MDPbiome: microbiome engineering through prescriptive perturbations

Engineering agricultural soil microbiomes and predicting plant phenotypes

Plant growth-promoting rhizobacteria (PGPR) can improve crop yields, nutrient use efficiency, plant tolerance to stressors, and confer benefits to future generations of crops grown in the same soil. Unlocking the potential of microbial communities in the rhizosphere and endosphere is therefore of great interest for sustainable agriculture advancements. Before plant microbiomes can be engineered to confer desirable phenotypic effects on their plant hosts, a deeper understanding of the interacting factors influencing rhizosphere community structure and function is needed. Dealing with this complexity is becoming more feasible using computational approaches. In this review, we discuss recent advances at the intersection of experimental and computational strategies for the investigation of plant-microbiome interactions and the engineering of desirable soil microbiomes.

Navigation between initial and desired community states using shortcuts

Ecological management problems often involve navigating from an initial to a desired community state. We ask whether navigation without brute-force additions and deletions of species is possible via: adding/deleting a small number of individuals of a species, changing the environment, and waiting. Navigation can yield direct paths (single sequence of actions) or shortcut paths (multiple sequences of actions with lower cost than a direct path). We ask (1) when is non-brute-force navigation possible?; (2) do shortcuts exist and what are their properties?; and (3) what heuristics predict shortcut existence? Using a state diagram framework applied to several empirical datasets, we show that (1) non-brute-force navigation is only possible between some state pairs, (2) shortcuts exist between many state pairs; and (3) changes in abundance and richness are the strongest predictors of shortcut existence, independent of dataset and algorithm choices. State diagrams thus unveil hidden strategies for manipulating species coexistence and efficiently navigating between states.

Combating biotic stresses in plants by synthetic microbial communities: Principles, applications, and challenges

Presently, agriculture worldwide is facing the major challenge of feeding the increasing population sustainably. The conventional practices have not only failed to meet the projected needs, but also led to tremendous environmental consequences. Hence, to ensure a food-secure and environmentally sound future, the major thrust is on sustainable alternatives. Due to challenges associated with conventional means of application of biocontrol agents in the management of biotic stresses in agro-ecosystems, significant transformations in this context is needed. The crucial role played by soil microbiomes in efficiently and sustainably managing the agricultural production has unfolded a newer approach of rhizospheric engineering that shows immense promise in mitigating biotic stresses in an eco-friendly manner. The strategy of generating synthetic microbial communities (SynCom), by integrating omics approaches with traditional techniques of enumeration and in-depth analysis of plant-microbe interactions, is encouraging. The review discusses the significance of the rhizospheric microbiome in plant's fitness, and its manipulation for enhancing plant attributes. The focus of the review is to critically analyze the potential tools for the design and utilization of SynCom as a sustainable approach for rhizospheric engineering to ameliorate biotic stresses in plants. Further, based on the synthesis of reports in the area, we have put forth possible solutions to some of the critical issues that impair the large-scale application of SynComs in agriculture.

Strategies for Enhancing in vitro Degradation of Linuron by Variovorax sp. Strain SRS 16 Under the Guidance of Metabolic Modeling

Phenyl urea herbicides are being extensively used for weed control in both agricultural and non-agricultural applications. Linuron is one of the key herbicides in this family and is in wide use. Like other phenyl urea herbicides, it is known to have toxic effects as a result of its persistence in the environment. The natural removal of linuron from the environment is mainly carried through microbial biodegradation. Some microorganisms have been reported to mineralize linuron completely and utilize it as a carbon and nitrogen source. Variovorax sp. strain SRS 16 is one of the known efficient degraders with a recently sequenced genome. The genomic data provide an opportunity to use a genome-scale model for improving biodegradation. The aim of our study is the construction of a genome-scale metabolic model following automatic and manual protocols and its application for improving its metabolic potential through iterative simulations. Applying flux balance analysis (FBA), growth and degradation performances of SRS 16 in different media considering the influence of selected supplements (potential carbon and nitrogen sources) were simulated. Outcomes are predictions for the suitable media modification, allowing faster degradation of linuron by SRS 16. Seven metabolites were selected for in vitro validation of the predictions through laboratory experiments confirming the degradation-promoting effect of specific amino acids (glutamine and asparagine) on linuron degradation and SRS 16 growth. Overall, simulations are shown to be efficient in predicting the degradation potential of SRS 16 in the presence of specific supplements. The generated information contributes to the understanding of the biochemistry of linuron degradation and can be further utilized for the development of new cleanup solutions without any genetic manipulation.

Microbiome approaches provide the key to biologically control postharvest pathogens and storability of fruits and vegetables

Microbes play an important role in plants and interact closely with their host starting from sprouting seeds, continuing during growth and after harvest. The discovery of their importance for plant and postharvest health initiated a biotechnological development of various antagonistic bacteria and fungi for disease control. Nevertheless, their application often showed inconsistent effects. Recently, high-throughput sequencing-based techniques including advanced microscopy reveal fruits and vegetables as holobionts. At harvest, all fruits and vegetables harbor a highly abundant and specific microbiota including beneficial, pathogenic and spoilage microorganisms. Especially, a high microbial diversity and resilient microbial networks were shown to be linked to fruit and vegetable health, while diseased products showed severe dysbiosis. Field and postharvest handling of fruits and vegetables was shown to affect the indigenous microbiome and therefore has a substantial impact on the storability of fruits and vegetables. Microbiome tracking can be implemented as a new tool to evaluate and assess all postharvest processes and contribute to fruit and vegetable health. Here, we summarize current research advancements in the emerging field of postharvest microbiomes and elaborate its importance. The generated knowledge provides profound insights into postharvest microbiome dynamics and sets a new basis for targeted, microbiome-driven and sustainable control strategies.

