Evaluation of Fusobacterium nucleatum Enoyl-ACP Reductase (FabK) as a Narrow-Spectrum Drug Target.

Fusobacterium nucleatum, a pathobiont inhabiting the oral cavity, contributes to opportunistic diseases, such as periodontal diseases and gastrointestinal cancers, which involve microbiota imbalance. Broad-spectrum antimicrobial agents, while effective against F. nucleatum infections, can exacerbate dysbiosis. This necessitates the discovery of more targeted narrow-spectrum antimicrobial agents. We therefore investigated the potential for the fusobacterial enoyl-ACP reductase II (ENR II) isoenzyme FnFabK (C4N14_ 04250) as a narrow-spectrum drug target. ENRs catalyze the rate-limiting step in the bacterial fatty acid synthesis pathway. Bioinformatics revealed that of the four distinct bacterial ENR isoforms, F. nucleatum specifically encodes FnFabK. Genetic studies revealed that fabK was indispensable for F. nucleatum growth, as the gene could not be deleted, and silencing of its mRNA inhibited growth under the test conditions. Remarkably, exogenous fatty acids failed to rescue growth inhibition caused by the silencing of fabK. Screening of synthetic phenylimidazole analogues of a known FabK inhibitor identified an inhibitor (i.e., 681) of FnFabK enzymatic activity and F. nucleatum growth, with an IC50 of 2.1 µM (1.0 µg/mL) and a MIC of 0.4 µg/mL, respectively. Exogenous fatty acids did not attenuate the activity of 681 against F. nucleatum. Furthermore, FnFabK was confirmed as the intracellular target of 681 based on the overexpression of FnFabK shifting MICs and 681-resistant mutants having amino acid substitutions in FnFabK or mutations in other genetic loci affecting fatty acid biosynthesis. 681 had minimal activity against a range of commensal flora, and it was less active against streptococci in physiologic fatty acids. Taken together, FnFabK is an essential enzyme that is amenable to drug targeting for the discovery and development of narrow-spectrum antimicrobial agents.

The influence of gene flow on population viability in an isolated urban caracal population.

Wildlife populations are becoming increasingly fragmented by anthropogenic development. Small and isolated populations often face an elevated risk of extinction, in part due to inbreeding depression. Here, we examine the genomic consequences of urbanization in a caracal (Caracal caracal) population that has become isolated in the Cape Peninsula region of the City of Cape Town, South Africa, and is thought to number ~50 individuals. We document low levels of migration into the population over the past ~75 years, with an estimated rate of 1.3 effective migrants per generation. As a consequence of this isolation and small population size, levels of inbreeding are elevated in the contemporary Cape Peninsula population (mean FROH = 0.20). Inbreeding primarily manifests as long runs of homozygosity >10 Mb, consistent with the effects of isolation due to the rapid recent growth of Cape Town. To explore how reduced migration and elevated inbreeding may impact future population dynamics, we parameterized an eco-evolutionary simulation model. We find that if migration rates do not change in the future, the population is expected to decline, though with a low projected risk of extinction. However, if migration rates decline or anthropogenic mortality rates increase, the potential risk of extinction is greatly elevated. To avert a population decline, we suggest that translocating migrants into the Cape Peninsula to initiate a genetic rescue may be warranted in the near future. Our analysis highlights the utility of genomic datasets coupled with computational simulation models for investigating the influence of gene flow on population viability.

A longitudinal study of queen health in honey bees reveals tissue specific response to seasonal changes and pathogen pressure.

