The recognition that all macroorganisms live in symbiotic association with microbial communities has opened up a new field in biology. Animals, plants, and algae are now considered holobionts, complex ecosystems consisting of the host, the microbiota, and the interactions among them. Accordingly, ecological concepts can be applied to understand the host-derived and microbial processes that govern the dynamics of the interactive networks within the holobiont. In marine systems, holobionts are further integrated into larger and more complex communities and ecosystems, a concept referred to as “nested ecosystems.” In this review, we discuss the concept of holobionts as dynamic ecosystems that interact at multiple scales and respond to environmental change. We focus on the symbiosis of sponges with their microbial communities—a symbiosis that has resulted in one of the most diverse and complex holobionts in the marine environment. In recent years, the field of sponge microbiology has remarkably advanced in terms of curated databases, standardized protocols, and information on the functions of the microbiota. Like a Russian doll, these microbial processes are translated into sponge holobiont functions that impact the surrounding ecosystem. For example, the sponge-associated microbial metabolisms, fueled by the high filtering capacity of the sponge host, substantially affect the biogeochemical cycling of key nutrients like carbon, nitrogen, and phosphorous. Since sponge holobionts are increasingly threatened by anthropogenic stressors that jeopardize the stability of the holobiont ecosystem, we discuss the link between environmental perturbations, dysbiosis, and sponge diseases. Experimental studies suggest that the microbial community composition is tightly linked to holobiont health, but whether dysbiosis is a cause or a consequence of holobiont collapse remains unresolved. Moreover, the potential role of the microbiome in mediating the capacity for holobionts to acclimate and adapt to environmental change is unknown. Future studies should aim to identify the mechanisms underlying holobiont dynamics at multiple scales, from the microbiome to the ecosystem, and develop management strategies to preserve the key functions provided by the sponge holobiont in our present and future oceans.

/pdf/the-sponge-holobiont-in-a-changing-ocean-from-microbes-to-4jk0oq5277.pdf

The sponge holobiont in a changing ocean: from microbes to ecosystems.

The gut microbiome has been implicated in cancer in several ways, as specific microbial signatures are known to promote cancer development and influence safety, tolerability and efficacy of therapies. The 'omics' technologies used for microbiome analysis continuously evolve and, although much of the research is still at an early stage, large-scale datasets of ever increasing size and complexity are being produced. However, there are varying levels of difficulty in realizing the full potential of these new tools, which limit our ability to critically analyse much of the available data. In this Perspective, we provide a brief overview on the role of gut microbiome in cancer and focus on the need, role and limitations of a machine learning-driven approach to analyse large amounts of complex health-care information in the era of big data. We also discuss the potential application of microbiome-based big data aimed at promoting precision medicine in cancer.

Gut microbiome, big data and machine learning to promote precision medicine for cancer.

Metaorganisms in extreme environments: do microbes play a role in organismal adaptation?

Microorganisms are ubiquitous and closely related to people's daily lives. Since they were first discovered in the 19th century, researchers have shown great interest in microorganisms. People studied microorganisms through cultivation, but this method is expensive and time consuming. However, the cultivation method cannot keep a pace with the development of high-throughput sequencing technology. To deal with this problem, machine learning (ML) methods have been widely applied to the field of microbiology. Literature reviews have shown that ML can be used in many aspects of microbiology research, especially classification problems, and for exploring the interaction between microorganisms and the surrounding environment. In this study, we summarize the application of ML in microbiology.

/pdf/application-of-machine-learning-in-microbiology-2ytjp1fbpt.pdf

Application of Machine Learning in Microbiology.

Defining the organisation of species interaction networks and unveiling the processes behind their assembly is fundamental to understanding patterns of biodiversity, community stability and ecosystem functioning. Marine sponges host complex communities of microorganisms that contribute to their health and survival, yet the mechanisms behind microbiome assembly are largely unknown. We present the global marine sponge-microbiome network and reveal a modular organisation in both community structure and function. Modules are linked by a few sponge species that share microbes with other species around the world. Further, we provide evidence that abiotic factors influence the structuring of the sponge microbiome when considering all microbes present, but biotic interactions drive the assembly of more intimately associated 'core' microorganisms. These findings suggest that both ecological and evolutionary processes are at play in host-microbe network assembly. We expect mechanisms behind microbiome assembly to be consistent across multicellular hosts throughout the tree of life.

/pdf/modularity-and-predicted-functions-of-the-global-sponge-15zpif51ty.pdf

Modularity and predicted functions of the global sponge-microbiome network

The dichotomy between high microbial abundance (HMA) and low microbial abundance (LMA) sponges has been observed in sponge-microbe symbiosis, although the extent of this pattern remains poorly unknown. We characterized the differences between the microbiomes of HMA (n=19) and LMA (n=17) sponges (575 specimens) present in the Sponge Microbiome Project. HMA sponges were associated with richer and more diverse microbiomes than LMA sponges, as indicated by the comparison of alpha diversity metrics. Microbial community structures differed between HMA and LMA sponges considering Operational Taxonomic Units (OTU) abundances and across microbial taxonomic levels, from phylum to species. The largest proportion of microbiome variation was explained by the host identity. Several phyla, classes, and OTUs were found differentially abundant in either group, which were considered “HMA indicators” and “LMA indicators”. Machine learning algorithms (classifiers) were trained to predict the HMA-LMA status of sponges. Among nine different classifiers, higher performances were achieved by Random Forest trained with phylum and class abundances. Random Forest with optimized parameters predicted the HMA-LMA status of additional 135 sponge species (1,232 specimens) without a priori knowledge. These sponges were grouped in four clusters, from which the largest two were composed of species consistently predicted as HMA (n=44) and LMA (n=74). In summary, our analyses shown distinct features of the microbial communities associated with HMA and LMA sponges. The prediction of the HMA-LMA status based on the microbiome profiles of sponges demonstrates the application of machine learning to explore patterns of host-associated microbial communities.

/pdf/predicting-the-hma-lma-status-in-marine-sponges-by-machine-1in272d7px.pdf

Predicting the HMA-LMA Status in Marine Sponges by Machine Learning

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Predicting the HMA-LMA Status in Marine Sponges by Machine Learning