Metabolic engineering of human gut microbiome: Recent developments and future perspectives

Temporal network analysis of gut microbiota unveils aging trajectories associated with colon cancer

The human gut microbiome's role in colorectal cancer (CRC) pathogenesis has gained increasing recognition. This study aimed to delineate the microbiome characteristics that distinguish CRC patients from healthy individuals, while also evaluating the influence of aging, through a comprehensive metagenomic approach. The study analyzed a cohort of 80 CRC patients and 80 matched healthy controls, dividing participants into a normal and a CRC group, further categorized by age into young, middle-aged, and old-aged subgroups. Extensive metagenomic sequencing of fecal samples allowed for the exploration of both the structural and functional profiles of the microbiome, with findings validated in an independent cohort to ensure robustness. Our results highlight notable differences in microbiome composition between CRC patients and healthy individuals, which exhibit age-dependent variations. Specifically, a higher prevalence of pathogenic bacteria, such as Bacteroides vulgatus, known to drive inflammation and carcinogenesis, was observed in CRC patients, alongside a reduction in beneficial microbes, including Lactobacillus. Functionally, the CRC-associated microbiome showed an increase in pathways related to DNA repair, cell cycle regulation, and metabolic activities, such as the Citrate cycle and Galactose metabolism, underscoring distinct microbial alterations in CRC patients that could influence disease onset and progression. These insights lay a foundation for future research into microbiome-based diagnostics and treatments for CRC.

IMPORTANCE

This study underscores the critical role of the gut microbiome in colorectal cancer (CRC) pathogenesis, particularly in the context of aging. By identifying age-specific microbial biomarkers and functional pathways associated with CRC, our findings provide novel insights into how microbiome composition and metabolic activities influence disease progression. These discoveries pave the way for developing personalized microbiome-based diagnostic tools and therapeutic strategies, potentially improving CRC prevention and treatment outcomes across different age groups. Understanding these microbial dynamics could also inform interventions targeting gut microbiota to mitigate CRC risk and progression.

Metaproteomic Profiling of the Secretome of a Granule-forming Ca. Accumulibacter Enrichment

Extracellular proteins are supposed to play crucial roles in the formation and structure of biofilms and aggregates. However, often little is known about these proteins, in particular for microbial communities. Here, we use two advanced metaproteomic approaches to study the extracellular proteome in a granular Candidatus Accumulibacter enrichment as a proxy for microbial communities that form solid microbial granules, such as those used in biological wastewater treatment. Limited proteolysis of whole granules and metaproteome isolation from the culture's supernatant successfully classified over 50% of the identified protein biomass to be secreted. Moreover, structural and sequence-based classification identified 387 proteins, corresponding to over 50% of the secreted protein biomass, with characteristics that could aid the formation of aggregates, including filamentous, beta-barrel containing, and cell surface proteins. While various of these aggregate-forming proteins originated from Ca. Accumulibacter, some proteins associated with other taxa. This suggests that not only a range of different proteins but also multiple organisms contribute to granular biofilm formation. Therefore, the obtained extracellular metaproteome data from the granular Ca. Accumulibacter enrichment provides a resource for exploring proteins that potentially support the formation and stability of granular biofilms, whereas the demonstrated approaches can be applied to explore biofilms of microbial communities in general.

Personalized gut microbial community modeling by leveraging genome-scale metabolic models and metagenomics

The impact of the gut microbiome on human health is increasingly recognized as dysbiosis has been found to be associated with a spectrum of diseases. Here, we review the databases of genome-scale metabolic models (GEMs), which have paved the way for investigations into the metabolic capabilities of gut microbes and their interspecies dynamics. We further discuss the strategies for developing community-level GEMs, which are crucial for understanding the complex interactions within microbial communities and between the microbiome and its host. Such GEMs can guide the design of synthetic microbial communities for disease treatment. Finally, we explore advances in personalized gut microbiome modeling. These advancements broaden our mechanistic understanding and hold promise for applications in precision medicine and therapeutic interventions.