The health of honey bee queens is crucial for colony success, particularly during stressful periods like overwintering. To accompany a previous longitudinal study of colony and worker health, we explored niche-specific gut microbiota, host gene expression, and pathogen prevalence in honey bee queens overwintering in a warm southern climate. We found differential gene expression and bacterial abundance with respect to various pathogens throughout the season. Biologically older queens had larger microbiotas, particularly enriched in Bombella and Bifidobacterium. Both Deformed Wing Virus A and B subtypes were highest in the fat body tissue in January, correlating with colony Varroa levels, and Deformed Wing Virus titers in workers. High viral titers in queens were associated with decreased vitellogenin expression, suggesting a potential trade-off between immune function and reproductive capacity. Additionally, we found a complex and dynamic relationship between these viral loads and immune gene expression, indicating a possible breakdown in the coordinated immune response as the season progressed. Our study also revealed a potential link between Nosema and Melissococcus plutonius infections in queens, demonstrating that seasonal opportunism is not confined to just workers. Overall, our findings highlight the intricate interplay between pathogens, metabolic state, and immune response in honey bee queens. Combined with worker and colony-level metrics from the same colonies, our findings illustrate the social aspect of queen health and resilience over the winter dearth.

Topological "Shape" in Micellar Dynamics.

For micelles, "shape" is prominent in rheological computations of fluid flow, but this "shape" is often expressed too informally to be useful for rigorous analyses. We formalize topological "shape equivalence" of micelles, both globally and locally, to enable visualization of computational fluid dynamics. Although topological methods in visualization provide significant insights into fluid flows, this opportunity has been limited by the known difficulties in creating representative geometry. We present an agile geometric algorithm to represent the micellar shape for input into fluid flow visualizations. We show that worm-like and cylindrical micelles have formally equivalent shapes, but that visualization accentuates unexplored differences. This global-local paradigm is extensible beyond micelles.

Ensemble pretrained language models to extract biomedical knowledge from literature.

OBJECTIVES: The rapid expansion of biomedical literature necessitates automated techniques to discern relationships between biomedical concepts from extensive free text. Such techniques facilitate the development of detailed knowledge bases and highlight research deficiencies. The LitCoin Natural Language Processing (NLP) challenge, organized by the National Center for Advancing Translational Science, aims to evaluate such potential and provides a manually annotated corpus for methodology development and benchmarking. MATERIALS AND METHODS: For the named entity recognition (NER) task, we utilized ensemble learning to merge predictions from three domain-specific models, namely BioBERT, PubMedBERT, and BioM-ELECTRA, devised a rule-driven detection method for cell line and taxonomy names and annotated 70 more abstracts as additional corpus. We further finetuned the T0pp model, with 11 billion parameters, to boost the performance on relation extraction and leveraged entites' location information (eg, title, background) to enhance novelty prediction performance in relation extraction (RE). RESULTS: Our pioneering NLP system designed for this challenge secured first place in Phase I-NER and second place in Phase II-relation extraction and novelty prediction, outpacing over 200 teams. We tested OpenAI ChatGPT 3.5 and ChatGPT 4 in a Zero-Shot setting using the same test set, revealing that our finetuned model considerably surpasses these broad-spectrum large language models. DISCUSSION AND CONCLUSION: Our outcomes depict a robust NLP system excelling in NER and RE across various biomedical entities, emphasizing that task-specific models remain superior to generic large ones. Such insights are valuable for endeavors like knowledge graph development and hypothesis formulation in biomedical research.

Constraining models of dominance for nonsynonymous mutations in the human genome.

