"I realized I was not alone": A mixed-methods investigation of the implementation of Ubuntu groups to reduce burnout and social isolation in an allopathic medical School in the Southeastern United States.

PURPOSE/BACKGROUND: Healthcare providers experience higher rates of workplace burnout, a reality highlighted by the COVID-19 pandemic. In response, small groups, inspired by South African philosophy, Ubuntu, were introduced to decrease burnout and social isolation and build community and belonging. This study examines how participation in these groups can impact these measures. METHODS: In this mixed-methods study, trained facilitators led small groups that utilized story-sharing to foster connections within the group and broader community. Quantitative and qualitative data were analyzed separately and merged to identify convergence. RESULTS: Three main qualitative themes emerged: 1) seeking and building connections and community, 2) curiosity, learning, and growing, and 3) open-hearted and thriving. These themes were linked to quantitative outcomes, showing a statistically significant decrease in social isolation among staff/faculty and students. Furthermore, faculty/staff exhibited reduced burnout compared to students, while students reported increased feelings of belonging. CONCLUSION: Participation in Ubuntu groups positively influenced students' sense of belonging, reduced faculty/staff burnout, and alleviated social isolation for all participants. Future research should explore the potential of this intervention to further promote wellness on medical campuses. Programs emphasizing the well-being of individuals, including faculty, staff, and students, are crucial for supporting the overall health of medical communities and the wider society.

Research Education Program for Underrepresented Minority Students: Students' Perception of Academic Enrichment and Research Activities.

The Research Education Program (REP) is an NIH R25-funded training grant designed to increase the pipeline of underrepresented minority (URM) students entering graduate programs and pursuing biomedical research and health care careers. Each week, students participated in different academic enrichment activities during morning sessions. Research activities were during afternoon sessions. URM students presented their research findings in a local poster session with their peers, graduate medical students, and faculty members. They also attended national conferences to gain experience and expand their professional networks. Our participants included 14.3% rural, 42.85% suburban, and 42.85% urban students. Of this, 83.33% were females, while 16.67% were males. In addition, 100% of students indicated exceptional satisfaction in 64.0% of the academic enrichment activities offered by the REP, and 100% indicated exceptional satisfaction in 63.0% of the research activities. Future research will investigate the long-term effects of REP and graduate enrollments.

Modeling chemical interaction profiles: I. Spectral data-activity relationship and structure-activity relationship models for inhibitors and non-inhibitors of cytochrome P450 CYP3A4 and CYP2D6 isozymes.

An interagency collaboration was established to model chemical interactions that may cause adverse health effects when an exposure to a mixture of chemicals occurs. Many of these chemicals--drugs, pesticides, and environmental pollutants--interact at the level of metabolic biotransformations mediated by cytochrome P450 (CYP) enzymes. In the present work, spectral data-activity relationship (SDAR) and structure-activity relationship (SAR) approaches were used to develop machine-learning classifiers of inhibitors and non-inhibitors of the CYP3A4 and CYP2D6 isozymes. The models were built upon 602 reference pharmaceutical compounds whose interactions have been deduced from clinical data, and 100 additional chemicals that were used to evaluate model performance in an external validation (EV) test. SDAR is an innovative modeling approach that relies on discriminant analysis applied to binned nuclear magnetic resonance (NMR) spectral descriptors. In the present work, both 1D ¹³C and 1D ¹5N-NMR spectra were used together in a novel implementation of the SDAR technique. It was found that increasing the binning size of 1D ¹³C-NMR and ¹5N-NMR spectra caused an increase in the tenfold cross-validation (CV) performance in terms of both the rate of correct classification and sensitivity. The results of SDAR modeling were verified using SAR. For SAR modeling, a decision forest approach involving from 6 to 17 Mold2 descriptors in a tree was used. Average rates of correct classification of SDAR and SAR models in a hundred CV tests were 60% and 61% for CYP3A4, and 62% and 70% for CYP2D6, respectively. The rates of correct classification of SDAR and SAR models in the EV test were 73% and 86% for CYP3A4, and 76% and 90% for CYP2D6, respectively. Thus, both SDAR and SAR methods demonstrated a comparable performance in modeling a large set of structurally diverse data. Based on unique NMR structural descriptors, the new SDAR modeling method complements the existing SAR techniques, providing an independent estimator that can increase confidence in a structure-activity assessment. When modeling was applied to hazardous environmental chemicals, it was found that up to 20% of them may be substrates and up to 10% of them may be inhibitors of the CYP3A4 and CYP2D6 isoforms. The developed models provide a rare opportunity for the environmental health branch of the public health service to extrapolate to hazardous chemicals directly from human clinical data. Therefore, the pharmacological and environmental health branches are both expected to benefit from these reported models.

