data collected at the San Pedro Ocean Time-series (SPOT) station and the network predictions were compared against a database of established protist-protist interactions. These database comparisons revealed hundreds of confirmed protist-protist interactions, and thousands of putative, novel interactions that may be occurring at the SPOT station. The database comparisons carried out in this study provide a new way of evaluating network predictions and highlight the complex, yet critical role that ecological interactions play in shaping protistan community composition in marine ecosystems.

Response of Soil Phage Communities and Prokaryote-Phage Interactions to Long-Term Drought

Soil moisture is a fundamental factor affecting terrestrial ecosystem functions. In this study, microscopic enumeration and joint metaviromic and metagenomic sequencing were employed together to investigate the impact of prolonged drought on soil phage communities and their interactions with prokaryotes in a subtropical evergreen forest. Our findings revealed a marked reduction in the abundances of prokaryotic and viral-like particles, by 73.1% and 75.2%, respectively, and significantly altered the structure of prokaryotic and phage communities under drought. Meanwhile, drought substantially increased the fraction of prokaryotic communities containing lysogenic phages by 163%, as well as the proportion of temperate phages. Nonetheless, drought likely amplified negative prokaryote-phage interactions given the nearly doubled proportion of negative links in the prokaryote-phage co-occurrence network, as well as the higher frequency and diversity of antiphage defense systems found in prokaryotic genomes. Under drought, soil phages exerted greater top-down control on typical soil k-strategists including Acidobacteria and Chloroflexi. Moreover, phage-encoded auxiliary metabolic genes may impact host metabolism in biosynthesis-related functions. Collectively, the findings of this study underscore the profound impact of drought on soil phages and prokaryote-phage interactions. These results also emphasize the importance of managing soil moisture levels during soil amendment and microbiome manipulation to account for the influence of soil phages.

Dual-Domain Primary Succession of Bacteria in Glacier Forefield Streams and Soils of a Maritime and Continental Glacier

Glaciers retreat rapidly and create newly exposed terrestrial and aquatic habitats in glacier forefields, where primary succession proceeds synchronously in glacier forefields. Here, we introduced the "Dual-Domain Primary Succession" concept to examine the parallel yet distinct primary succession processes in soil and stream ecosystems within glacier forefields, by focusing on Hailuogou Glacier and Urumqi Glacier No.1 in China. Findings showed that soil bacterial communities exhibited higher α-diversity with a decreasing pattern in Hailuogou Glacier, in contrast to Urumqi Glacier No.1, which displayed lower and unimodally distributed α-diversity along the glacier forefield chronosequence (GFC). A similar pattern emerged in streams, except for an increasing α-diversity trend in Urumqi Glacier No.1 stream along the GFC. Additionally, α-diversity in streams changed more rapidly than in soils for Hailuogou Glacier, but more slowly for Urumqi Glacier No.1. Along GFC, both soil and stream bacterial communities experienced spatial variations, primarily due to species turnover. The succession of community composition was evident at the OTU level, with each module in the co-occurrence network consisting of OTUs enriched at specific successional stages. A substantial number of OTUs shared between paired soil and stream samples showed a decreasing trend along the GFC, while β-diversity increased. The results suggested that bacterial communities have a similar succession pattern but in different pace between soil and stream while having distinct successional trajectories between the studied glaciers. This study highlighted the "Dual-Domain Primary Succession" in glacier forefields, but further studies with more glaciers are necessary to make broader generalizations.

Enhancing microbial predator-prey detection with network and trait-based analyses

BACKGROUND

Network analyses are often applied to microbial communities using sequencing survey datasets. However, associations in such networks do not necessarily indicate actual biotic interactions, and even if they do, the nature of the interactions commonly remains unclear. While network analyses are valuable for generating hypotheses, the inferred hypotheses are rarely experimentally confirmed.

RESULTS

We employed cross-kingdom network analyses, applied trait-based functions to the microorganisms, and subsequently experimentally investigated the found putative predator-prey interactions to evaluate whether, and to what extent, correlations indicate actual predator-prey relationships. For this, we investigated algae and their protistan predators in biocrusts of three distinct polar regions, i.e., Svalbard, the Antarctic Peninsula, and Continental Antarctica. Network analyses using FlashWeave indicated that 89, 138, and 51 correlations occurred between predatory protists and algae, respectively. However, trait assignment revealed that only 4.7-9.3% of said correlations link predators to actually suitable prey. We further confirmed these results with HMSC modeling, which resulted in similar numbers of 7.5% and 4.8% linking predators to suitable prey for full co-occurrence and abundance models, respectively. The combination of network analyses and trait assignment increased confidence in the prediction of predator-prey interactions, as we show that 82% of all experimentally investigated correlations could be verified. Furthermore, we found that more vicious predators, i.e., predators with the highest growth rate in co-culture with their prey, exhibit higher stress and betweenness centrality - giving rise to the future possibility of determining important predators from their network statistics.

CONCLUSIONS

Our results support the idea of using network analyses for inferring predator-prey interactions, but at the same time call for cautionary consideration of the results, by combining them with trait-based approaches to increase confidence in the prediction of biological interactions. Video Abstract.

Topology of gut Microbiota Network and Guild-Based Analysis in Chinese Adults

Gut microbiota with co-abundant behaviors is considered belonging to the same guild in micro-ecosystem. In this study, we established co-abundance networks of operational taxonomic units (OTUs) among 2944 Chinese adults from the Shanghai Men's and Women's Health Studies and observed a positive connection-dominated scale-free network using Sparse Correlations for Compositional data (SparCC). The closeness centrality was negatively correlated with other degree-based topological metrics in the network, indicating the isolated modularization of the bacteria. A total of 130 guilds were constructed, with a high modularity of 0.68, and retaining more diversity of OTUs than genus classification. The scores of guild structure similarity for comparisons between all, the healthy and the unhealthy subjects were higher than those derived from randomized permutations, suggesting a robust guild structure. We further used the constructed 130 guilds as the aggregation units to identify gut microbiota that may be associated with type 2 diabetes, and found that the OTUs in 21 significant guilds relevant to diabetes belonged to 19 of 41 (46.3%) previously reported genera (derived from Disbiome database), while only 10 (24.4%) showed different abundances between diabetes patients and healthy subjects in genus-based analysis. Our study reveals modularization of gut microbiota as guilds in Chinese populations, and demonstrates advantages of guild-based analysis in identifying diabetes-related gut bacteria. The analytical method based on microbial networks should be widely used to deepen our understanding of the role of gut microbiota in human health.

Supplementary Information

The online version contains supplementary material available at 10.1007/s43657-024-00211-8.

Comammox Nitrospira act as key bacteria in weakly acidic soil via potential cobalamin sharing

The discovery of comammox Nitrospira in low pH environments has reshaped the ammonia oxidation process in acidic settings, providing a plausible explanation for the higher nitrification rates observed in weakly acidic soils. However, the response of comammox Nitrospira to varying pH levels and its ecological role in these environments remains unclear. Here, a survey across soils with varying pH values (ranging from 4.4 to 9.7) was conducted to assess how comammox Nitrospira perform under different pH conditions. Results showed that comammox Nitrospira dominate ammonia oxidation in weakly acidic soils, functioning as a K-strategy species characterized by slow growth and stress tolerance. As a key species in this environment, comammox Nitrospira may promote bacterial cooperation under low pH conditions. Genomic evidence suggested that cobalamin sharing is a potential mechanism, as comammox Nitrospira uniquely encode a metabolic pathway that compensates for cobalamin imbalance in weakly acidic soils, where 86.8% of metagenome-assembled genomes (MAGs) encode cobalamin-dependent genes. Additionally, we used DNA stable-isotope probing (DNA-SIP) to demonstrate its response to pH fluctuations to reflect how it responds to the decrease in pH. Results confirmed that comammox Nitrospira became dominant ammonia oxidizers in the soil after the decrease in pH. We suggested that comammox Nitrospira will become increasingly important in global soils, under the trend of soil acidification. Overall, our work provides insights that how comammox Nitrospira perform in weakly acidic soil and its response to pH changes.

