Engineering Human Microbiota: Influencing Cellular and Community Dynamics for Therapeutic Applications

Causal effects in microbiomes using interventional calculus

Causal inference in biomedical research allows us to shift the paradigm from investigating associational relationships to causal ones. Inferring causal relationships can help in understanding the inner workings of biological processes. Association patterns can be coincidental and may lead to wrong conclusions about causality in complex systems. Microbiomes are highly complex, diverse, and dynamic environments. Microbes are key players in human health and disease. Hence knowledge of critical causal relationships among the entities in a microbiome, and the impact of internal and external factors on microbial abundance and their interactions are essential for understanding disease mechanisms and making appropriate treatment recommendations. In this paper, we employ causal inference techniques to understand causal relationships between various entities in a microbiome, and to use the resulting causal network to make useful computations. We introduce a novel pipeline for microbiome analysis, which includes adding an outcome or "disease" variable, and then computing the causal network, referred to as a "disease network", with the goal of identifying disease-relevant causal factors from the microbiome. Internventional techniques are then applied to the resulting network, allowing us to compute a measure called the causal effect of one or more microbial taxa on the outcome variable or the condition of interest. Finally, we propose a measure called causal influence that quantifies the total influence exerted by a microbial taxon on the rest of the microiome. Our pipeline is robust, sensitive, different from traditional approaches, and able to predict interventional effects without any controlled experiments. The pipeline can be used to identify potential eubiotic and dysbiotic microbial taxa in a microbiome. We validate our results using synthetic data sets and using results on real data sets that were previously published.

Current methodologies to refine bioavailability, delivery, and therapeutic efficacy of plant flavonoids in cancer treatment

Over the last two decades, several advancements have been made to improve the therapeutic efficacy of plant flavonoids, especially in cancer treatment. Factors such as low bioavailability, poor flavonoid stability and solubility, ineffective targeted delivery, and chemo-resistance hinder the application of flavonoids in anti-cancer therapy. Many anti-cancer compounds failed in the clinical trials because of unexpected altered clearance of flavonoids, poor absorption after administration, low efficacy, and/or adverse effects. Hence, the current research strategies are focused on improving the therapeutic efficacy of plant flavonoids, especially by enhancing their bioavailability through combination therapy, engineering gut microbiota, regulating flavonoids interaction with adenosine triphosphate binding cassette efflux transporters, and efficient delivery using nanocrystal and encapsulation technologies. This review aims to discuss different methodologies with examples from reported dietary flavonoids that showed an enhanced anti-cancer efficacy in both in vitro and in vivo models. Further, the review discusses the recent progress in biochemical modifications of flavonoids to improve bioavailability, solubility, and therapeutic efficacy.

Fecal Microbiota Transplantation in Allogeneic Hematopoietic Stem Cell Transplantation Recipients: A Systematic Review

The disruption of gut microbiota eubiosis has been linked to major complications in allogeneic hematopoietic stem cell transplantation (allo-HSCT) recipients. Various strategies have been developed to reduce dysbiosis and related complications. Fecal microbiota transplantation (FMT) consists of the infusion of fecal matter from a healthy donor to restore impaired intestinal homeostasis, and could be applied in the allo-HSCT setting. We conducted a systematic review of studies addressing the use of FMT in allo-HSCT patients. In the 23 papers included in the qualitative synthesis, FMT was used for the treatment of recurrent Clostridioides difficile infections or as a therapeutic strategy for steroid-resistant gut aGvHD. FMT was also performed with a preventive aim (e.g., to decolonize from antibiotic-resistant bacteria). Additional knowledge on the biological mechanisms underlying clinical findings is needed in order to employ FMT in clinical practice. There is also concern regarding the administration of microbial consortia in immune-compromised patients with altered gut permeability. Therefore, the safety profile and efficacy of the procedure must be determined to better assess the role of FMT in allo-HSCT recipients.

Emerging Priorities for Microbiome Research

Microbiome research has increased dramatically in recent years, driven by advances in technology and significant reductions in the cost of analysis. Such research has unlocked a wealth of data, which has yielded tremendous insight into the nature of the microbial communities, including their interactions and effects, both within a host and in an external environment as part of an ecological community. Understanding the role of microbiota, including their dynamic interactions with their hosts and other microbes, can enable the engineering of new diagnostic techniques and interventional strategies that can be used in a diverse spectrum of fields, spanning from ecology and agriculture to medicine and from forensics to exobiology. From June 19-23 in 2017, the NIH and NSF jointly held an Innovation Lab on Quantitative Approaches to Biomedical Data Science Challenges in our Understanding of the Microbiome. This review is inspired by some of the topics that arose as priority areas from this unique, interactive workshop. The goal of this review is to summarize the Innovation Lab's findings by introducing the reader to emerging challenges, exciting potential, and current directions in microbiome research. The review is broken into five key topic areas: (1) interactions between microbes and the human body, (2) evolution and ecology of microbes, including the role played by the environment and microbe-microbe interactions, (3) analytical and mathematical methods currently used in microbiome research, (4) leveraging knowledge of microbial composition and interactions to develop engineering solutions, and (5) interventional approaches and engineered microbiota that may be enabled by selectively altering microbial composition. As such, this review seeks to arm the reader with a broad understanding of the priorities and challenges in microbiome research today and provide inspiration for future investigation and multi-disciplinary collaboration.