Modeling chemical interaction profiles: II. Molecular docking, spectral data-activity relationship, and structure-activity relationship models for potent and weak inhibitors of cytochrome P450 CYP3A4 isozyme.

Polypharmacy increasingly has become a topic of public health concern, particularly as the U.S. population ages. Drug labels often contain insufficient information to enable the clinician to safely use multiple drugs. Because many of the drugs are bio-transformed by cytochrome P450 (CYP) enzymes, inhibition of CYP activity has long been associated with potentially adverse health effects. In an attempt to reduce the uncertainty pertaining to CYP-mediated drug-drug/chemical interactions, an interagency collaborative group developed a consensus approach to prioritizing information concerning CYP inhibition. The consensus involved computational molecular docking, spectral data-activity relationship (SDAR), and structure-activity relationship (SAR) models that addressed the clinical potency of CYP inhibition. The models were built upon chemicals that were categorized as either potent or weak inhibitors of the CYP3A4 isozyme. The categorization was carried out using information from clinical trials because currently available in vitro high-throughput screening data were not fully representative of the in vivo potency of inhibition. During categorization it was found that compounds, which break the Lipinski rule of five by molecular weight, were about twice more likely to be inhibitors of CYP3A4 compared to those, which obey the rule. Similarly, among inhibitors that break the rule, potent inhibitors were 2-3 times more frequent. The molecular docking classification relied on logistic regression, by which the docking scores from different docking algorithms, CYP3A4 three-dimensional structures, and binding sites on them were combined in a unified probabilistic model. The SDAR models employed a multiple linear regression approach applied to binned 1D ¹³C-NMR and 1D ¹5N-NMR spectral descriptors. Structure-based and physical-chemical descriptors were used as the basis for developing SAR models by the decision forest method. Thirty-three potent inhibitors and 88 weak inhibitors of CYP3A4 were used to train the models. Using these models, a synthetic majority rules consensus classifier was implemented, while the confidence of estimation was assigned following the percent agreement strategy. The classifier was applied to a testing set of 120 inhibitors not included in the development of the models. Five compounds of the test set, including known strong inhibitors dalfopristin and tioconazole, were classified as probable potent inhibitors of CYP3A4. Other known strong inhibitors, such as lopinavir, oltipraz, quercetin, raloxifene, and troglitazone, were among 18 compounds classified as plausible potent inhibitors of CYP3A4. The consensus estimation of inhibition potency is expected to aid in the nomination of pharmaceuticals, dietary supplements, environmental pollutants, and occupational and other chemicals for in-depth evaluation of the CYP3A4 inhibitory activity. It may serve also as an estimate of chemical interactions via CYP3A4 metabolic pharmacokinetic pathways occurring through polypharmacy and nutritional and environmental exposures to chemical mixtures.

American Society for Pharmacology and Experimental Therapeutics Division for Pharmacology Education at EB2022-Meeting report.

The American Society for Pharmacology and Experimental Therapeutics (ASPET) held its annual meeting at the Experimental Biology 2022 conference in Philadelphia, PA on April 2-5, 2022. The authors provide a synopsis and discussion of each of the four sessions presented at the meeting under the ASPET Division for Pharmacology Education (DPE).