Siderophore synthetase-receptor gene coevolution reveals habitat- and pathogen-specific bacterial iron interaction networks

Bacterial social interactions play crucial roles in various ecological, medical, and biotechnological contexts. However, predicting these interactions from genome sequences is notoriously difficult. Here, we developed bioinformatic tools to predict whether secreted iron-scavenging siderophores stimulate or inhibit the growth of community members. Siderophores are chemically diverse and can be stimulatory or inhibitory depending on whether bacteria have or lack corresponding uptake receptors. We focused on 1928 representative Pseudomonas genomes and developed an experimentally validated coevolution algorithm to match encoded siderophore synthetases to corresponding receptor groups. We derived community-level iron interaction networks to show that siderophore-mediated interactions differ across habitats and lifestyles. Specifically, dense networks of siderophore sharing and competition were observed among environmental and nonpathogenic species, while small, fragmented networks occurred among human-associated and pathogenic species. Together, our sequence-to-ecology approach empowers the analyses of social interactions among thousands of bacterial strains and offers opportunities for targeted intervention to microbial communities.

Warm growing season activates microbial nutrient cycling to promote fertilizer nitrogen uptake by maize

The influence of nitrogen (N) inputs on soil microbial communities and N uptake by plants is well-documented. Seasonal variations further impact these microbial communities and their nutrient-cycling functions, particularly within multiple cropping systems. Nevertheless, the combined effects of N fertilization and growing seasons on soil microbial communities and plant N uptake remain ambiguous, thereby constraining our comprehension of the optimal growing season for maximizing crop production. In this study, we employed 15N isotope labeling, high-throughput sequencing, and quantitative polymerase chain reaction (qPCR) techniques to investigate the effects of two distinct growing seasons on microbial communities and maize 15N uptake ratios (15NUR). Our results showed that the warm growing season (26.6 °C) increased microbial diversity, reduced network complexity but enhanced stability, decreased microbial associations, and increased modularization compared to the cool growing season (23.1 °C). Additionally, the warm growing season favored oligotrophic species and increased the abundance of microbial guilds and functional genes related to N, phosphorus, and sulfur cycling. Furthermore, alterations in the characteristics of soil microbial keystone taxa were closely linked to variations in maize 15NUR. Overall, our findings demonstrate significant seasonal variations in soil microbial diversity and functioning, with maize exhibiting higher 15NUR during the warm growing season of the double cropping system.

Rectal mucosal inflammation, microbiome, and wound healing in men who have sex with men who engage in receptive anal intercourse

Mucosal injury is common during consensual intercourse and induces an inflammatory response that could contribute to pathogen transmission including HIV. Here, we compared mucosal immune and microbiome responses to experimentally induced mucosal injury between men who have sex with men engaging in receptive anal intercourse (MSM-RAI) and men who do not engage in RAI (controls), all without HIV. Rectal mucosal secretions were collected from adult MSM-RAI (n = 19) and controls (n = 6) via anoscopy before and up to eight days after experimentally induced injury. Mucosal healing was evaluated by repeated injury surface area measurements with digital imaging. MSM-RAI demonstrated overall significantly higher concentrations of pro-inflammatory cytokines and a distinct rectal microbiome compared with controls. Wound healing was numerically faster in MSM-RAI but did not meet statistical significance (p = 0.09). Different cytokine injury response patterns were observed between MSM-RAI and controls; however, IL-6 and IP-10 were important mediators in both groups. Microbial guilds, particularly from the Lachnospiraceae and Prevotellaceae families, were associated with rectal mucosal inflammation. This work is the first experimental study of rectal mucosal injury and the immune environment in healthy humans and provides a more nuanced understanding of rectal mucosal inflammation after injury, which can inform our understanding of HIV transmission.

Gestational diabetes-combined excess weight gain exacerbates gut microbiota dysbiosis in newborns, associated with reduced abundance of Clostridium, Coriobacteriaceae, and Collinsella

Background

Existing literature indicates that Gestational diabetes mellitus (GDM) and maternal obesity disrupt the normal colonization of the neonatal gut microbiota alone. Still, the combined impact of GDM and excessive gestational weight gain (EGWG) on this process remains under explored. The association between gestational weight gain before/after GDM diagnosis and neonatal gut microbiota characteristics is also unclear.The purpose of this study is to conduct investigation and analysis on the above-mentioned issues, providing a basis for optimizing clinical management plans.

Methods

This study involved 98 mother-infant pairs categorized into GDM and non-GDM groups. The GDM group was further subdivided based on gestational weight gain (GWG) into normal (GDM+NGWG) and excessive (GDM+EGWG) weight gain groups. Neonatal stool samples were collected within 24 hours post-delivery for gut microbiota profiling through 16S rRNA gene sequencing. Statistical analyses explored correlations between total GWG/BMI gain and those before/after GDM diagnosis (t-GWG/GBG; b-GWG/GBG; a-GWG/GBG) with key bacterial taxa.

Results

Notable genus-level changes included enrichment of Escherichia and Klebsiella, and depletion of Bacteroides, Bifidobacterium, Coprococcus, Ruminococcus among GDM-Total and GDM+EGWG groups compared to non-GDM. Further,LEfSe analysis identified 30 differential bacteria taxa between GDM-Total and healthy control groups, which increased to 38 between GDM+EGWG and non-GDM groups, highlighting more pronounced microbial shifts associated with EGWG. Clostridium was negatively correlated with t-GWG and newborn birth weight; The Coriobacteriaceae showed a negative correlation with t-GWG, t-GBG, and a-GBG. Additionally,Collinsella exhibited negative correlations with t-GBG and a-GBG.

Conclusion

This study has identified that the presence of EGWG in GDM mothers further exacerbated neonatal gut microbial perturbations. Total GWG/GBG and those after the diagnosis of GDM were negatively correlated with the abundance of neonatal gut Clostridium, Coriobacteriaceae, and Collinsella. These findings provide new insights for precise prevention and management of GDM.

Unveiling the hidden impact: How human disturbances threaten aquatic microorganisms in cities

Urban activity emissions have important ecological significance to bacterial communities' spatial and temporal distribution and the mechanism of bacterial community construction. The mechanism of bacterial community construction is the key to community structure and lifestyle, and the influence of this aspect has not been thoroughly studied. This study analyzed the response of bacteria in water and sediment in different seasons to urban activities in Jinsha River. The results showed that the influence of urban activities on bacterial community structure in sediment was greater than that in water. The input of pollution in different regions changed the diversity and abundance of water and sediments bacteria and promoted bacterial community reconstruction to a certain extent. Co-network analysis found that many metal-mediated species are core species within the same module and can be used to mitigate pollution caused by metal or organic pollutants due to interspecific solid interactions. Different potential pollution sources around urban rivers affect the metabolic function of bacteria in aquatic ecosystems and promote the detoxification function of bacteria in different media. The results of this study supplement our understanding of the characteristics of microbial communities in urban river systems and provide clues for understanding the maintenance mechanism of microbial diversity in multi-pollution environments.