Dominance is a fundamental parameter in genetics, determining the dynamics of natural selection on deleterious and beneficial mutations, the patterns of genetic variation in natural populations, and the severity of inbreeding depression in a population. Despite this importance, dominance parameters remain poorly known, particularly in humans or other non-model organisms. A key reason for this lack of information about dominance is that it is extremely challenging to disentangle the selection coefficient (s) of a mutation from its dominance coefficient (h). Here, we explore dominance and selection parameters in humans by fitting models to the site frequency spectrum (SFS) for nonsynonymous mutations. When assuming a single dominance coefficient for all nonsynonymous mutations, we find that numerous h values can fit the data, so long as h is greater than ~0.15. Moreover, we also observe that theoretically-predicted models with a negative relationship between h and s can also fit the data well, including models with h=0.05 for strongly deleterious mutations. Finally, we use our estimated dominance and selection parameters to inform simulations revisiting the question of whether the out-of-Africa bottleneck has led to differences in genetic load between African and non-African human populations. These simulations suggest that the relative burden of genetic load in non-African populations depends on the dominance model assumed, with slight increases for more weakly recessive models and slight decreases shown for more strongly recessive models. Moreover, these results also demonstrate that models of partially recessive nonsynonymous mutations can explain the observed severity of inbreeding depression in humans, bridging the gap between molecular population genetics and direct measures of fitness in humans. Our work represents a comprehensive assessment of dominance and deleterious variation in humans, with implications for parameterizing models of deleterious variation in humans and other mammalian species.

Biophysical library screening using a Thermo-FMN assay to identify and characterize Clostridioides difficile FabK inhibitors.

Clostridioides difficile, a gram-positive anaerobic bacterium, is one of the most frequent causes of nosocomial infections. C. difficile infection (CDI) results in almost a half a million infections and approximately 30,000 deaths in the U.S. each year. Broad-spectrum antibacterial use is a strong risk factor for development of recurring CDI. There is a critical need for narrow-spectrum antibacterials with activity limited to C. difficile. The C. difficile enoyl-acyl carrier protein (ACP) reductase II enzyme (CdFabK), an essential and rate-limiting enzyme in the organism's fatty acid biosynthesis pathway (FAS-2), is an attractive target for narrow-spectrum CDI therapeutics as it is not present in many of the non-pathogenic gut organisms. We have previously characterized inhibitors of the CdFabK enzyme with narrow-spectrum anti-difficile activity and favorable in vivo efficacy, ADME, and low dysbiosis. To expand our knowledge of the structural requirements for CdFabK inhibition, we seek to identify new inhibitors with novel chemical scaffolds. Herein we present the optimization of a thermo-FMN biophysical assay based on the principles of differential scanning fluorimetry, or thermal shift, which leverages the fluorescence signal of the FabK enzyme's FMN prosthetic group. The optimized assay was validated by pilot testing a 10K diversity-based chemical library and novel scaffold hit compounds were identified and biochemically characterized. Additionally, we show that the thermo-FMN assay can be used to determine the thermodynamic dissociation constant, Kd, of CdFabK inhibitors.

Association of Gabapentinoids With Opioid-Related Overdose in the Inpatient Setting: A Single Center Retrospective Case-Control Study.

Objectives: Recent data suggest concomitant gabapentinoid use increases opioid-related overdose (ORO) risk; however, this association has not been well studied in the hospital setting. The primary objective of this study was to compare ORO risk, indicated by naloxone administration, in patients receiving opioids plus gabapentinoids versus opioids alone. Methods: In this retrospective case-control study of adults admitted to a large community hospital from 1/1/20 to 12/31/21, all cases (defined as patients who received naloxone more than 24 hours after admission) identified were matched 1:1 to randomly selected controls (defined as patients on opioids who did not receive naloxone). The primary outcome was the percentage of cases and controls with concomitant inpatient gabapentinoid use. Logistic regression was performed to determine the independent association between gabapentinoids and ORO (as evidenced by inpatient naloxone administration). Results: Baseline characteristics were similar between the 144 cases and 144 controls. Gabapentinoid exposure was greater for cases than controls (34.0%vs 20.8%, P = .0118). Median hospital length of stay (11vs 4 days, P < .0001) and mortality (19%vs 5%; P = .0018) were also higher for cases. In logistic regression analysis, ORO (adjusted OR 4.91; 95% CI 1.86-12.96) and serotonergic medication exposure (adjusted OR 4.31; 95% CI 1.50-12.38) were significantly associated with gabapentinoid use. Conclusions: Concomitant gabapentinoid use with opioids was associated with increased ORO risk in the inpatient setting. When considering prescribing gabapentinoids in conjunction with opioids in the hospital setting, potential benefits should be weighed against increased overdose risk.

