Prospectively predicting BPaMZ phase IIb/III trial outcomes using a translational mouse-to-human platform.

Despite known treatments, tuberculosis (TB) remains the world's top infectious killer, highlighting the pressing need for new drug regimens. To prioritize the most efficacious drugs for clinical testing, we previously developed a PK-PD translational platform with bacterial dynamics that reliably predicted short-term monotherapy outcomes in Phase IIa trials from preclinical mouse studies. In this study, we extended our platform to include PK-PD models that account for drug-drug interactions in combination regimens and bacterial regrowth in our bacterial dynamics model to predict cure at the end of treatment and relapse 6 months post-treatment. The Phase III STAND trial testing a new regimen comprised of pretomanid (Pa), moxifloxacin (M), and pyrazinamide (Z) (PaMZ) was suspended after a separate ongoing trial (NC-005) suggested that adding bedaquiline (B) to the PaMZ regimen would improve efficacy. To forecast if the addition of B would, indeed, benefit the PaMZ regimen, we applied an extended translational platform to both regimens. We predicted currently available short- and long-term clinical data well for drug combinations related to BPaMZ. We predicted the addition of B to PaMZ to shorten treatment duration by 2 months and to have similar bacteriological success to standard HRZE treatment (considering only treatment success but not withdrawal from side effects and other adverse events), both at the end of treatment for treatment efficacy and 6 months after treatment has ended in relapse prevention. Using BPaMZ as a case study, we have demonstrated our translational platform can predict Phase II and III outcomes prior to actual trials, allowing us to better prioritize the regimens most likely to succeed.

How to Determine the Role of the Microbiome in Drug Disposition.

With a paradigm shift occurring in health care toward personalized and precision medicine, understanding the numerous environmental factors that impact drug disposition is of paramount importance. The highly diverse and variant nature of the human microbiome is now recognized as a factor driving interindividual variation in therapeutic outcomes. The purpose of this review is to provide a practical guide on methodology that can be applied to study the effects of microbes on the absorption, distribution, metabolism, and excretion of drugs. We also highlight recent examples of how these methods have been successfully applied to help build the basis for researching the intersection of the microbiome and pharmacology. Although in vitro and in vivo preclinical models are highlighted, these methods are also relevant in late-phase drug development or even as a part of routine after-market surveillance. These approaches will aid in filling major knowledge gaps for both current and upcoming therapeutics with the long-term goal of achieving a new type of knowledge-based medicine that integrates data on the host and the microbiome.

SIMMER employs similarity algorithms to accurately identify human gut microbiome species and enzymes capable of known chemical transformations.

Bacteria within the gut microbiota possess the ability to metabolize a wide array of human drugs, foods, and toxins, but the responsible enzymes for these chemical events remain largely uncharacterized due to the time-consuming nature of current experimental approaches. Attempts have been made in the past to computationally predict which bacterial species and enzymes are responsible for chemical transformations in the gut environment, but with low accuracy due to minimal chemical representation and sequence similarity search schemes. Here, we present an in silico approach that employs chemical and protein Similarity algorithms that Identify MicrobioMe Enzymatic Reactions (SIMMER). We show that SIMMER accurately predicts the responsible species and enzymes for a queried reaction, unlike previous methods. We demonstrate SIMMER use cases in the context of drug metabolism by predicting previously uncharacterized enzymes for 88 drug transformations known to occur in the human gut. We validate these predictions on external datasets and provide an in vitro validation of SIMMER's predictions for metabolism of methotrexate, an anti-arthritic drug. After demonstrating its utility and accuracy, we made SIMMER available as both a command-line and web tool, with flexible input and output options for determining chemical transformations within the human gut. We present SIMMER as a computational addition to the microbiome researcher's toolbox, enabling them to make informed hypotheses before embarking on the lengthy laboratory experiments required to characterize novel bacterial enzymes that can alter human ingested compounds.

Human gut bacterial metabolism drives Th17 activation and colitis.

Bacterial activation of T helper 17 (Th17) cells exacerbates mouse models of autoimmunity, but how human-associated bacteria impact Th17-driven disease remains elusive. We show that human gut Actinobacterium Eggerthella lenta induces intestinal Th17 activation by lifting inhibition of the Th17 transcription factor Rorγt through cell- and antigen-independent mechanisms. E. lenta is enriched in inflammatory bowel disease (IBD) patients and worsens colitis in a Rorc-dependent manner in mice. Th17 activation varies across E. lenta strains, which is attributable to the cardiac glycoside reductase 2 (Cgr2) enzyme. Cgr2 is sufficient to induce interleukin (IL)-17a, a major Th17 cytokine. cgr2+ E. lenta deplete putative steroidal glycosides in pure culture; related compounds are negatively associated with human IBD severity. Finally, leveraging the sensitivity of Cgr2 to dietary arginine, we prevented E. lenta-induced intestinal inflammation in mice. Together, these results support a role for human gut bacterial metabolism in driving Th17-dependent autoimmunity.

Genetic basis for the cooperative bioactivation of plant lignans by Eggerthella lenta and other human gut bacteria.

Plant-derived lignans, consumed daily by most individuals, are thought to protect against cancer and other diseases1; however, their bioactivity requires gut bacterial conversion to enterolignans2. Here, we dissect a four-species bacterial consortium sufficient for all five reactions in this pathway. A single enzyme (benzyl ether reductase, encoded by the gene ber) was sufficient for the first two biotransformations, variable between strains of Eggerthella lenta, critical for enterolignan production in gnotobiotic mice and unique to Coriobacteriia. Transcriptional profiling (RNA sequencing) independently identified ber and genomic loci upregulated by each of the remaining substrates. Despite their low abundance in gut microbiomes and restricted phylogenetic range, all of the identified genes were detectable in the distal gut microbiomes of most individuals living in northern California. Together, these results emphasize the importance of considering strain-level variations and bacterial co-occurrence to gain a mechanistic understanding of the bioactivation of plant secondary metabolites by the human gut microbiome.

Isolation and preliminary characterization of amino acid substitution mutations that increase the activity of the osmoregulated ProP protein of Salmonella enterica serovar Typhimurium.

In Enterobacteriaceae, the ProP protein, which takes up proline and glycine betaine, is subject to a post-translational control mechanism that increases its activity at high osmolarity. In order to investigate the osmoregulatory mechanism of the Salmonella enterica ProP, we devised a positive selection for mutations that conferred increased activity on this protein at low osmolarity. The selection involved the isolation of mutations in a proline auxotroph that resulted in increased accumulation of proline via the ProP system in the presence of glycine betaine, which is a competitive inhibitor of proline uptake by this permease. This selection was performed by first-year undergraduates in two semesters of a research-based laboratory course. The students generated sixteen mutations resulting in six different single amino acids substitutions. They determined the effects of the mutations on the growth rates of the cells in media of high and low osmolarity in the presence of low concentrations of proline or glycine betaine. Furthermore, they identified the mutations by DNA sequencing and displayed the mutated amino acids on a putative three-dimensional structure of the protein. This analysis suggested that all six amino acid substitutions are residues in trans-membrane helices that have been proposed to contribute to the formation of the transport pore, and, thus, may affect the substrate binding site of the protein.