Dynamic microbiome diversity shaping the adaptation of sponge holobionts in coastal waters

The microbial communities associated with sponges contribute to the adaptation of hosts to environments, which are essential for the trophic transformation of benthic-marine coupling. However, little is known about the symbiotic microbial community interactions and adaptative strategies of high- and low-microbial abundance (HMA and LMA) sponges, which represent two typical ecological phenotypes. Here, we compared the 1-year dynamic patterns of microbiomes with the HMA sponge Spongia officinalis and two LMA sponge species Tedania sp. and Haliclona simulans widespread on the coast of China. Symbiotic bacterial communities with the characteristic HMA-LMA dichotomy presented higher diversity and stability in S. officinalis than in Tedania sp. and H. simulans, while archaeal communities showed consistent diversity across all sponges throughout the year. Dissolved oxygen, dissolved inorganic phosphorus, dissolved organic phosphorus, and especially temperature were the major factors affecting the seasonal changes in sponge microbial communities. S. officinalis-associated microbiome had higher diversity, stronger stability, and closer interaction, which adopted a relatively isolated strategy to cope with environmental changes, while Tedania sp. and H. simulans were more susceptible and shared more bacterial Amplicon Sequence Variants (ASVs) with surrounding waters, with an open way facing the uncertainty of the environment. Meta-analysis of the microbiome in composition, diversity, and ecological function from 13 marine sponges further supported that bacterial communities associated with HMA and LMA sponges have evolved two distinct environmental adaptation strategies. We propose that the different adaptive ways of sponges responding to the environment may be responsible for their successful evolution and their competence in global ocean change.

IMPORTANCE

During long-term evolution, sponge holobionts, among the oldest symbiotic relationships between microbes and metazoans, developed two distinct phenotypes with high- and low-microbial abundance (HMA and LMA). Despite sporadic studies indicating that the characteristic microbial assemblages present in HMA and LMA sponges, the adaptation strategies of symbionts responding to environments are still unclear. This deficiency limits our understanding of the selection of symbionts and the ecological functions during the evolutionary history and the adaptative assessment of HMA and LMA sponges in variable environments. Here, we explored symbiotic communities with two distinct phenotypes in a 1-year dynamic environment and combined with the meta-analysis of 13 sponges. The different strategies of symbionts in adapting to the environment were basically drawn: microbes with LMA were more acclimated to environmental changes, forming relatively loose-connected communities, while HMA developed relatively tight-connected and more similar communities beyond the divergence of species and geographical location.

Accumulated temperature dictates the regional structural variation of prokaryotic periphyton at soil-water interface in paddy fields

As a pervasive microbial aggregate found at the water-soil interface in paddy fields, periphyton plays crucial roles in modulating nutrient biogeochemical cycling. Consequently, it effectively mitigates non-point source pollution due to its diverse composition. Despite its significance, the mechanisms governing periphyton diversity across different rice planting regions remain poorly understood. To bridge this gap, we investigated periphyton grown in 200 paddy fields spanning 25° of latitude. Initially, we analyzed local diversity and latitudinal variations in prokaryotic communities within paddy field periphyton, identifying 7 abundant taxa, 42 moderate taxa, and 39 rare taxa as the fundamental prokaryotic framework. Subsequently, to elucidate the mechanisms governing periphyton diversity across large scales, we constructed interaction models illustrating triangular relationships among local richness, assembly, and regional variation of prokaryotic subcommunities. Our findings suggest that accumulated temperature-driven environmental filtering partially influences the assembly process of prokaryotes, thereby impacting local species richness and ultimately governing regional structural variations in periphyton. Furthermore, we determined that a latitude of 39° represents the critical threshold maximizing local species richness of periphyton in paddy fields. This study advances our understanding of the factors shaping periphyton geo-imprints and provides valuable insights into predicting their responses to environmental changes, potentially influencing rice production outcomes.

High-altitude environments enhance the ability of Eothenomys miletus to regulate body mass during food limitation, with a focus on gut microorganisms and physiological markers

Animals' digestion, energy metabolism, and immunity are significantly influenced by interactions between the gut microbiota and the intestinal environment of the host. Previous studies have shown that gut microbiota of Eothenomys miletus can respond to environmental changes, high fiber or fat foods. But how E. miletus in high-altitude adapt to their environment through gut microbiota and physiological changes during winter food shortages period was unclear. In the present study, we evaluated the altitude differences in gut microbiota and their interactions with physiology in terms of body mass regulation in order to study the adaptation of the gut microbiota and physiological indicators of the E. miletus under food restriction settings. E. miletus were collected for this study from Jingdong County (JD, low-altitude) and Xianggelila County (XGLL, high-altitude) in Yunnan Province, China, and split into three groups: control group, food-restricted feeding group for 7 days, and re-feeding group was offered a standard diet for 14 days. 16S rRNA gene sequencing and physiological methods were used to analyze the abundance and community structure of gut microbiota, as well as physiological indicators of each group in E. miletus. The results showed that while the RMR changed more during the period of food restriction, the body mass and major organ masses of E. miletus from high-altitude changed less. After food restriction, RMR in XGLL decreased by 25.25%, while that of in JD decreased by 16.54%. E. miletus from the XGLL had gut bacteria that were more abundant in Firmicutes and had fewer OTUs, and the microbiota had a closer interaction with physiological indicators. Moreover, the gut microbiota adapted to the food shortage environment by enhancing the genera of Bacterroides, Ruminococcus, Turicibacter, and Treponema to improve the utilization of nutrient resources. The interactions between microbial species and the equilibrium of energy homeostasis were further impacted by alterations in physiological indicators and microbial community structure. These variations were important for E. miletus to adapt to the fluctuations and changes of food resources in high-altitude region, which also expand our knowledge of organismal adaptations and the mechanisms behind the interactions between gut bacteria and host physiology.

Integrated analysis of microbiome and host transcriptome unveils correlations between lung microbiota and host immunity in bronchoalveolar lavage fluid of pneumocystis pneumonia patients

OBJECTIVE

The lung microbiota of patients with pulmonary diseases is disrupted and impacts the immunity. The microbiological and immune landscape of the lungs in patients with pneumocystis pneumonia (PCP) remains poorly understood.

METHODS

Multi-omics analysis and machine learning were performed on bronchoalveolar lavage fluid to explore interaction between the lung microbiota and host immunity in PCP. Then we constructed a diagnostic model using differential genes with LASSO regression and validated by qPCR. The immune infiltration analysis was performed to explore the landscape of lung immunity in patients with PCP.

RESULTS

Patients with PCP showed a low alpha diversity of lung microbiota, accompanied by the elevated abundance of Firmicutes, and the differential expressed genes (DEGs) analysis displayed a downregulation of MAPK signaling. The MAPK10, TGFB1, and EFNA3 indicated a potential to predict PCP (AUC = 0.86). The lung immune landscape in PCP showed the lower levels of naïve CD4+ T cells and activated dendritic cells. The correlation analysis of the MAPK signaling pathway-related DEGs and the differential microorganisms at the level of phylum showed that the Firmicutes was negatively correlated with these DEGs.

CONCLUSION

We profiled the characteristics of lung microbiota and immune landscape in PCP, which may contribute to elucidating the mechanism of PCP.

Co-Occurrence Patterns Do Not Predict Mutualistic Interactions Between Plant and Butterfly Species

Biotic interactions are crucial for determining the structure and dynamics of communities; however, direct measurement of these interactions can be challenging in terms of time and resources, especially when numerous species are involved. Inferring species interactions from species co-occurrence patterns is increasingly being used; however, recent studies have highlighted some limitations. To our knowledge, no attempt has been made to test the accuracy of the existing methods for detecting mutualistic interactions in terrestrial ecosystems. In this study, we compiled two literature-based, long-term datasets of interactions between butterflies and herbaceous plant species in two regions of Germany and compared them with observational abundance and presence/absence data collected within a year in the same regions. We tested how well the species associations generated by three different co-occurrence analysis methods matched those of empirically measured mutualistic associations using sensitivity and specificity analyses and compared the strength of associations. We also checked whether flower abundance data (instead of plant abundance data) increased the accuracy of the co-occurrence models and validated our results using empirical flower visitation data. The results revealed that, although all methods exhibited low sensitivity, our implementation of the Relative Interaction Intensity index with pairwise null models performed the best, followed by the probabilistic method and Spearman's rank correlation method. However, empirical data showed a significant number of interactions that were not detected using co-occurrence methods. Incorporating flower abundance data did not improve sensitivity but enhanced specificity in one region. Further analysis demonstrated incongruence between the predicted co-occurrence associations and actual interaction strengths, with many pairs exhibiting high interaction strength but low co-occurrence or vice versa. These findings underscore the complexity of ecological dynamics and highlight the limitations of current co-occurrence methods for accurately capturing species interactions.