The impact of non-neutral synonymous mutations when inferring selection on non-synonymous mutations.

The distribution of fitness effects (DFE) describes the proportions of new mutations that have different effects on reproductive fitness. Accurate measurements of the DFE are important because the DFE is a fundamental parameter in evolutionary genetics and has implications for our understanding of other phenomena like complex disease or inbreeding depression. Current computational methods to infer the DFE for nonsynonymous mutations from natural variation first estimate demographic parameters from synonymous variants to control for the effects of demography and background selection. Then, conditional on these parameters, the DFE is then inferred for nonsynonymous mutations. This approach relies on the assumption that synonymous variants are neutrally evolving. However, some evidence points toward synonymous mutations having measurable effects on fitness. To test whether selection on synonymous mutations affects inference of the DFE of nonsynonymous mutations, we simulated several possible models of selection on synonymous mutations using SLiM and attempted to recover the DFE of nonsynonymous mutations using Fit∂a∂i, a common method for DFE inference. Our results show that the presence of selection on synonymous variants leads to incorrect inferences of recent population growth. Furthermore, under certain parameter combinations, inferences of the DFE can have an inflated proportion of highly deleterious nonsynonymous mutations. However, this bias can be eliminated if the correct demographic parameters are used for DFE inference instead of the biased ones inferred from synonymous variants. Our work demonstrates how unmodeled selection on synonymous mutations may affect downstream inferences of the DFE.

CHEK2 Founder Variants and Thyroid Cancer Risk.

Background: Germline pathogenic variants in CHEK2 are associated with a moderate increase in the lifetime risk for breast cancer. Increased risk for other cancers, including non-medullary thyroid cancer (NMTC), has also been suggested. To date, data implicating CHEK2 variants in NMTC predisposition primarily derive from studies within Poland, driven by a splice site variant (c.444 + 1G>A) that is uncommon in other populations. In contrast, the predominant CHEK2 variants in non-Polish populations are c.1100del and c.470T>C/p.I157T, representing 61.1% and 63.8%, respectively, of all CHEK2 pathogenic variants in two large U.S.-based commercial laboratory datasets. To further delineate the impact of common CHEK2 variants on thyroid cancer, we aimed to investigate the association of three CHEK2 founder variants (c.444 + 1G>A, c.1100del, and c.470T>C/p.Ile157Thr) on NMTC susceptibility in three groups of unselected NMTC patients. Methods: The presence of three CHEK2 founder variants was assessed within three groups: (1) 1544 NMTC patients (and 1593 controls) from previously published genome-wide association study (GWAS) analyses, (2) 789 NMTC patients with germline exome sequencing (Oncology Research Information Exchange Network [ORIEN] Avatar), and (3) 499 NMTC patients with germline sequence data available in The Cancer Genome Atlas (TCGA). A case-control study design was utilized with odds ratios (ORs) calculated by comparison of all three groups with the Ohio State University GWAS control group. Results: The predominant Polish variant (c.444 + 1G>A) was present in only one case. The proportion of patients with c.1100del was 0.92% in the GWAS group, 1.65% in the ORIEN Avatar group, and 0.80% in the TCGA group. The ORs (with 95% confidence intervals [CIs]) for NMTC associated with c.1100del were 1.71 (0.73-4.29), 2.64 (0.95-7.63), and 2.5 (0.63-8.46), respectively. The proportion of patients with c.470T>C/p.I157T was 0.91% in the GWAS group, 0.76% in the ORIEN Avatar group, and 0.80% in the TCGA group, respectively. The ORs (with CIs) for NMTC associated with c.470T>C/p.I157T were 1.75 (0.74-4.39), 1.52 (0.42-4.96), and 2.31 (0.58-7.90), respectively. Conclusions: Our analyses of unselected patients with NMTC suggest that CHEK2 variants c.1100del and c.470T>C/p.I157T have only a modest impact on thyroid cancer risk. These results provide important information for providers regarding the relatively low magnitude of thyroid cancer risk associated with these CHEK2 variants.