Growth rate alterations of human colorectal cancer cells by 157 gut bacteria

Several bacteria in the human gut microbiome have been associated with colorectal cancer (CRC) by high-throughput screens. In some cases, molecular mechanisms have been elucidated that drive tumorigenesis, including bacterial membrane proteins or secreted molecules that interact with the human cancer cells. For most gut bacteria, however, it remains unknown if they enhance or inhibit cancer cell growth. Here, we screened bacteria-free supernatants (secretomes) and inactivated cells of over 150 cultured bacterial strains for their effects on cell growth. We observed family-level and strain-level effects that often differed between bacterial cells and secretomes, suggesting that different molecular mechanisms are at play. Secretomes of Bacteroidaceae, Enterobacteriaceae, and Erysipelotrichaceae bacteria enhanced cell growth, while most Fusobacteriaceae cells and secretomes inhibited growth, contrasting prior findings. In some bacteria, the presence of specific functional genes was associated with cell growth rates, including the virulence genes TcdA, TcdB in Clostridiales and FadA in Fusobacteriaceae, which both inhibited growth. Bacteroidaceae cells that enhanced growth were enriched for genes of the cobalamin synthesis pathway, while Fusobacteriaceae cells that inhibit growth were enriched for genes of the ethanolamine utilization pathway. Together, our results reveal how different gut bacteria have wide-ranging effects on cell growth, contribute a better understanding of the effects of the gut microbiome on host cells, and provide a valuable resource for identifying candidate target genes for potential microbiome-based diagnostics and treatment strategies.

Engineering microbial diagnostics and therapeutics with smart control

Microbes have become an increasingly powerful chassis for developing diagnostic and therapeutic technologies. While many of the earlier engineering efforts used microbes that expressed relevant proteins constitutively, more microbes are being engineered to express them with region-selectivity and disease-responsiveness through biosensors. Such 'smart' microbes have been developed to diagnose and treat a wide range of disorders and diseases, including bacterial infections, cancers, inflammatory disorders, and metabolic disorders. In this review, we discuss synthetic biology technologies that have been applied to engineer microbes for biomedical applications, focusing on recent reports that demonstrate microbial sensing by using animal models or clinical samples. Advances in synthetic biology will enable engineered microbes to significantly improve the medical field.

Optimal Microbiome Networks: Macroecology and Criticality

The human microbiome is an extremely complex ecosystem considering the number of bacterial species, their interactions, and its variability over space and time. Here, we untangle the complexity of the human microbiome for the Irritable Bowel Syndrome (IBS) that is the most prevalent functional gastrointestinal disorder in human populations. Based on a novel information theoretic network inference model, we detected potential species interaction networks that are functionally and structurally different for healthy and unhealthy individuals. Healthy networks are characterized by a neutral symmetrical pattern of species interactions and scale-free topology versus random unhealthy networks. We detected an inverse scaling relationship between species total outgoing information flow, meaningful of node interactivity, and relative species abundance (RSA). The top ten interacting species are also the least relatively abundant for the healthy microbiome and the most detrimental. These findings support the idea about the diminishing role of network hubs and how these should be defined considering the total outgoing information flow rather than the node degree. Macroecologically, the healthy microbiome is characterized by the highest Pareto total species diversity growth rate, the lowest species turnover, and the smallest variability of RSA for all species. This result challenges current views that posit a universal association between healthy states and the highest absolute species diversity in ecosystems. Additionally, we show how the transitory microbiome is unstable and microbiome criticality is not necessarily at the phase transition between healthy and unhealthy states. We stress the importance of considering portfolios of interacting pairs versus single node dynamics when characterizing the microbiome and of ranking these pairs in terms of their interactions (i.e., species collective behavior) that shape transition from healthy to unhealthy states. The macroecological characterization of the microbiome is useful for public health and disease diagnosis and etiognosis, while species-specific analyses can detect beneficial species leading to personalized design of pre- and probiotic treatments and microbiome engineering.

Robust and automatic definition of microbiome states

Analysis of microbiome dynamics would allow elucidation of patterns within microbial community evolution under a variety of biologically or economically important circumstances; however, this is currently hampered in part by the lack of rigorous, formal, yet generally-applicable approaches to discerning distinct configurations of complex microbial populations. Clustering approaches to define microbiome "community state-types" at a population-scale are widely used, though not yet standardized. Similarly, distinct variations within a state-type are well documented, but there is no rigorous approach to discriminating these more subtle variations in community structure. Finally, intra-individual variations with even fewer differences will likely be found in, for example, longitudinal data, and will correlate with important features such as sickness versus health. We propose an automated, generic, objective, domain-independent, and internally-validating procedure to define statistically distinct microbiome states within datasets containing any degree of phylotypic diversity. Robustness of state identification is objectively established by a combination of diverse techniques for stable cluster verification. To demonstrate the efficacy of our approach in detecting discreet states even in datasets containing highly similar bacterial communities, and to demonstrate the broad applicability of our method, we reuse eight distinct longitudinal microbiome datasets from a variety of ecological niches and species. We also demonstrate our algorithm's flexibility by providing it distinct taxa subsets as clustering input, demonstrating that it operates on filtered or unfiltered data, and at a range of different taxonomic levels. The final output is a set of robustly defined states which can then be used as general biomarkers for a wide variety of downstream purposes such as association with disease, monitoring response to intervention, or identifying optimally performant populations.