Temporal microbiome changes in axolotl limb regeneration: Stage-specific restructuring of bacterial and fungal communities with a Flavobacterium bloom during blastema proliferation

The intricate relationship between regeneration and microbiota has recently gained attention, spanning diverse model organisms. Axolotl (Ambystoma mexicanum) is a critically endangered salamander species and a model organism for regenerative and developmental biology. Despite its significance, a noticeable gap exists in understanding the interplay between axolotl regeneration and its microbiome. Here, we analyse in depth bacterial 16S rRNA amplicon dataset that we reported before as data resource and profile fungal community by sequencing ITS amplicons at the critical stages of limb regeneration (0-1-4-7-30-60 days post amputation, 'dpa'). Results reveal a decline in richness and evenness in the course of limb regeneration, with bacterial community richness recovering beyond 30 dpa unlike fungi community. Beta diversity analysis reveals precise restructuring of the bacterial community along the three phases of limb regeneration, contrasting with less congruent changes in the fungal community. Temporal dynamics of the bacterial community highlight prevalent anaerobic bacteria in initiation phase and Flavobacterium bloom in the early phase correlating with limb blastema proliferation. Predicted functional analysis mirrors these shifts, emphasising a transition from amino acid metabolism to lipid metabolism control. Fungal communities shift from Blastomycota to Ascomycota dominance in the late regeneration stage. Our findings provide ecologically relevant insights into stage specific role of microbiome contributions to axolotl limb regeneration.

Metabarcoding reveals ecologically distinct fungal assemblages in river and groundwater along an Austrian alpine to lowland gradient

Biodiversity, the source of origin, and ecological roles of fungi in groundwater are to this day a largely neglected field in fungal and freshwater ecology. We used DNA-based Illumina high-throughput sequence analysis of both fungal gene markers 5.8S and internal transcribed spacers region 2 (ITS2), improving taxonomic classification. This study focused on the groundwater and river mycobiome along an altitudinal and longitudinal transect of a pre-alpine valley in Austria in two seasons. Using Bayesian network modeling approaches, we identified patterns in fungal community assemblages that were mostly shaped by differences in landscape (climatic, topological, and geological) and environmental conditions. While river fungi were comparatively more diverse, unique fungal assemblages could be recovered from groundwater, including typical aquatic lineages such as Rozellomycota and Olpidiomycota. The most specious assemblages in groundwater were not linked to the input of organic material from the surface, and as such, seem to be sustained by characteristic groundwater conditions. Based on what is known from closely related fungi, our results suggest that the present fungal communities potentially contribute to mineral weathering, carbon cycling, and denitrification in groundwater. Furthermore, we were able to observe the effects of varying land cover due to agricultural practices on fungal biodiversity in groundwater ecosystems. This study contributes to improving our understanding of fungi in the subsurface aquatic biogeosphere.

SPLANG-a synthetic poisson-lognormal-based abundance and network generative model for microbial interaction inference algorithms

Microbes are pervasive and their interaction with each other and the environment can impact fields as diverse as health and agriculture. While network inference and related algorithms that use abundance data from pyrosequencing can infer microbial interaction networks, the ambiguity surrounding the actual underlying networks hampers the validation of these algorithms. This study introduces a generative model to synthesize both the underlying interactive network and observable abundance data, serving as a test bed for the existing and future network inference algorithms. We tested our generative model with four typical network inference algorithms; our results suggest that none of these algorithms demonstrate adequate accuracy for inferring ecologies of non-commensalistic species, either mutualistic or competitive. We further explored the potential for predictability by combining existing algorithms with an oracle algorithm built by fusing the results of several existing algorithms. The oracle algorithm reveals promising improvements in predictability, although it falls short when applied to networks characterized by dense interspecies taxa interactions. Our work underscores the need for the continued development and validation of algorithms to unravel the intricacies of microbial interaction networks.

Unfolding and de-confounding: biologically meaningful causal inference from longitudinal multi-omic networks using METALICA

A key challenge in the analysis of microbiome data is the integration of multi-omic datasets and the discovery of interactions between microbial taxa, their expressed genes, and the metabolites they consume and/or produce. In an effort to improve the state of the art in inferring biologically meaningful multi-omic interactions, we sought to address some of the most fundamental issues in causal inference from longitudinal multi-omics microbiome data sets. We developed METALICA, a suite of tools and techniques that can infer interactions between microbiome entities. METALICA introduces novel unrolling and de-confounding techniques used to uncover multi-omic entities that are believed to act as confounders for some of the relationships that may be inferred using standard causal inferencing tools. The results lend support to predictions about biological models and processes by which microbial taxa interact with each other in a microbiome. The unrolling process helps identify putative intermediaries (genes and/or metabolites) to explain the interactions between microbes; the de-confounding process identifies putative common causes that may lead to spurious relationships to be inferred. METALICA was applied to the networks inferred by existing causal discovery, and network inference algorithms were applied to a multi-omics data set resulting from a longitudinal study of IBD microbiomes. The most significant unrollings and de-confoundings were manually validated using the existing literature and databases.

IMPORTANCE

We have developed a suite of tools and techniques capable of inferring interactions between microbiome entities. METALICA introduces novel techniques called unrolling and de-confounding that are employed to uncover multi-omic entities considered to be confounders for some of the relationships that may be inferred using standard causal inferencing tools. To evaluate our method, we conducted tests on the inflammatory bowel disease (IBD) dataset from the iHMP longitudinal study, which we pre-processed in accordance with our previous work. From this dataset, we generated various subsets, encompassing different combinations of metagenomics, metabolomics, and metatranscriptomics datasets. Using these multi-omics datasets, we demonstrate how the unrolling process aids in the identification of putative intermediaries (genes and/or metabolites) to explain the interactions between microbes. Additionally, the de-confounding process identifies potential common causes that may give rise to spurious relationships to be inferred. The most significant unrollings and de-confoundings were manually validated using the existing literature and databases.

Bacterial and Symbiodiniaceae communities' variation in corals with distinct traits and geographical distribution

Coral microbiomes play crucial roles in holobiont homeostasis and adaptation. The host's ability to populate broad ecological niches and to cope with environmental changes seems to be related to the flexibility of the coral microbiome. By means of high-throughput DNA sequencing we characterized simultaneously both bacterial (16S rRNA) and Symbiodiniaceae (ITS2) communities of four reef-building coral species (Mussismilia braziliensis, Mussismilia harttii, Montastraea cavernosa, and Favia gravida) that differ in geographic distribution and niche specificity. Samples were collected in a marginal reef system (Abrolhos, Brazil) in four sites of contrasting irradiance and turbidity. Biological filters governed by the host are important in shaping corals' microbiome structure. More structured associated microbial communities by reef site tend to occur in coral species with broader geographic and depth ranges, especially for Symbiodiniaceae, whereas the endemic and habitat-specialist host, M. braziliensis, has relatively more homogenous bacterial communities with more exclusive members. Our findings lend credence to the hypothesis that higher microbiome flexibility renders corals more adaptable to diverse environments, a trend that should be investigated in more hosts and reef areas.