A longitudinal field study of commercial honey bees shows that non-native probiotics do not rescue antibiotic treatment, and are generally not beneficial.

Probiotics are widely used in agriculture including commercial beekeeping, but there is little evidence supporting their effectiveness. Antibiotic treatments can greatly distort the gut microbiome, reducing its protective abilities and facilitating the growth of antibiotic resistant pathogens. Commercial beekeepers regularly apply antibiotics to combat bacterial infections, often followed by an application of non-native probiotics advertised to ease the impact of antibiotic-induced gut dysbiosis. We tested whether probiotics affect the gut microbiome or disease prevalence, or rescue the negative effects of antibiotic induced gut dysbiosis. We found no difference in the gut microbiome or disease markers by probiotic application or antibiotic recovery associated with probiotic treatment. A colony-level application of the antibiotics oxytetracycline and tylosin produced an immediate decrease in gut microbiome size, and over the longer-term, very different and persistent dysbiotic effects on the composition and membership of the hindgut microbiome. Our results demonstrate the lack of probiotic effect or antibiotic rescue, detail the duration and character of dysbiotic states resulting from different antibiotics, and highlight the importance of the gut microbiome for honeybee health.

In vivo evaluation of Clostridioides difficile enoyl-ACP reductase II (FabK) inhibition by phenylimidazole unveils a promising narrow-spectrum antimicrobial strategy.

Clostridioides difficile infection (CDI) is a leading cause of hospital-acquired diarrhea, which often stems from disruption of the gut microbiota by broad-spectrum antibiotics. The increasing prevalence of antibiotic-resistant C. difficile strains, combined with disappointing clinical trial results for recent antibiotic candidates, underscores the urgent need for novel CDI antibiotics. To this end, we investigated C. difficile enoyl ACP reductase (CdFabK), a crucial enzyme in de novo fatty acid synthesis, as a drug target for microbiome-sparing antibiotics. To test this concept, we evaluated the efficacy and in vivo spectrum of activity of the phenylimidazole analog 296, which is validated to inhibit intracellular CdFabK. Against major CDI-associated ribotypes 296 had an Minimum inhibitory concentration (MIC90) of 2 µg/mL, which was comparable to vancomycin (1 µg/mL), a standard of care antibiotic. In addition, 296 achieved high colonic concentrations and displayed dosed-dependent efficacy in mice with colitis CDI. Mice that were given 296 retained colonization resistance to C. difficile and had microbiomes that resembled the untreated mice. Conversely, both vancomycin and fidaxomicin induced significant changes to mice microbiomes, in a manner consistent with prior reports. CdFabK, therefore, represents a potential target for microbiome-sparing CDI antibiotics, with phenylimidazoles providing a good chemical starting point for designing such agents.

Updating M6 pregnancy of unknown location risk-prediction model including evaluation of clinical factors.