Insights into the role of intestinal microbiota in hematopoietic stem-cell transplantation

The gut microbiota (GM) is able to modulate the human immune system. The development of novel investigation methods has provided better characterization of the GM, increasing our knowledge of the role of GM in the context of hematopoietic stem-cell transplantation (HSCT). In particular, the GM influences the development of the major complications seen after HSCT, having an impact on overall survival. In fact, this evidence highlights the possible therapeutic implications of modulation of the GM during HSCT. Insights into the complex mechanisms and functions of the GM are essential for the rational design of these therapeutics. To date, preemptive and curative approaches have been tested. The current state of understanding of the impact of the GM on HSCT, and therapies targeting the GM balance is reviewed herein.

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.

16S rRNA sequence embeddings: Meaningful numeric feature representations of nucleotide sequences that are convenient for downstream analyses

Advances in high-throughput sequencing have increased the availability of microbiome sequencing data that can be exploited to characterize microbiome community structure in situ. We explore using word and sentence embedding approaches for nucleotide sequences since they may be a suitable numerical representation for downstream machine learning applications (especially deep learning). This work involves first encoding (“embedding”) each sequence into a dense, low-dimensional, numeric vector space. Here, we use Skip-Gram word2vec to embed k-mers, obtained from 16S rRNA amplicon surveys, and then leverage an existing sentence embedding technique to embed all sequences belonging to specific body sites or samples. We demonstrate that these representations are meaningful, and hence the embedding space can be exploited as a form of feature extraction for exploratory analysis. We show that sequence embeddings preserve relevant information about the sequencing data such as k-mer context, sequence taxonomy, and sample class. Specifically, the sequence embedding space resolved differences among phyla, as well as differences among genera within the same family. Distances between sequence embeddings had similar qualities to distances between alignment identities, and embedding multiple sequences can be thought of as generating a consensus sequence. In addition, embeddings are versatile features that can be used for many downstream tasks, such as taxonomic and sample classification. Using sample embeddings for body site classification resulted in negligible performance loss compared to using OTU abundance data, and clustering embeddings yielded high fidelity species clusters. Lastly, the k-mer embedding space captured distinct k-mer profiles that mapped to specific regions of the 16S rRNA gene and corresponded with particular body sites. Together, our results show that embedding sequences results in meaningful representations that can be used for exploratory analyses or for downstream machine learning applications that require numeric data. Moreover, because the embeddings are trained in an unsupervised manner, unlabeled data can be embedded and used to bolster supervised machine learning tasks.

Improvements in the way genomes are sequenced have led to an abundance of microbiome data. With the right approaches, researchers use these data to thoroughly characterize how microbes interact with each other and their host, but sequencing data is of a form (sequences of letters) not ideal for many data analysis approaches. We therefore present an approach to transform sequencing data into arrays of numbers that can capture interesting qualities of the data at the sub-sequence, full-sequence, and sample levels. This allows us to measure the importance of certain microbial sequences with respect to the type of microbe and the condition of the host. Also, representing sequences in this way improves our ability to use other complicated modeling approaches. Using microbiome data from human samples, we show that our numeric representations captured differences between various types of microbes, as well as differences in the body site location from which the samples were collected.

Polysaccharide Utilization Loci: Fueling Microbial Communities

The complex carbohydrates of terrestrial and marine biomass represent a rich nutrient source for free-living and mutualistic microbes alike. The enzymatic saccharification of these diverse substrates is of critical importance for fueling a variety of complex microbial communities, including marine, soil, ruminant, and monogastric microbiota. Consequently, highly specific carbohydrate-active enzymes, recognition proteins, and transporters are enriched in the genomes of certain species and are of critical importance in competitive environments. In Bacteroidetes bacteria, these systems are organized as polysaccharide utilization loci (PULs), which are strictly regulated, colocalized gene clusters that encode enzyme and protein ensembles required for the saccharification of complex carbohydrates. This review provides historical perspectives and summarizes key findings in the study of these systems, highlighting a critical shift from sequence-based PUL discovery to systems-based analyses combining reverse genetics, biochemistry, enzymology, and structural biology to precisely illuminate the molecular mechanisms underpinning PUL function. The ecological implications of dynamic PUL deployment by key species in the human gastrointestinal tract are explored, as well as the wider distribution of these systems in other gut, terrestrial, and marine environments.