Feature sequence-based genome mining uncovers the hidden diversity of bacterial siderophore pathways

Microbial secondary metabolites are a rich source for pharmaceutical discoveries and play crucial ecological functions. While tools exist to identify secondary metabolite clusters in genomes, precise sequence-to-function mapping remains challenging because neither function nor substrate specificity of biosynthesis enzymes can accurately be predicted. Here, we developed a knowledge-guided bioinformatic pipeline to solve these issues. We analyzed 1928 genomes of Pseudomonas bacteria and focused on iron-scavenging pyoverdines as model metabolites. Our pipeline predicted 188 chemically different pyoverdines with nearly 100% structural accuracy and the presence of 94 distinct receptor groups required for the uptake of iron-loaded pyoverdines. Our pipeline unveils an enormous yet overlooked diversity of siderophores (151 new structures) and receptors (91 new groups). Our approach, combining feature sequence with phylogenetic approaches, is extendable to other metabolites and microbial genera, and thus emerges as powerful tool to reconstruct bacterial secondary metabolism pathways based on sequence data.

Networks as tools for defining emergent properties of microbiomes and their stability

The potential promise of the microbiome to ameliorate a wide range of societal and ecological challenges, from disease prevention and treatment to the restoration of entire ecosystems, hinges not only on microbiome engineering but also on the stability of beneficial microbiomes. Yet the properties of microbiome stability remain elusive and challenging to discern due to the complexity of interactions and often intractable diversity within these communities of bacteria, archaea, fungi, and other microeukaryotes. Networks are powerful tools for the study of complex microbiomes, with the potential to elucidate structural patterns of stable communities and generate testable hypotheses for experimental validation. However, the implementation of these analyses introduces a cascade of dichotomies and decision trees due to the lack of consensus on best practices. Here, we provide a road map for network-based microbiome studies with an emphasis on discerning properties of stability. We identify important considerations for data preparation, network construction, and interpretation of network properties. We also highlight remaining limitations and outstanding needs for this field. This review also serves to clarify the varying schools of thought on the application of network theory for microbiome studies and to identify practices that enhance the reproducibility and validity of future work. Video Abstract.

The Association of the Oral Microbiota with Cognitive Functioning in Adolescence

Background: A growing body of research supports the role of the microbial communities residing in the digestive system in the host's cognitive functioning. Most of these studies have been focused on the gut microbiome and its association with clinical phenotypes in middle-aged and older adults. There is an insufficiency of population-based research exploring the association of normative cognitive functioning with the microbiome particularly with the oral microbiota. Methods: In this study, using metagenomics and metabolomics, we characterized the salivary microbiome diversity in a sample of 51 males of Hispanic and African American origin aged 12-18 years and explored the associations between the microbiome and the youths' cognitive performance captured with the Kaufman Assessment Battery for Children II (KABC-II). Results: Several bacterial species of the oral microbiota and related metabolic pathways were associated with cognitive function. In particular, we found negative associations between indicators of general intelligence and the relative abundance of Bacteroidetes and Lachnospiraceae and positive associations with Bifidobacteriaceae and Prevotella histicola sp. Among metabolic pathways, the super pathways related to bacterial cell division and GABA metabolism were linked to cognitive function. Conclusions: The results of our work are consistent with the literature reporting on the association between microbiota and cognitive function and support further population work to elucidate the potential for a healthy oral microbiome to improve cognitive health.

Metabolic interdependencies in thermophilic communities are revealed using co-occurrence and complementarity networks

Microbial communities exhibit intricate interactions underpinned by metabolic dependencies. To elucidate these dependencies, we present a workflow utilizing random matrix theory on metagenome-assembled genomes to construct co-occurrence and metabolic complementarity networks. We apply this approach to a temperature gradient hot spring, unraveling the interplay between thermal stress and metabolic cooperation. Our analysis reveals an increase in the frequency of metabolic interactions with rising temperatures. Amino acids, coenzyme A derivatives, and carbohydrates emerge as key exchange metabolites, forming the foundation for syntrophic dependencies, in which commensalistic interactions take a greater proportion than mutualistic ones. These metabolic exchanges are most prevalent between phylogenetically distant species, especially archaea-bacteria collaborations, as a crucial adaptation to harsh environments. Furthermore, we identify a significant positive correlation between basal metabolite exchange and genome size disparity, potentially signifying a means for streamlined genomes to leverage cooperation with metabolically richer partners. This phenomenon is also confirmed by another composting system which has a similar wide range of temperature fluctuations. Our workflow provides a feasible way to decipher the metabolic complementarity mechanisms underlying microbial interactions, and our findings suggested environmental stress regulates the cooperative strategies of thermophiles, while these dependencies have been potentially hardwired into their genomes during co-evolutions.

Exploring Multifunctional Markers of Biological Age in Farmed Gilthead Sea Bream (Sparus aurata): A Transcriptomic and Epigenetic Interplay for an Improved Fish Welfare Assessment Approach

DNA methylation clocks provide information not only about chronological but also biological age, offering a high-resolution and precise understanding of age-related pathology and physiology. Attempts based on transcriptomic and epigenetic approaches arise as integrative biomarkers linking the quantification of stress responses with specific fitness traits and may help identify biological age markers, which are also considered welfare indicators. In gilthead sea bream, targeted gene expression and DNA methylation analyses in white skeletal muscle proved sirt1 as a reliable marker of age-mediated changes in energy metabolism. To complete the list of welfare auditing biomarkers, wide analyses of gene expression and DNA methylation in one- and three-year-old fish were combined. After discriminant analysis, 668 differentially expressed transcripts were matched with those containing differentially methylated (DM) regions (14,366), and 172 were overlapping. Through enrichment analyses and selection, two sets of genes were retained: 33 showing an opposite trend for DNA methylation and expression, and 57 down-regulated and hypo-methylated. The first set displayed an apparently more reproducible and reliable pattern and 10 multifunctional genes with DM CpG in regulatory regions (sirt1, smad1, ramp1, psmd2-up-regulated; col5a1, calcrl, bmp1, thrb, spred2, atp1a2-down-regulated) were deemed candidate biological age markers for improved welfare auditing in gilthead sea bream.

A Novel Slope-Matrix-Graph Algorithm to Analyze Compositional Microbiome Data

Networks are widely used to represent relationships between objects, including microorganisms within ecosystems, based on high-throughput sequencing data. However, challenges arise with appropriate statistical algorithms, handling of rare taxa, excess zeros in compositional data, and interpretation. This work introduces a novel Slope-Matrix-Graph (SMG) algorithm to identify microbiome correlations primarily based on slope-based distance calculations. SMG effectively handles any proportion of zeros in compositional data and involves: (1) searching for correlated relationships (e.g., positive and negative directions of changes) based on a "target of interest" within a setting, and (2) quantifying graph changes via slope-based distances between objects. Evaluations on simulated datasets demonstrated SMG's ability to accurately cluster microbes into distinct positive/negative correlation groups, outperforming methods like Bray-Curtis and SparCC in both sensitivity and specificity. Moreover, SMG demonstrated superior accuracy in detecting differential abundance (DA) compared to ZicoSeq and ANCOM-BC2, making it a robust tool for microbiome analysis. A key advantage is SMG's natural capacity to analyze zero-inflated compositional data without transformations. Overall, this simple yet powerful algorithm holds promise for diverse microbiome analysis applications.