OBJECTIVES: Ectopic pregnancy (EP) is a major high-risk outcome following a pregnancy of unknown location (PUL) classification. Biochemical markers are used to triage PUL as high vs low risk to guide appropriate follow-up. The M6 model is currently the best risk-prediction model. We aimed to update the M6 model and evaluate whether performance can be improved by including clinical factors. METHODS: This prospective cohort study recruited consecutive PUL between January 2015 and January 2017 at eight units (Phase 1), with two centers continuing recruitment between January 2017 and March 2021 (Phase 2). Serum samples were collected routinely and sent for ß-human chorionic gonadotropin (ß-hCG) and progesterone measurement. Clinical factors recorded were maternal age, pain score, bleeding score and history of EP. Based on transvaginal ultrasonography and/or biochemical confirmation during follow-up, PUL were classified subsequently as failed PUL (FPUL), intrauterine pregnancy (IUP) or EP (including persistent PUL (PPUL)). The M6 models with (M6P ) and without (M6NP ) progesterone were refitted and extended with clinical factors. Model validation was performed using internal-external cross-validation (IECV) (Phase 1) and temporal external validation (EV) (Phase 2). Missing values were handled using multiple imputation. RESULTS: Overall, 5473 PUL were recruited over both phases. A total of 709 PUL were excluded because maternal age was < 16 years or initial ß-hCG was ≤ 25 IU/L, leaving 4764 (87%) PUL for analysis (2894 in Phase 1 and 1870 in Phase 2). For the refitted M6P model, the area under the receiver-operating-characteristics curve (AUC) for EP/PPUL vs IUP/FPUL was 0.89 for IECV and 0.84-0.88 for EV, with respective sensitivities of 94% and 92-93%. For the refitted M6NP model, the AUCs were 0.85 for IECV and 0.82-0.86 for EV, with respective sensitivities of 92% and 93-94%. Calibration performance was good overall, but with heterogeneity between centers. Net Benefit confirmed clinical utility. The change in AUC when M6P was extended to include maternal age, bleeding score and history of EP was between -0.02 and 0.01, depending on center and phase. The corresponding change in AUC when M6NP was extended was between -0.01 and 0.03. At the 5% threshold to define high risk of EP/PPUL, extending M6P altered sensitivity by -0.02 to -0.01, specificity by 0.03 to 0.04 and Net Benefit by -0.005 to 0.006. Extending M6NP altered sensitivity by -0.03 to -0.01, specificity by 0.05 to 0.07 and Net Benefit by -0.005 to 0.006. CONCLUSIONS: The updated M6 model offers accurate diagnostic performance, with excellent sensitivity for EP. Adding clinical factors to the model improved performance in some centers, especially when progesterone levels were not suitable or unavailable. © 2023 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.

Evolutionary Patterns of Intersexual Power.

Dominance and leverage are both possible causes of social inequality. If sexual dimorphism influences patterns of intersexual dominance, we predicted that highly dimorphic species are constrained to exhibit male-biased power (MP), but species with low sexual dimorphism are free to demonstrate a broader range of intersexual power relationships. If market effects influence intersexual leverage, we predicted that females have more power when group composition is more male-biased and estrus is asynchronous. We analyzed data on intersexual power, sexual dimorphism, expected estrous overlap, and sex ratio for 79 extant primate species using phylogenetic logistic regression and ancestral state reconstructions. Although MP is more common, every major primate clade includes non-MP species. MP was associated with greater body mass and canine length dimorphism and with female-biased sex-ratios. Low estrous overlap was associated with non-MP. Although MP was reconstructed as likely ancestral for anthropoids, the last common ancestor of this clade probably did not exhibit high sexual dimorphism. The last common ancestor of catarrhines was probably highly dimorphic, potentially constraining intersexual power relationships. Non-MP probably evolved multiple times in primates and may be less common because multiple traits are linked to MP while fewer traits are associated with female-biased power or equality.

Using Computational Simulations to Model Deleterious Variation and Genetic Load in Natural Populations.

AbstractDeleterious genetic variation is abundant in wild populations, and understanding the ecological and conservation implications of such variation is an area of active research. Genomic methods are increasingly used to quantify the impacts of deleterious variation in natural populations; however, these approaches remain limited by an inability to accurately predict the selective and dominance effects of mutations. Computational simulations of deleterious variation offer a complementary tool that can help overcome these limitations, although such approaches have yet to be widely employed. In this perspective article, we aim to encourage ecological and conservation genomics researchers to adopt greater use of computational simulations to aid in deepening our understanding of deleterious variation in natural populations. We first provide an overview of the components of a simulation of deleterious variation, describing the key parameters involved in such models. Next, we discuss several approaches for validating simulation models. Finally, we compare and validate several recently proposed deleterious mutation models, demonstrating that models based on estimates of selection parameters from experimental systems are biased toward highly deleterious mutations. We describe a new model that is supported by multiple orthogonal lines of evidence and provide example scripts for implementing this model (https://github.com/ckyriazis/simulations_review).