Gender Differences in Demographic and Pharmacological Factors in Patients Diagnosed with Late-Onset of Alzheimer's Disease.

BACKGROUND: Whether gender differences exist in late-onset of Alzheimer's disease (LOAD) treated with cholinesterase inhibitors (ChEIs) is not fully understood. This study investigated demographic and pharmacological characteristics in LOAD patients to determine gender differences in LOAD patients treated with ChEIs alone and ChEIs with other medications. METHODS: This 5-year retrospective data analysis included 9290 LOAD AD patients with 2949 men patients and 6341 women. Potential predictors of demographic and pharmacological characteristics associated gender differences in patients treated with and without ChEIs therapy were determined using univariate analysis, while multivariable models adjusted for demographic and pharmacological variables. RESULTS: In the adjusted analysis, men patients with LOAD that presented with a history of alcohol use (ETOH) (OR = 1.339, 95% CI, 1.072-1.672, p = 0.010), treated with second generation antipsychotics (SGAs) (OR = 1.271, 95% CI, 1.003-1.610, p = 0.047), citalopram (OR = 5.103, 95% CI, 3.423-7.607, p < 0.001), memantine (OR = 4.409, 95% CI, 3.704-5.249, p < 0.001), and buspirone (OR = 2.166, 95% CI, 1.437-3.264, p < 0.001) were more likely to receive ChEIs therapy, whereas older men were less likely to be treated with ChEIs therapy. Women who were African Americans (OR = 1.387, 95% CI, 1.168-1.647, p < 0.001), that received memantine (OR = 3.412, 95% CI, 3.034-3.837, p < 0.001), selective serotonin reuptake inhibitor (SSRIs) (OR = 1.143, 95% CI, 1.016-1.287, p = 0.026), and a history of ETOH (OR = 2.109, 95% CI, 1.724-2.580, p < 0.001) were more likely to receive ChEIs therapy, whereas older women were less likely to receive ChEIs therapy. CONCLUSION: In both men and women patients, those with increasing age were less likely to be treated with ChEI therapy, while patients treated with memantine were also likely to receive ChEI therapy. Our findings highlight the importance for clinicians to optimize ChEI in LOAD to improve treatment effectiveness and eliminate gender differences in ChEI therapy.

Sex Differences in Demographic and Pharmacological Factors in Alzheimer Patients With Dementia and Cognitive Impairments.

Objective: The current study investigates sex differences associated with pharmacological and demographic characteristics in Alzheimer patients (AD) with dementia (ADD) or mild cognitive impairment (MCI). Method: A retrospective analytical approach was used to analyze data from 45,696 AD patients with MCI or ADD. The univariate analysis was used to determine differences in demographic, and pharmacological characteristics for male and female ADD and MCI-AD patients. Multivariate analysis was used to predict specific pharmacological and demographic factors that are associated with male and female MCI and ADD patients. Result: In the adjusted analysis for male patients, Hispanics [0.166,0.020 - 1.355, P = 0.094] or African Americans [OR = 2.380, 95% CI,2.120 - 2.674, P < 0.001], were more likely to have MCI-AD and be treated with galantamine [OR = 0.559, 95% CI, 0.382 - 0.818, P = 0.003], donepezil [OR = 1.639, 95% CI,1.503 - 1.787, P < 0.001], rivastigmine [OR = 1.394, 95% CI,1.184 - 1.642, P < 0.001], olanzapine [OR = 2.727, 95% CI,2.315 - 3.212, P < 0.001], risperidone [OR = 2.973, 95% CI,2.506 - 3.526, P < 0.001], present with increasing age [1.075,1.071 - 1.079, P < 0.001], and are on tobacco use [OR = 1.150, 95% CI,1.054 - 1.254, P = 0.002]. For female patients, buspirone [OR = 0.767, 95% CI, 0.683 - 0.861, P < 0.001] and a history of alcohol (ETOH) use [OR = 0.484, 95% CI, 0.442 - 0.529, P < 0.001] were associated with MCI-AD. Increasing age [OR = 1.096, 95% CI, 1.093 - 1.100, P < 0.001], donepezil [OR = 2.185, 95% CI, 2.035 - 2.346, P < 0.001], memantine [OR = 2.283, 95% CI, 2.104 - 2.477, P < 0.001] aripiprazole [OR = 1.807, 95% CI, 1.544 - 2.113, P < 0.001] olanzapine [OR = 2.289, 95% CI, 1.986 - 2.640, P < 0.001] risperidone [OR = 2.548, 95% CI, 2.246 - 2.889, P < 0.001] buspirone [OR = 0.767, 95% CI, 0.683 - 0.861, P < 0.001] escitalopram [OR = 1.213, 95% CI,1.119 - 1.315, P < 0.001] African Americans [OR = 1.395, 95% CI, 1.268 - 1.535, P < 0.001] and tobacco use [OR = 1.150, 95% CI, 1.073 - 1.233, P < 0.001] were associated with ADD. Conclusion: Our findings reveal that MCI-AD patients were more likely to be Hispanics or African American males treated with rivastigmine, olanzapine and citalopram. African American females were associated with ADD and more likely to be treated with buspirone and presented with a history of ETOH. This finding suggests the need for a pharmacological treatment approach encompassing sex-sensitive strategies for MCI-AD and ADD patients.

