The microbiome plays many roles in human health, often through the exclusive lens of clinical interest. The inevitable end point for all living hosts, death, has its own altered microbiome configurations. However, little is understood about the ecology and changes of microbial communities after death, or their potential utility for understanding the health condition of the recently living. Here we reveal distinct postmortem microbiomes of human hosts from a large-scale survey of death cases representing a predominantly urban population, and demonstrated these microbiomes reflected antemortem health conditions within 24–48 hours of death. Our results characterized microbial community structure and predicted function from 188 cases representing a cross-section of an industrial-urban population. We found strong niche differentiation of anatomic habitat and microbial community turnover based on topographical distribution. Microbial community stability was documented up to two days after death. Additionally, we observed a positive relationship between cell motility and time since host death. Interestingly, we discovered evidence that microbial biodiversity is a predictor of antemortem host health condition (e.g., heart disease). These findings improve the understanding of postmortem host microbiota dynamics, and provide a robust dataset to test the postmortem microbiome as a tool for assessing health conditions in living populations.

Decomposition contributes to global ecosystem function by contributing to nutrient recycling, energy flow, and limiting biomass accumulation. The decomposer organisms influencing this process form diverse, complex, and highly dynamic communities that often specialize on different plant or animal resources. Despite performing the same net role, there is a need to conceptually synthesize information on the structure and function of decomposer communities across the spectrum of dead plant and animal resources. A lack of synthesis has limited cross‐disciplinary learning and research in important areas of ecosystem and community ecology. Here we expound on the “necrobiome” concept and develop a framework describing the decomposer communities and their interactions associated with plant and animal resource types within multiple ecosystems. We outline the biotic structure and ecological functions of the necrobiome, along with how the necrobiome fits into a broader landscape and ecosystem context. The expanded necrobiome model provides a set of perspectives on decomposer communities across resource types, and conceptually unifies plant and animal decomposer communities into the same framework, while acknowledging key differences in processes and mechanisms. This framework is intended to raise awareness among researchers, and advance the construction of explicit, mechanistic hypotheses that further our understanding of decomposer community contributions to biodiversity, the structure and function of ecosystems, global nutrient recycling and energy flow.

Using larvae of the black soldier fly (Hermetia illucens; BSF) to convert low-value residual organic resources into high-value products like protein-rich animal feed ingredients and biofuel while managing organic waste has developed into a global industry. Considering the associated exponential increase in publications dealing with diet conversion efficiency by BSF larvae, it is timely to suggest procedures to arrive at an improved harmonization and reproducibility among studies. This means establishing protocols for describing the basic experiment design, fly colony origin, rearing procedures, reference and experimental feeding substrates, and sampling preparations including microbiota and chemical analyses. Such standardised protocols are instrumental to allow conversion efficiencies to be calculated. Some of these parameters are relatively easy to describe such as giving the origin and rearing conditions, while others are more challenging (e.g. description of microbe community). In this article we discuss and propose such procedures with the aim to arrive at standardisation of how future resource conversion studies with BSF larvae are conducted and how results are communicated.

Letermovir is a human cytomegalovirus terminase inhibitor for cytomegalovirus infection prophylaxis in hematopoietic stem cell transplant recipients. Posaconazole (POS), a substrate of glucuronosyltransferase and P-glycoprotein, and voriconazole (VRC), a substrate of CYP2C9/19, are commonly administered to transplant recipients. Because coadministration of these azoles with letermovir is expected, the effect of letermovir on exposure to these antifungals was investigated. Two trials were conducted in healthy female subjects 18 to 55 years of age. In trial 1, single-dose POS 300 mg was administered alone, followed by a 7-day washout; then letermovir 480 mg once daily was given for 14 days with POS 300 mg coadministered on day 14. In trial 2, on day 1 VRC 400 mg was given every 12 hours; on days 2 and 3, VRC 200 mg was given every 12 hours, and on day 4 VRC 200 mg. On days 5 to 8, letermovir 480 mg was given once daily. Days 9 to 12 repeated days 1 to 4 coadministered with letermovir 480 mg once daily. In both trials, blood samples were collected for the assessment of the pharmacokinetic profiles of the antifungals, and safety was assessed. The geometric mean ratios (90%CIs) for POS+letermovir/POS area under the curve and peak concentration were 0.98 (0.83, 1.17) and 1.11 (0.95, 1.29), respectively. Voriconazole+letermovir/VRC area under the curve and peak concentration geometric mean ratios were 0.56 (0.51, 0.62) and 0.61 (0.53, 0.71), respectively. All treatments were generally well tolerated. Letermovir did not affect POS pharmacokinetics to a clinically meaningful extent but decreased VRC exposure. These results suggest that letermovir may be a perpetrator of CYP2C9/19-mediated drug-drug interactions.

Microbially mediated mechanisms of human decomposition begin immediately after death, and are a driving force for the conversion of a once living organism to a resource of energy and nutrients. Little is known about post-mortem microbiology in cadavers, particularly the community structure of microflora residing within the cadaver and the dynamics of these communities during decomposition. Recent work suggests these bacterial communities undergo taxa turnover and shifts in community composition throughout the post-mortem interval. In this paper we describe how the microbiome of a living host changes and transmigrates within the body after death thus linking the microbiome of a living individual to post-mortem microbiome changes. These differences in the human post-mortem from the ante-mortem microbiome have demonstrated promise as evidence in death investigations. We investigated the post-mortem structure and function dynamics of Staphylococcus aureus and Clostridium perfringens after intranasal inoculation in the animal model Mus musculus L. (mouse) to identify how transmigration of bacterial species can potentially aid in post-mortem interval estimations. S. aureus was tracked using in vivo and in vitro imaging to determine colonization routes associated with different physiological events of host decomposition, while C. perfringens was tracked using culture-based techniques. Samples were collected at discrete time intervals associated with various physiological events and host decomposition beginning at 1h and ending at 60 days post-mortem. Results suggest that S. aureus reaches its highest concentration at 5-7 days post-mortem then begins to rapidly decrease and is undetectable by culture on day 30. The ability to track these organisms as they move in to once considered sterile space may be useful for sampling during autopsy to aid in determining post-mortem interval range estimations, cause of death, and origins associated with the geographic location of human remains during death investigations.