Disentangling the factors defining Bacillus subtilis group species abundance in natural soils

Bacillus subtilis is ubiquitously and broadly distributed in various environments but is mostly isolated from soil. Given that B. subtilis is known as a plant growth-promoting rhizobacterium in agriculture, we aimed to describe the natural distribution of this species and uncover how biotic and abiotic factors affect its distribution. When comparing different soils, we discovered that B. subtilis group species are most abundant in grasslands but can rarely be isolated from forest soil, even if the soil sample sites are situated in proximity. Differential analysis revealed that spore-forming bacteria exhibited enrichments in the grassland, suggesting niche overlap or synergistic interactions leading to the proliferation of certain Bacillus species in grassland environments. Network analysis further revealed that Bacillus and other Bacillota established a densely interconnected hub module in the grassland, characterised by positive associations indicating co-occurrence, a pattern not observed in the forest soil. Speculating that this difference was driven by abiotic factors, we combined amplicon sequencing with physico-chemical analysis of soil samples and found multiple chemical variables, mainly pH, to affect microbial composition. Our study pinpoints the factors that influence B. subtilis abundance in natural soils and, therefore, offers insights for designing B. subtilis-based biocontrol products in agricultural settings.

New insights into biologic interpretation of bioinformatic pipelines for fish eDNA metabarcoding: A case study in Pearl River estuary

Environmental DNA (eDNA) metabarcoding is an emerging tool for monitoring biological communities in aquatic ecosystems. The selection of bioinformatic pipelines significantly impacts the results of biodiversity assessments. However, there is currently no consensus on the appropriate bioinformatic pipelines for fish community analysis in eDNA metabarcoding. In this study, we compared three bioinformatic pipelines (Uparse, DADA2, and UNOISE3) using real and mock (constructed with 15/30 known fish) communities to investigate the differences in biological interpretation during the data analysis process in eDNA metabarcoding. Performance evaluation and diversity analyses revealed that the choice of bioinformatic pipeline could impact the biological results of metabarcoding experiments. Among the three pipelines, the operational taxonomic units (OTU)-based pipeline (Uparse) showed the best performance (sensitivity: 0.6250 ± 0.0166; compositional similarity: 0.4000 ± 0.0571), the highest richness (25-102) and minimal inter-group differences in alpha diversity. It suggested the OTU-based pipeline possessed superior capability in fish diversity monitoring compared to ASV/ZOTU-based pipeline. Additionally, the Bray-Curtis distance matrix achieved the highest discriminative effect in the PCoA (43.3%-53.89%) and inter-group analysis (P < 0.01), indicating it was better at distinguishing compositional differences or specific genera of fish community at different sampling sites than other distance matrices. These findings provide new insights into fish community monitoring through eDNA metabarcoding in estuarine environments.

Temporal shifts in the phytoplankton network in a large eutrophic shallow freshwater lake subjected to major environmental changes due to human interventions

Phytoplankton communities are crucial components of aquatic ecosystems, and since they are highly interactive, they always form complex networks. Yet, our understanding of how interactive phytoplankton networks vary through time under changing environmental conditions is limited. Using a 29-year (339 months) long-term dataset on Lake Taihu, China, we constructed a temporal network comprising monthly sub-networks using "extended Local Similarity Analysis" and assessed how eutrophication, climate change, and restoration efforts influenced the temporal dynamics of network complexity and stability. The network architecture of phytoplankton showed strong dynamic changes with varying environments. Our results revealed cascading effects of eutrophication and climate change on phytoplankton network stability via changes in network complexity. The network stability of phytoplankton increased with average degree, modularity, and nestedness and decreased with connectance. Eutrophication (increasing nitrogen) stabilized the phytoplankton network, mainly by increasing its average degree, while climate change, i.e., warming and decreasing wind speed enhanced its stability by increasing the cohesion of phytoplankton communities directly and by decreasing the connectance of network indirectly. A remarkable shift and a major decrease in the temporal dynamics of phytoplankton network complexity (average degree, nestedness) and stability (robustness, persistence) were detected after 2007 when numerous eutrophication mitigation efforts (not all successful) were implemented, leading to simplified phytoplankton networks and reduced stability. Our findings provide new insights into the organization of phytoplankton networks under eutrophication (or re-oligotrophication) and climate change in subtropical shallow lakes.

A comparative analysis of mutual information methods for pairwise relationship detection in metagenomic data

BACKGROUND

Construction of co-occurrence networks in metagenomic data often employs correlation to infer pairwise relationships between microbes. However, biological systems are complex and often display qualities non-linear in nature. Therefore, the reliance on correlation alone may overlook important relationships and fail to capture the full breadth of intricacies presented in underlying interaction networks. It is of interest to incorporate metrics that are not only robust in detecting linear relationships, but non-linear ones as well.

RESULTS

In this paper, we explore the use of various mutual information (MI) estimation approaches for quantifying pairwise relationships in biological data and compare their performances against two traditional measures-Pearson's correlation coefficient, r, and Spearman's rank correlation coefficient, ρ. Metrics are tested on both simulated data designed to mimic pairwise relationships that may be found in ecological systems and real data from a previous study on C. diff infection. The results demonstrate that, in the case of asymmetric relationships, mutual information estimators can provide better detection ability than Pearson's or Spearman's correlation coefficients. Specifically, we find that these estimators have elevated performances in the detection of exploitative relationships, demonstrating the potential benefit of including them in future metagenomic studies.

CONCLUSIONS

Mutual information (MI) can uncover complex pairwise relationships in biological data that may be missed by traditional measures of association. The inclusion of such relationships when constructing co-occurrence networks can result in a more comprehensive analysis than the use of correlation alone.

Mulberry polyphenols restored both small and large intestinal microflora in db/db mice, potentially alleviating type 2 diabetes

Polyphenols in mulberry fruit have potential anti-diabetic effects by targeting the gut microbiota. This study investigated how mulberry polyphenols (MPs) influence the microbiota of the small and large intestines and their effects on type 2 diabetes symptoms. The results showed lower microbiota densities in the small intestine. MP treatments improved microbiota richness and diversity in both intestines, similar to metformin. In particular, at a 400 mg kg-1 dose, mulberry polyphenols decreased Firmicutes, Lactobacillus, and Bacilli, while increasing Bacteroidetes, leading to elevated propionate and butyrate levels. Less abundant small intestinal microbiota, like Enterobacterales, Mycoplasmatales, Enterobacteriaceae, and Ureaplasma, were involved in regulating blood glucose and insulin levels. Functional analysis suggested that mulberry polyphenols reshaped the small intestinal microbiota to influence blood glucose balance via unknown pathways, while in the large intestine, they primarily affected blood glucose through carbohydrate transport and metabolism. Based on their ability to regulate the composition of intestinal flora, MPs likely improved glucose homeostasis by enhancing glucose utilization, supporting pancreatic tissue health, and increasing serum antioxidant capacity. However, the specific mechanisms underlying this potential are yet to be fully explored. This study provides new insights into the influence of MPs on remodeling the microbiota residing in both the small and large intestines, which thereby may contribute to the improvement of the pathophysiology of type 2 diabetes.

Elucidating the dynamics and impact of the gut microbiome on maternal nutritional status during pregnancy, effect on pregnancy outcomes and infant health in rural Pakistan: study protocol for a prospective, longitudinal observational study

INTRODUCTION

Undernutrition during pregnancy is linked to adverse pregnancy and birth outcomes and has downstream effects on the growth and development of children. The gut microbiome has a profound influence on the nutritional status of the host. This phenomenon is understudied in settings with a high prevalence of undernutrition, and further investigation is warranted to better understand such interactions.