Physiologically-Based Pharmacokinetic Modeling to Investigate the Effect of Maturation on Buprenorphine Pharmacokinetics in Newborns with Neonatal Opioid Withdrawal Syndrome.

Neonatal opioid withdrawal syndrome (NOWS) is a major public health concern whose incidence has paralleled the opioid epidemic in the United States. Sublingual buprenorphine is an emerging treatment for NOWS, but given concerns about long-term adverse effects of perinatal opioid exposure, precision dosing of buprenorphine is needed. Buprenorphine pharmacokinetics (PK) in newborns, however, is highly variable. To evaluate underlying sources of PK variability, a neonatal physiologically-based pharmacokinetic (PBPK) model of sublingual buprenorphine was developed using Simcyp (version 19.1). The PBPK model included metabolism by cytochrome P450 (CYP) 3A4, CYP2C8, UDP-glucuronosyltransferase (UGT) 1A1, UGT1A3, UGT2B7, and UGT2B17, with additional biliary excretion. Maturation of metabolizing enzymes was incorporated, and default CYP2C8 and UGT2B7 ontogeny profiles were updated according to recent literature. A biliary clearance developmental profile was outlined using clinical data from neonates receiving sublingual buprenorphine as NOWS treatment. Extensive PBPK model validation in adults demonstrated good predictability, with geometric mean (95% confidence interval (CI)) predicted/observed ratios (P/O ratios) of area under the curve from zero to infinity (AUC0-∞ ), peak concentration (Cmax ), and time to reach peak concentration (Tmax ) equaling 1.00 (0.74-1.33), 1.04 (0.84-1.29), and 0.95 (0.72-1.26), respectively. In neonates, the geometric mean (95% CI) P/O ratio of whole blood concentrations was 0.75 (95% CI 0.64-0.87). PBPK modeling and simulation demonstrated that variability in biliary clearance, sublingual absorption, and CYP3A4 abundance are likely important drivers of buprenorphine PK variability in neonates. The PBPK model could be used to guide development of improved buprenorphine starting dose regimens for the treatment of NOWS.

Opioid Treatment for Neonatal Opioid Withdrawal Syndrome: Current Challenges and Future Approaches.