Inference of the demographic histories and selective effects of human gut commensal microbiota over the course of human history.

Despite the importance of gut commensal microbiota to human health, there is little knowledge about their evolutionary histories, including their population demographic histories and their distributions of fitness effects (DFE) of new mutations. Here, we infer the demographic histories and DFEs of 27 of the most highly prevalent and abundant commensal gut microbial species in North Americans over timescales exceeding human generations using a collection of lineages inferred from a panel of healthy hosts. We find overall reductions in genetic variation among commensal gut microbes sampled from a Western population relative to an African rural population. Additionally, some species in North American microbiomes display contractions in population size and others expansions, potentially occurring at several key historical moments in human history. DFEs across species vary from highly to mildly deleterious, with accessory genes experiencing more drift compared to core genes. Within genera, DFEs tend to be more congruent, reflective of underlying phylogenetic relationships. Taken together, these findings suggest that human commensal gut microbes have distinct evolutionary histories, possibly reflecting the unique roles of individual members of the microbiome.

In vivo evaluation of Clostridioides difficile enoyl-ACP reductase II (FabK) Inhibition by phenylimidazole unveils a promising narrow-spectrum antimicrobial strategy.

Clostridioides difficile infection (CDI) is a leading cause of hospital-acquired diarrhea, which often stem from disruption of the gut microbiota by broad-spectrum antibiotics. The increasing prevalence of antibiotic-resistant C. difficile strains, combined with disappointing clinical trials results for recent antibiotic candidates, underscore the urgent need for novel CDI antibiotics. To this end, we investigated C. difficile enoyl ACP reductase (CdFabK), a crucial enzyme in de novo fatty acid synthesis, as a drug target for microbiome-sparing antibiotics. To test this concept, we evaluated the efficacy and in vivo spectrum of activity of the phenylimidazole analog 296, which is validated to inhibit intracellular CdFabK. Against major CDI-associated ribotypes 296 had an MIC90 of 2 µg/ml, which was comparable to vancomycin (1 µg/ml), a standard of care antibiotic. In addition, 296 achieved high colonic concentrations and displayed dosed-dependent efficacy in mice with colitis CDI. Mice that were given 296 retained colonization resistance to C. difficile and had microbiomes that resembled the untreated mice. Conversely, both vancomycin and fidaxomicin induced significant changes to mice microbiomes, in a manner consistent with prior reports. CdFabK therefore represents a potential target for microbiome-sparing CDI antibiotics, with phenylimidazoles providing a good chemical starting point for designing such agents.

Honey bee retinue workers respond similarly to queens despite seasonal differences in Queen Mandibular Pheromone (QMP) signaling.

Honey bee colonies maintain viable queens in part through communication with Queen Mandibular Pheromone (QMP), a mixture that signals the queen's presence and reproductive quality to workers. In turn, workers are thought to provide retinue queen care or replace queens partially based on QMP profiles. We examined the effects of seasonal dearth (overwintering in a warm subtropical location) on queen-worker interactions. Retinue worker responses to continuously ovipositing queens were considered in view of QMP signaling and queen reproductive quality. QMP signaling was estimated from QMP residues recovered from nest worker bodies, which is the primary mode of QMP transfer from the queen to the colony at large. QMP residues varied seasonally but not at all with queen reproductive quality (spermatheca sperm storage, ovary protein and lipid contents). 9-HDA and 9-ODA were lower in January than other months. HOB decreased from July to January, while HVA, a component associated with mated queens, increased sharply in January. Despite these seasonal signaling differences, retinue workers attended queens at similar levels through the months. In terms of reproductive quality, queens did not differ over the months in matedness (spermatheca sperm storage) or physiological age (protein carbonyl content), but varied in nutrient allocation to reproductive and non-reproductive tissues. Queen ovaries contained more protein in September than in November, and more lipid in July and September than in November and January. Queen fat bodies had more protein in July than September or November, but less lipid in July and September than November or January. Retinue worker responses did not vary with seasonal QMP changes, but reflected overall continuous brood rearing efforts and queen matedness throughout the year. The absence of seasonal differences in worker responses to QMP should be considered in the broader context of continuous reproductive efforts in warm subtropical colonies.