Next-Generation Probiotics and Chronic Diseases: A Review of Current Research and Future Directions

The burgeoning field of microbiome research has profoundly reshaped our comprehension of human health, particularly highlighting the potential of probiotics and fecal microbiota transplantation (FMT) as therapeutic interventions. While the benefits of traditional probiotics are well-recognized, the efficacy and mechanisms remain ambiguous, and FMT's long-term effects are still being investigated. Recent advancements in high-throughput sequencing have identified gut microbes with significant health benefits, paving the way for next-generation probiotics (NGPs). These NGPs, engineered through synthetic biology and bioinformatics, are designed to address specific disease states with enhanced stability and viability. This review synthesizes current research on NGP stability, challenges in delivery, and their applications in preventing and treating chronic diseases such as diabetes, obesity, and cardiovascular diseases. We explore the physiological characteristics, safety profiles, and mechanisms of action of various NGP strains while also addressing the challenges and opportunities presented by their integration into clinical practice. The potential of NGPs to revolutionize microbiome-based therapies and improve clinical outcomes is immense, underscoring the need for further research to optimize their efficacy and ensure their safety.

Assembly of functional microbial ecosystems: from molecular circuits to communities

Microbes compete and cooperate with each other via a variety of chemicals and circuits. Recently, to decipher, simulate, or reconstruct microbial communities, many researches have been engaged in engineering microbiomes with bottom-up synthetic biology approaches for diverse applications. However, they have been separately focused on individual perspectives including genetic circuits, communications tools, microbiome engineering, or promising applications. The strategies for coordinating microbial ecosystems based on different regulation circuits have not been systematically summarized, which calls for a more comprehensive framework for the assembly of microbial communities. In this review, we summarize diverse cross-talk and orthogonal regulation modules for de novo bottom-up assembling functional microbial ecosystems, thus promoting further consortia-based applications. First, we review the cross-talk communication-based regulations among various microbial communities from intra-species and inter-species aspects. Then, orthogonal regulations are summarized at metabolites, transcription, translation, and post-translation levels, respectively. Furthermore, to give more details for better design and optimize various microbial ecosystems, we propose a more comprehensive design-build-test-learn procedure including function specification, chassis selection, interaction design, system build, performance test, modeling analysis, and global optimization. Finally, current challenges and opportunities are discussed for the further development and application of microbial ecosystems.

Refining microbial community metabolic models derived from metagenomics using reference-based taxonomic profiling

Characterization of microbial community metabolic output is crucial to understanding their functions. Construction of genome-scale metabolic models from metagenome-assembled genomes (MAG) has enabled prediction of metabolite production by microbial communities, yet little is known about their accuracy. Here, we examined the performance of two approaches for metabolite prediction from metagenomes, one that is MAG-guided and another that is taxonomic reference-guided. We applied both on shotgun metagenomics data from human and environmental samples, and validated findings in the human samples using untargeted metabolomics. We found that in human samples, where taxonomic profiling is optimized and reference genomes are readily available, when number of input taxa was normalized, the reference-guided approach predicted more metabolites than the MAG-guided approach. The two approaches showed significant overlap but each identified metabolites not predicted in the other. Pathway enrichment analyses identified significant differences in inferences derived from data based on the approach, highlighting the need for caution in interpretation. In environmental samples, when the number of input taxa was normalized, the reference-guided approach predicted more metabolites than the MAG-guided approach for total metabolites in both sample types and non-redundant metabolites in seawater samples. Nonetheless, as was observed for the human samples, the approaches overlapped substantially but also predicted metabolites not observed in the other. Our findings report on utility of a complementary input to genome-scale metabolic model construction that is less computationally intensive forgoing MAG assembly and refinement, and that can be applied on shallow shotgun sequencing where MAGs cannot be generated.IMPORTANCELittle is known about the accuracy of genome-scale metabolic models (GEMs) of microbial communities despite their influence on inferring community metabolic outputs and culture conditions. The performance of GEMs for metabolite prediction from metagenomes was assessed by applying two approaches on shotgun metagenomics data from human and environmental samples, and validating findings in the human samples using untargeted metabolomics. The performance of the approach was found to be dependent on sample type, but collectively, the reference-guided approach predicted more metabolites than the MAG-guided approach. Despite the differences, the predictions from the approaches overlapped substantially but each identified metabolites not predicted in the other. We found significant differences in biological inferences based on the approach, with some examples of uniquely enriched pathways in one group being invalidated when using the alternative approach, highlighting the need for caution in interpretation of GEMs.