Chronic intrauterine exposure to psychoactive drugs often results in neonatal opioid withdrawal syndrome (NOWS). When nonpharmacologic measures are insufficient in controlling NOWS, morphine, methadone, and buprenorphine are first-line medications commonly used to treat infants with NOWS because of in utero exposure to opioids. Research suggests that buprenorphine may be the leading drug therapy used to treat NOWS when compared with morphine and methadone. Currently, there are no consensus or standardized treatment guidelines for medications prescribed for NOWS. Opioids used to treat NOWS exhibit large interpatient variability in pharmacokinetics (PK) and pharmacodynamic (PD) response in neonates. Organ systems undergo rapid maturation after birth that may alter drug disposition and exposure for any given dose during development. Data regarding the PK and PD of opioids in neonates are sparse. Pharmacometric methods such as physiologically based pharmacokinetic and population pharmacokinetic modeling can be used to explore factors predictive of some of the variability associated with the PK/PD of opioids in newborns. This review discusses the utility of pharmacometric techniques for enhancing precision dosing in infants requiring opioid treatment for NOWS. Applying these approaches may contribute to optimizing the outcome by reducing cumulative drug exposure, mitigating adverse drug effects, and reducing the burden of NOWS in neonates.

An Adaptive Blended Learning Model for the Implementation of an Integrated Medical Neuroscience Course During the Covid-19 Pandemic.

The implementation of an integrated medical neuroscience course by technologically pivoting an in-person neuroscience course to online using an adaptive blended method may provide a unique approach for teaching a medical neuroscience course during the Covid-19 pandemic. An adaptive blended learning method was developed in response to the requirements necessitated by the Covid-19 pandemic. This model combined pedagogical needs with digital technology using online learning activities to implement student learning in a medical neuroscience course for year one medical students. This approach provided medical students with an individually customized learning opportunity in medical neuroscience. The students had the complete choice to engage the learning system synchronously or asynchronously and learn neuroscience materials at different locations and times in response to the demands required to deal with the pandemic. Students' performance in summative and formative examinations of the adaptive blended learning activities were compared with the previous performance obtained the previous year when the contents of the medical neuroscience course were implemented using the conventional "face-to-face" learning approach. While the cohort of our students in 2019 and 2020 changed, the contents, sessions, volume of material, and assessment were constant. This enabled us to compare the results of the 2019 and 2020 classes. Overall, students' performance was not significantly different between the adaptive blended learning and the in-person approach. More students scored between 70% and 79% during the adaptive blended learning compared with in-class teaching, while more students scored between 80% and 89% during the in-person learning than during the adaptive blended learning. Finally, the percentage of students that scored >90% was not significantly different for both Years 2019 and 2020. The adaptive blended learning approach was effective in enhancing academic performance for high-performing medical students. It also permitted the early identification of underachieving students, thereby serving as an early warning sign to permit timely intervention.

Physiologic Indirect Response Modeling to Describe Buprenorphine Pharmacodynamics in Newborns Treated for Neonatal Opioid Withdrawal Syndrome.

BACKGROUND AND OBJECTIVE: Buprenorphine has been shown to be effective in treating infants with neonatal opioid withdrawal syndrome. However, an evidence-based buprenorphine dosing strategy has not been established in the treatment of neonatal opioid withdrawal syndrome because of a lack of exposure-response data. The aim of this study was to develop an integrated pharmacokinetic and pharmacodynamic model to predict buprenorphine treatment outcomes in newborns with neonatal opioid withdrawal syndrome. METHODS: Clinical data were obtained from 19 newborns with a median (range) gestational age of 37 (34-41) weeks enrolled in a pilot pharmacokinetic study of buprenorphine. Sparse blood sampling, comprising three specimens obtained around the second dose of buprenorphine, was performed using heel sticks with dried blood spot technology. Standardized neonatal opioid withdrawal syndrome severity scores (Finnegan scores) were collected every 3-4 h based on symptoms by bedside nursing staff. Mean Finnegan scores were used as a pharmacodynamic marker in the exposure-response modeling. The blood concentration-Finnegan score relationship was described using a physiologic indirect response model with inclusion of natural disease remission. RESULTS: A total of 52 buprenorphine blood concentrations and 780 mean Finnegan scores were available for the pharmacokinetic/pharmacodynamic modeling and exposure-response analysis. A one-compartment model with first-order absorption adequately described the pharmacokinetic data. The buprenorphine blood concentration at 50% of maximum effect for the inhibition of disease progression was 0.77 ng/mL (95% confidence interval 0.32-1.2). The inclusion of natural disease remission described as a function of postnatal age significantly improved the model fit. CONCLUSIONS: A buprenorphine pharmacokinetic/pharmacodynamic model was successfully developed. The model could facilitate model-informed optimization of the buprenorphine dosing regimen in the treatment of neonatal opioid withdrawal syndrome.