Doravirine is a nonnucleoside reverse transcriptase inhibitor in clinical development for the treatment of human immunodeficiency virus-1 infection in combination with other antiretroviral therapies. The cytochrome P450 (CYP)3A-dependent metabolism of doravirine makes it susceptible to interactions with modulators of this pathway, including the antituberculosis treatment rifampin. Rifabutin, an alternative antibiotic used to treat tuberculosis, may have a lower-magnitude effect on CYP3A. The aim of this trial was to determine the effect of steady-state rifabutin on doravirine single-dose pharmacokinetics and tolerability. In this open-label, 2-period, fixed-sequence, drug-drug interaction study, healthy subjects received a single dose of doravirine 100 mg alone and coadministered on day 14 of once-daily administration of rifabutin 300 mg for 16 days. Plasma samples were taken to determine doravirine pharmacokinetics, and safety was monitored throughout. Dose adjustment of doravirine in the presence of coadministered rifabutin was explored through nonparametric superposition analysis. Rifabutin reduced doravirine area under the concentration-time curve from time zero to infinite and plasma drug concentration 24 hours postdose with geometric mean ratios ([rifabutin+doravirine]/[doravirine alone]) (90%CIs) of 0.50 (0.45-0.55) and 0.32 (0.28-0.35), respectively. Doravirine apparent clearance increased from 5.9 L/h without rifabutin to 12.2 L/h when coadministered. Doravirine pharmacokinetics with and without coadministered rifabutin were not equivalent. Nonparametric superposition analysis projected that administration of doravirine 100 mg twice daily with rifabutin will restore steady-state trough concentration values to efficacious levels associated with doravirine 100 mg once daily in the absence of CYP3A inducers. Doravirine may be coadministered with rifabutin when the doravirine dose frequency is increased from 100 mg once daily to 100 mg twice daily.

Background The postmortem microbiome can provide valuable information to a death investigation and to the human health of the once living. Microbiome sequencing produces, in general, large multi-dimensional datasets that can be difficult to analyze and interpret. Machine learning methods can be useful in overcoming this analytical challenge. However, different methods employ distinct strategies to handle complex datasets. It is unclear whether one method is more appropriate than others for modeling postmortem microbiomes and their ability to predict attributes of interest in death investigations, which require understanding of how the microbial communities change after death and may represent those of the once living host. Methods and findings Postmortem microbiomes were collected by swabbing five anatomical areas during routine death investigation, sequenced and analyzed from 188 death cases. Three machine learning methods (boosted algorithms, random forests, and neural networks) were compared with respect to their abilities to predict case attributes: postmortem interval (PMI), location of death, and manner of death. Accuracy depended on the method used, the numbers of anatomical areas analyzed, and the predicted attribute of death. Conclusions All algorithms performed well but with distinct features to their performance. Xgboost often produced the most accurate predictions but may also be more prone to overfitting. Random forest was the most stable across predictions that included more anatomic areas. Analysis of postmortem microbiota from more than three anatomic areas appears to yield limited returns on accuracy, with the eyes and rectum providing the most useful information correlating with circumstances of death in most cases for this dataset.

Doravirine is a novel, highly potent, nonnucleoside reverse transcriptase inhibitor that is administered once daily and that is in development for the treatment of HIV-1 infection. In vitro and clinical data suggest that doravirine is unlikely to cause significant drug-drug interactions via major drug-metabolizing enzymes or transporters. As a common HIV-1 infection comorbidity, hypercholesterolemia is often treated with statins, including the commonly prescribed atorvastatin. Atorvastatin is subject to drug-drug interactions with cytochrome P450 3A4 (CYP3A4) inhibitors. Increased exposure due to CYP3A4 inhibition may lead to serious adverse events (AEs), including rhabdomyolysis. Furthermore, atorvastatin is a substrate for breast cancer resistance protein (BCRP), of which doravirine may be a weak inhibitor; this may increase atorvastatin exposure. The potential of doravirine to affect atorvastatin pharmacokinetics was investigated in a two-period, fixed-sequence study in healthy individuals. In period 1, a single dose of atorvastatin at 20 mg was administered followed by a 72-h washout. In period 2, doravirine at 100 mg was administered once daily for 8 days, with a single dose of atorvastatin at 20 mg concomitantly being administered on day 5. Sixteen subjects were enrolled, and 14 completed the trial; 2 discontinued due to AEs unrelated to the treatment. The atorvastatin area under the curve from time zero to infinity was similar with and without doravirine (geometric mean ratio [GMR] for doravirine-atorvastatin/atorvastatin, 0.98; 90% confidence interval [CI], 0.90 to 1.06), while the maximum concentration decreased by 33% (GMR for doravirineatorvastatin/atorvastatin, 0.67; 90% CI, 0.52 to 0.85). These changes were deemed not to be clinically meaningful. Both of the study drugs were generally well tolerated. Doravirine had no clinically relevant effect on atorvastatin pharmacokinetics in healthy subjects, providing support for the coadministration of doravirine and atorvastatin.