METHODS AND ANALYSIS

This is a prospective, longitudinal observational study to investigate the relationship between prokaryotic and eukaryotic microbes in the gut and their association with maternal body mass index (BMI), gestational weight gain, and birth and infant outcomes among young mothers (17-24 years) in Matiari District, Pakistan. We aim to enrol 400 pregnant women with low and normal BMIs at the time of recruitment (<16 weeks of gestation). To determine the weight gain during pregnancy, maternal weight is measured in the first and third trimesters. Gut microbiome dynamics (bacterial and eukaryotic) will be assessed using 16S and 18S rDNA surveys applied to the maternal stool samples. Birth outcomes include birth weight, small for gestational age, large for gestational age, preterm birth and mortality. Infant growth and nutritional parameters include WHO z-scores for weight, length and head circumference at birth through infancy. To determine the impact of the maternal microbiome, including exposure to pathogens and parasites on the development of the infant microbiome, we will analyse maternal and infant microbiome composition, micronutrients in serum using metallomics (eg, zinc, magnesium and selenium) and macronutrients in the stool. Metatranscriptomics metabolomics and markers of inflammation will be selectively deployed on stool samples to see the variations in dietary intake and maternal nutritional status. We will also use animal models to explore the bacterial and eukaryotic components of the microbiome.

ETHICS AND DISSEMINATION

The study is approved by the National Bioethics Committee (NBC) in Pakistan, the Ethics Review Committee (ERC) at Aga Khan University and the Research Ethics Board (REB) at the Hospital for Sick Children, and findings will be published in peer-reviewed journals.

TRIAL REGISTRATION NUMBER

NCT05108675.

A rigorous and versatile statistical test for correlations between stationary time series

In disciplines from biology to climate science, a routine task is to compute a correlation between a pair of time series and determine whether the correlation is statistically significant (i.e., unlikely under the null hypothesis that the time series are independent). This problem is challenging because time series typically exhibit autocorrelation and thus cannot be properly analyzed with the standard iid-oriented statistical tests. Although there are well-known parametric tests for time series, these are designed for linear correlation statistics and thus not suitable for the increasingly popular nonlinear correlation statistics. There are also nonparametric tests that can be used with any correlation statistic, but for these, the conditions that guarantee correct false positive rates are either restrictive or unclear. Here, we describe the truncated time-shift (TTS) test, a nonparametric procedure to test for dependence between 2 time series. We prove that this test correctly controls the false positive rate as long as one of the time series is stationary, a minimally restrictive requirement among current tests. The TTS test is versatile because it can be used with any correlation statistic. Using synthetic data, we demonstrate that this test performs correctly even while other tests suffer high false positive rates. In simulation examples, simple guidelines for parameter choices allow high statistical power to be achieved with sufficient data. We apply the test to datasets from climatology, animal behavior, and microbiome science, verifying previously discovered dependence relationships and detecting additional relationships.

Enhanced nutrient supply promotes mutualistic interactions between cyanobacteria and bacteria in oligotrophic ocean

Cyanobacteria can form complex interactions with heterotrophic microorganisms, but this relationship is susceptible to nutrient concentrations. Disentangling the cyanobacteria-bacteria interactions in relation to nutrient supply is essential to understanding their roles in geochemical cycles under global change. We hypothesize that enhanced nutrient supply in oligotrophic oceans can promote interactions among cyanobacteria and bacteria. Therefore, we investigated the planktonic bacteria and their interactions with cyanobacteria in relation to elevated nutrients caused by enhanced upwelling around a shallow and a deep seamount in the tropical western Pacific Ocean. We found obviously higher complexity of network occurred with significantly more cyanobacteria in the deep chlorophyll maximum layer of the shallow seamount when compared with that of the deep seamount. Cyanobacteria can shape bacterial interaction and community evenness in response to relatively high nutrient concentrations. The effects of the nutrients on cyanobacteria-related networks were further estimated based on the Tara Oceans data. Statistical analyses further showed a facilitative effect of nitrate concentrations on cyanobacteria-bacteria mutualistic interactions in the global oligotrophic ocean. By analysing the Tara Ocean macrogenomic data, we detected functional genes related to cyanobacteria-bacteria interactions in all samples, indicating the existence of a mutualistic relationship. Our results reveal cyanobacteria-bacteria interaction in response to nutrient elevation in oligotrophic ocean and highlight the potentially negative effects of global change on the bacterial community from the view of the bio-interaction.

Large scale exploration reveals rare taxa crucially shape microbial assembly in alkaline lake sediments

Alkaline lakes are extreme environments inhabited by diverse microbial extremophiles. However, large-scale distribution patterns, environmental adaptations, community assembly, and evolutionary dynamics of microbial communities remain largely underexplored. This study investigated the characteristics of microbial communities on rare and abundant taxa in alkaline lake sediments in west and northwest China. We observed that abundant taxa varied significantly with geographical distance, while rare taxa remained unaffected by regional differences. The assembly process of abundant taxa was influenced by dispersal limitation, whilst rare taxa were predominantly driven by heterogeneous selection. Network analysis indicated that rare taxa as core species for community interactions and community stability. Rare taxa exhibited higher speciation and transition rate than abundant taxa, serving as a genetic reservoir and potential candidates to become abundance taxa, highlighting their crucial role in maintaining microbial diversity. These insights underscore the significant influence of rare taxa on ecosystem biodiversity and stability in alkaline lakes.

Metagenomics analysis reveals effects of salinity fluctuation on diversity and ecological functions of high and low nucleic acid content bacteria

Salinity is a critical environmental factor in marine ecosystems and has complex and wide-ranging biological effects. However, the effects of changing salinity on diversity and ecological functions of high nucleic acid (HNA) and low nucleic acid (LNA) bacteria are not well understood. In this study, we used 16S rRNA sequencing and metagenomic sequencing analysis to reveal the response of HNA and LNA bacterial communities and their ecological functions to salinity, which was decreased from 26 ‰ to 16 ‰. The results showed that salinity changes had significant effects on the community composition of HNA and LNA bacteria. Among LNA bacteria, 14 classes showed a significant correlation between relative abundance and salinity. Salinity changes can lead to the transfer of some bacteria from HNA bacteria to LNA bacteria. In the network topology relationship, the complexity of the network between HNA and LNA bacterial communities gradually decreased with decreased salinity. The abundance of some carbon and nitrogen cycling genes in HNA and LNA bacteria varied with salinity. Overall, this study demonstrates the effects of salinity on diversity and ecological functions and suggests the importance of salinity in regulating HNA and LNA bacterial communities and functions.

Genetic architecture and correlations between the gut microbiome and gut gene transcription in Chinook salmon (Oncorhynchus tshawytscha)

Population divergence through selection can drive local adaptation in natural populations which has implications for the effective restoration of declining and extirpated populations. However, adaptation to local environmental conditions is complicated when both the host and its associated microbiomes must respond via co-evolutionary change. Nevertheless, for adaptation to occur through selection, variation in both host and microbiome traits should include additive genetic effects. Here we focus on host immune function and quantify factors affecting variation in gut immune gene transcription and gut bacterial community composition in early life-stage Chinook salmon (Oncorhynchus tshawytscha). Specifically, we utilized a replicated factorial breeding design to determine the genetic architecture (sire, dam and sire-by-dam interaction) of gut immune gene transcription and microbiome composition. Furthermore, we explored correlations between host gut gene transcription and microbiota composition. Gene transcription was quantified using nanofluidic qPCR arrays (22 target genes) and microbiota composition using 16 S rRNA gene (V5-V6) amplicon sequencing. We discovered limited but significant genetic architecture in gut microbiota composition and transcriptional profiles. We also identified significant correlations between gut gene transcription and microbiota composition, highlighting potential mechanisms for functional interactions between the two. Overall, this study provides support for the co-evolution of host immune function and their gut microbiota in Chinook salmon, a species recognized as locally adapted. Thus, the inclusion of immune gene transcription profile and gut microbiome composition as factors in the development of conservation and commercial rearing practices may provide new and more effective approaches to captive rearing.