New specimens of middle Eocene omomyines (Primates, Omomyoidea) from the Uinta Basin of Utah and the Tornillo Basin of Texas, with clarification of the generic status of Ourayia, Mytonius, and Diablomomys.

In the middle Eocene, multiple lineages of North American omomyoids independently evolved body masses greater than 500 g. Most of these large-bodied omomyoids are known from small sample sizes, which has contributed to a lack of consensus regarding their alpha taxonomy. Here, we describe new Uintan omomyine specimens from the Uinta Basin of Utah and the Tornillo Basin of Texas. These new samples expand the hypodigms of Diablomomys dalquesti, Mytonius hopsoni, and Ourayia uintensis, and favor the recognition of new species of Mytonius and Ourayia based on specimens from the Tornillo Basin. These samples support the recognition of Diablomomys as a valid genus distinct from Omomys, Ourayia as a valid genus distinct from Macrotarsius, and Mytonius as a valid genus distinct from Ourayia. Although Diablomomys and Omomys co-occur in the late Uintan of the Tornillo Basin, Ourayia and Mytonius are time-successive taxa with a wide distribution across multiple Laramide basins. The data presented here reinforce the conclusion that the Uintan was a time period in which omomyines diversified to include a large number of taxa with body masses above Kay's threshold and frugivorous-folivorous diets. These data also provide evidence that North American primate faunas exhibited a shifting pattern of regional endemism during the middle Eocene. By the early Uintan, primate faunas from Southern California were already quite distinct from primate faunas of the central Rocky Mountains or Trans-Pecos Texas. By the late Uintan, primate faunas in all three regions demonstrated greater provincialism, with Trans-Pecos Texas and Southern California both exhibiting a large number of endemic primate taxa and sharing only a single primate genus (Macrotarsius) in common. This increase in primate endemism across the Uintan may be tied to changes in paleohabitats associated with the larger trend toward decreasing temperatures from the Early Eocene Climatic Optimum to the Eocene/Oligocene transition.

The signed helical angle: A technique for characterizing midfoot motion during gait.

Quantifying motion in the midfoot during gait and other movements is important for a variety of applications, but challenging due to the complexity of the multiple small articulations involved. The most common motion capture based techniques are limited in their ability to characterize the non-planar nature of the midfoot joint axes. In this study we developed a novel Signed Helical Angle (SHA) to quantify midfoot angular displacement. Motion capture data from 40 healthy subjects walking at a controlled speed were used to calculate finite helical axes and angles from a two-segment foot model. Axes were classified as either pronation or supination based on their orientation, and given a sign, thus either adding to or subtracting from the angular displacement. Analysis focused on insights from axis orientation and comparisons to other techniques. Results showed that when transitions were excluded, pronation and supination axes were fairly well clustered in the transverse plane. The resulting SHA midfoot angle waveform was comparable to sagittal plane Euler and helical component waveforms, but with 39% (approximately 3°) greater range of motion in pronation and 25% (approximately 4°) greater in supination, due to the direct measurement of the motion path and the influence of the other planes. The proposed SHA method may provide an intuitive and useful method to analyze midfoot motion for a variety of applications, particularly when interventions cause subtle changes that may be diluted in planar analyses.