Engineering Gut Symbionts: A Way to Promote Bee Growth?

Bees play a crucial role as pollinators, contributing significantly to ecosystems. However, the honeybee population faces challenges such as global warming, pesticide use, and pathogenic microorganisms. Promoting bee growth using several approaches is therefore crucial for maintaining their roles. To this end, the bacterial microbiota is well-known for its native role in supporting bee growth in several respects. Maximizing the capabilities of these microorganisms holds the theoretical potential to promote the growth of bees. Recent advancements have made it feasible to achieve this enhancement through the application of genetic engineering. In this review, we present the roles of gut symbionts in promoting bee growth and collectively summarize the engineering approaches that would be needed for future applications. Particularly, as the engineering of bee gut symbionts has not been advanced, the dominant gut symbiotic bacteria Snodgrassella alvi and Gilliamella apicola are the main focus of the paper, along with other dominant species. Moreover, we propose engineering strategies that will allow for the improvement in bee growth with listed gene targets for modification to further encourage the use of engineered gut symbionts to promote bee growth.

BN-BacArena: Bayesian network extension of BacArena for the dynamic simulation of microbial communities

MOTIVATION

Simulating gut microbial dynamics is extremely challenging. Several computational tools, notably the widely used BacArena, enable modeling of dynamic changes in the microbial environment. These methods, however, do not comprehensively account for microbe-microbe stimulant or inhibitory effects or for nutrient-microbe inhibitory effects, typically observed in different compounds present in the daily diet.

RESULTS

Here, we present BN-BacArena, an extension of BacArena consisting on the incorporation within the native computational framework of a Bayesian network model that accounts for microbe-microbe and nutrient-microbe interactions. Using in vitro experiments, 16S rRNA gene sequencing data and nutritional composition of 55 foods, the output Bayesian network showed 23 significant nutrient-bacteria interactions, suggesting the importance of compounds such as polyols, ascorbic acid, polyphenols and other phytochemicals, and 40 bacteria-bacteria significant relationships. With test data, BN-BacArena demonstrates a statistically significant improvement over BacArena to predict the time-dependent relative abundance of bacterial species involved in the gut microbiota upon different nutritional interventions. As a result, BN-BacArena opens new avenues for the dynamic modeling and simulation of the human gut microbiota metabolism.

AVAILABILITY AND IMPLEMENTATION

MATLAB and R code are available in https://github.com/PlanesLab/BN-BacArena.

The Potential Harmful Effects of Genetically Engineered Microorganisms (GEMs) on the Intestinal Microbiome and Public Health

Gut luminal dysbiosis and pathobiosis result in compositional and biodiversified alterations in the microbial and host co-metabolites. The primary mechanism of bacterial evolution is horizontal gene transfer (HGT), and the acquisition of new traits can be achieved through the exchange of mobile genetic elements (MGEs). Introducing genetically engineered microbes (GEMs) might break the harmonized balance in the intestinal compartment. The present objectives are: 1. To reveal the role played by the GEMs' horizontal gene transfers in changing the landscape of the enteric microbiome eubiosis 2. To expand on the potential detrimental effects of those changes on the human genome and health. A search of articles published in PubMed/MEDLINE, EMBASE, and Scielo from 2000 to August 2023 using appropriate MeSH entry terms was performed. The GEMs' horizontal gene exchanges might induce multiple human diseases. The new GEMs can change the long-term natural evolution of the enteric pro- or eukaryotic cell inhabitants. The worldwide regulatory authority's safety control of GEMs is not enough to protect public health. Viability, biocontainment, and many other aspects are only partially controlled and harmful consequences for public health should be avoided. It is important to remember that prevention is the most cost-effective strategy and primum non nocere should be the focus.