Integration of metataxonomic data sets into microbial association networks highlights shared bacterial community dynamics in fermented vegetables

The management of food fermentation is still largely based on empirical knowledge, as the dynamics of microbial communities and the underlying metabolic networks that produce safe and nutritious products remain beyond our understanding. Although these closed ecosystems contain relatively few taxa, they have not yet been thoroughly characterized with respect to how their microbial communities interact and dynamically evolve. However, with the increased availability of metataxonomic data sets on different fermented vegetables, it is now possible to gain a comprehensive understanding of the microbial relationships that structure plant fermentation. In this study, we applied a network-based approach to the integration of public metataxonomic 16S data sets targeting different fermented vegetables throughout time. Specifically, we aimed to explore, compare, and combine public 16S data sets to identify shared associations between amplicon sequence variants (ASVs) obtained from independent studies. The workflow includes steps for searching and selecting public time-series data sets and constructing association networks of ASVs based on co-abundance metrics. Networks for individual data sets are then integrated into a core network, highlighting significant associations. Microbial communities are identified based on the comparison and clustering of ASV networks using the "stochastic block model" method. When we applied this method to 10 public data sets (including a total of 931 samples) targeting five varieties of vegetables with different sampling times, we found that it was able to shed light on the dynamics of vegetable fermentation by characterizing the processes of community succession among different bacterial assemblages.

IMPORTANCE

Within the growing body of research on the bacterial communities involved in the fermentation of vegetables, there is particular interest in discovering the species or consortia that drive different fermentation steps. This integrative analysis demonstrates that the reuse and integration of public microbiome data sets can provide new insights into a little-known biotope. Our most important finding is the recurrent but transient appearance, at the beginning of vegetable fermentation, of amplicon sequence variants (ASVs) belonging to Enterobacterales and their associations with ASVs belonging to Lactobacillales. These findings could be applied to the design of new fermented products.

Bacteria undergo significant shifts while archaea maintain stability in Pocillopora damicornis under sustained heat stress

Global warming reportedly poses a critical risk to coral reef ecosystems. Bacteria and archaea are crucial components of the coral holobiont. The response of archaea associated with warming is less well understood than that of the bacterial community in corals. Also, there have been few studies on the dynamics of the microbial community in the coral holobiont under long-term heat stress. In order to track the dynamic alternations in the microbial communities within the heat-stressed coral holobiont, three-week heat-stress monitoring was carried out on the coral Pocillopora damicornis. The findings demonstrate that the corals were stressed at 32 °C, and showed a gradual decrease in Symbiodiniaceae density with increasing duration of heat stress. The archaeal community in the coral holobiont remained relatively unaltered by the increasing temperature, whereas the bacterial community was considerably altered. Sustained heat stress exacerbated the dissimilarities among parallel samples of the bacterial community, confirming the Anna Karenina Principle in animal microbiomes. Heat stress leads to more complex and unstable microbial networks, characterized by an increased average degree and decreased modularity, respectively. With the extension of heat stress duration, the relative abundances of the gene (nifH) and genus (Tistlia) associated with nitrogen fixation increased in coral samples, as well as the potential pathogenic bacteria (Flavobacteriales) and opportunistic bacteria (Bacteroides). Hence, our findings suggest that coral hosts might recruit nitrogen-fixing bacteria during the initial stages of suffering heat stress. An environment that is conducive to the colonization and development of opportunistic and pathogenic bacteria when the coral host becomes more susceptible as heat stress duration increases.

Pathological image profiling identifies onco-microbial, tumor immune microenvironment, and prognostic subtypes of colorectal cancer

Histology slide, tissue microbes, and the host gene expression can be independent prognostic factors of colorectal cancer (CRC), but the underlying associations and biological significance of these multimodal omics remain unknown. Here, we comprehensively profiled the matched pathological images, intratumoral microbes, and host gene expression characteristics in 527 patients with CRC. By clustering these patients based on histology slide features, we classified the patients into two histology slide subtypes (HSS). Onco-microbial community and tumor immune microenvironment (TIME) were also significantly different between the two subtypes (HSS1 and HSS2) of patients. Furthermore, variation in intratumoral microbes-host interaction was associated with the prognostic heterogeneity between HSS1 and HSS2. This study proposes a new CRC classification based on pathological image features and elucidates the process by which tumor microbes-host interactions are reflected in pathological images through the TIME.

Identification of keystone taxa in rhizosphere microbial communities using different methods and their effects on compounds of the host Cinnamomum migao

Exploring keystone taxa affecting microbial community stability and host function is crucial for understanding ecosystem functions. However, identifying keystone taxa from humongous microbial communities remains challenging. We collected 344 rhizosphere and bulk soil samples from the endangered plant C. migao for 2 years consecutively. Used high-throughput sequencing 16S rDNA and ITS to obtain the composition of bacterial and fungal communities. We explored keystone taxa and the applicability and limitations of five methods (SPEC-OCCU, Zi-Pi, Subnetwork, Betweenness, and Module), as well as the impact of microbial community domain, time series, and rhizosphere boundary on the identification of keystone taxa in the communities. Our results showed that the five methods, identified abundant keystone taxa in rhizosphere and bulk soil microbial communities. However, the keystone taxa shared by the rhizosphere and bulk soil microbial communities over time decreased rapidly decrease in the five methods. Among five methods on the identification of keystone taxa in the rhizosphere community, Module identified 113 taxa, SPEC-OCCU identified 17 taxa, Betweenness identified 3 taxa, Subnetwork identified 3 taxa, and Zi-Pi identified 4 taxa. The keystone taxa are mainly conditionally rare taxa, and their ecological functions include chemoheterotrophy, aerobic chemoheterotrophy, nitrate reduction, and anaerobic photoautotrophy. The results of the random forest model and structural equation model predict that keystone taxa Mortierella and Ellin6513 may have an effects on the accumulation of 1, 4, 7, - Cycloundecatriene, 1, 5, 9, 9-tetramethyl-, Z, Z, Z-, beta-copaene, bicyclogermacrene, 1,8-Cineole in C. migao fruits, but their effects still need further evidence. Our study evidence an unstable microbial community in the bulk soil, and the definition of microbial boundary and ecologically functional affected the identification of keystone taxa in the community. Subnetwork and Module are more in line with the definition of keystone taxa in microbial ecosystems in terms of maintaining community stability and hosting function.

Differences in archaeal diversity and potential ecological functions between saline and hypersaline lakes on Qinghai-Tibet Plateau were driven by multiple environmental and non-environmental factors beyond the salinity

BACKGROUND

Saline lakes are home to various archaea that play special and crucial roles in the global biogeochemical cycle. The Qinghai-Tibet Plateau hosts a large number of lakes with diverse salinity ranging from 0.1 to over 400 g/L, harboring complex and diverse archaea. To the best of our knowledge, the formation mechanisms and potential ecological roles of archaea in Qinghai-Tibetan Plateau saline lakes remain largely unknown.

RESULTS

Using High-throughput Illumina sequencing, we uncovered the vastly distinct archaea communities between two typical saline lakes with significant salinity differences on the Qinghai Tibet Plateau (Qinghai saline lake and Chaka hypersaline lake) and suggested archaea played different important roles in methanogenesis-related and nitrate reduction-related functions of these two lakes, respectively. Rather than the individual effect of salinity, the composite effect of salinity with diverse environmental parameters (e.g., temperature, chlorophyll a, total nitrogen, and total phosphorus) dominated the explanation of the variations in archaeal community structure in different habitats. Based on the network analysis, we further found the correlations between dominant archaeal OTUs were tight but significantly different between the two habitats, implying that archaeal interactions may also largely determine the shape of archaeal communities.

CONCLUSION

The present study improved our understanding of the structure and function of archaea in different saline lakes on the Qinghai-Tibet Plateau and provided a new perspective on the mechanisms underlying shaping their communities.