Gut-targeted therapies for type 2 diabetes mellitus: A review

Type 2 diabetes mellitus (T2DM) is a chronic metabolic disorder characterized by hyperglycemia and insulin resistance. The global prevalence of T2DM has reached epidemic proportions, affecting approximately 463 million adults worldwide in 2019. Current treatments for T2DM include lifestyle modifications, oral antidiabetic agents, and insulin therapy. However, these therapies may carry side effects and fail to achieve optimal glycemic control in some patients. Therefore, there is a growing interest in the role of gut microbiota and more gut-targeted therapies in the management of T2DM. The gut microbiota, which refers to the community of microorganisms that inhabit the human gut, has been shown to play a crucial role in the regulation of glucose metabolism and insulin sensitivity. Alterations in gut microbiota composition and diversity have been observed in T2DM patients, with a reduction in beneficial bacteria and an increase in pathogenic bacteria. This dysbiosis may contribute to the pathogenesis of the disease by promoting inflammation and impairing gut barrier function. Several gut-targeted therapies have been developed to modulate the gut microbiota and improve glycemic control in T2DM. One potential approach is the use of probiotics, which are live microorganisms that confer health benefits to the host when administered in adequate amounts. Several randomized controlled trials have demonstrated that certain probiotics, such as Lactobacillus and Bifidobacterium species, can improve glycemic control and insulin sensitivity in T2DM patients. Mechanisms may include the production of short-chain fatty acids, the improvement of gut barrier function, and the reduction of inflammation. Another gut-targeted therapy is fecal microbiota transplantation (FMT), which involves the transfer of fecal material from a healthy donor to a recipient. FMT has been used successfully in the treatment of Clostridioides difficile infection and is now being investigated as a potential therapy for T2DM. A recent randomized controlled trial showed that FMT from lean donors improved glucose metabolism and insulin sensitivity in T2DM patients with obesity. However, FMT carries potential risks, including transmission of infectious agents and alterations in the recipient's gut microbiota that may be undesirable. In addition to probiotics and FMT, other gut-targeted therapies are being investigated for the management of T2DM, such as prebiotics, synbiotics, and postbiotics. Prebiotics are dietary fibers that promote the growth of beneficial gut bacteria, while synbiotics combine probiotics and prebiotics. Postbiotics refer to the metabolic products of probiotics that may have beneficial effects on the host. The NIH SPARC program, or the Stimulating Peripheral Activity to Relieve Conditions, is a research initiative aimed at developing new therapies for a variety of health conditions, including T2DM. The SPARC program focuses on using electrical stimulation to activate peripheral nerves and organs, in order to regulate glucose levels in the body. The goal of this approach is to develop targeted, non-invasive therapies that can help patients better manage their diabetes. One promising area of research within the SPARC program is the use of electrical stimulation to activate the vagus nerve, which plays an important role in regulating glucose metabolism. Studies have shown that vagus nerve stimulation can improve insulin sensitivity and lower blood glucose levels in patients with T2DM. Gut-targeted therapies, such as probiotics and FMT, have shown potential for improving glycemic control and insulin sensitivity in T2DM patients. However, further research is needed to determine the optimal dose, duration, and safety of these therapies.

NovoLign: metaproteomics by sequence alignment

Tremendous advances in mass spectrometric and bioinformatic approaches have expanded proteomics into the field of microbial ecology. The commonly used spectral annotation method for metaproteomics data relies on database searching, which requires sample-specific databases obtained from whole metagenome sequencing experiments. However, creating these databases is complex, time-consuming, and prone to errors, potentially biasing experimental outcomes and conclusions. This asks for alternative approaches that can provide rapid and orthogonal insights into metaproteomics data. Here, we present NovoLign, a de novo metaproteomics pipeline that performs sequence alignment of de novo sequences from complete metaproteomics experiments. The pipeline enables rapid taxonomic profiling of complex communities and evaluates the taxonomic coverage of metaproteomics outcomes obtained from database searches. Furthermore, the NovoLign pipeline supports the creation of reference sequence databases for database searching to ensure comprehensive coverage. We assessed the NovoLign pipeline for taxonomic coverage and false positive annotations using a wide range of in silico and experimental data, including pure reference strains, laboratory enrichment cultures, synthetic communities, and environmental microbial communities. In summary, we present NovoLign, a de novo metaproteomics pipeline that employs large-scale sequence alignment to enable rapid taxonomic profiling, evaluation of database searching outcomes, and the creation of reference sequence databases. The NovoLign pipeline is publicly available via: https://github.com/hbckleikamp/NovoLign.