Pharmacotherapy of neonatal opioid withdrawal syndrome: a review of pharmacokinetics and pharmacodynamics.

INTRODUCTION: Neonatal opioid withdrawal syndrome (NOWS) often arises in infants born to mothers who used opioids during pregnancy. Morphine, methadone, and buprenorphine are the most common first-line treatments, whereas clonidine and phenobarbital are generally reserved for adjunctive therapy. These drugs exhibit substantial pharmacokinetic (PK) and pharmacodynamic (PD) variability. Current pharmacological treatments for NOWS are based on institutional protocols and largely rely on empirical treatment of patient symptoms. AREAS COVERED: This article reviews the PK/PD of NOWS pharmacotherapies with a focus on the implication of physiological development and maturation. Body size-standardized clearance is consistently low in neonates, except for methadone. This can be ascribed to underdeveloped metabolic and elimination pathways. The effects of pharmacogenetics have been clarified especially for morphine. The PK/PD relationship of medications used in the treatment of NOWS is generally understudied. EXPERT OPINION: Providing an appropriate opioid dose in neonates is challenging. Advancements in quantitative pharmacology and PK/PD modeling approaches facilitate identification of key factors driving PK/PD variability and characterization of exposure-response relationships. PK/PD model-informed simulations have been widely employed to define age-appropriate pediatric dosing regimens. The model-informed approach holds promise to aid more rational use of medications in the treatment of NOWS.

Utilizing Pediatric Physiologically Based Pharmacokinetic Models to Examine Factors That Contribute to Methadone Pharmacokinetic Variability in Neonatal Abstinence Syndrome Patients.

Chronic intrauterine exposure to psychoactive drugs often results in neonatal abstinence syndrome (NAS). NAS is the symptomatic drug withdrawal in newborns that generally occurs after in utero chronic opioid exposure. Methadone is an opioid analgesic commonly prescribed for pharmacologic management of NAS. It exhibits high pharmacokinetic (PK) variability. The current study used physiologically based PK modeling to predict the PK profile of methadone in 20 newborns treated for NAS. The physiologically based PK simulations adequately predicted the PK profile of the clinical data for 45% of the patients. Sensitivity analyses were conducted to explore contributing factors to methadone PK variability. The data suggest that P450 enzymatic activity impacts the clearance of methadone in virtual adults and neonates, while the contribution of cardiac output may be negligible. Understanding maturational and/or pharmacogenetic changes in cytochrome P450 enzymatic activity may further explain the large PK variability of methadone in newborns with NAS and will help individualized treatment.

Using a Vancomycin PBPK Model in Special Populations to Elucidate Case-Based Clinical PK Observations.

Simultaneous changes in several physiological factors may contribute to the large pharmacokinetic (PK) variability of vancomycin. This study was designed to systematically characterize the effects of multiple physiological factors to the altered PK of vancomycin observed in special populations. A vancomycin physiologically based pharmacokinetic (PBPK) model was developed as a PK simulation platform to quantitatively assess the effects of changes in physiologies to the PK profiles. The developed model predicted the concentration-time profiles in healthy adults and diseased patients. The implementation of developmental changes in both renal and non-renal elimination pathways to the pediatric model improved the predictability of vancomycin clearance. Simulated PK profiles with a 50% decrease in cardiac output (peak plasma concentration (Cmax ), 59.9 ng/mL) were similar to those observed in patients before bypass surgery (Cmax , 55.1 ng/mL). The PBPK modeling of vancomycin demonstrated its potential to provide mechanistic insights into the altered disposition observed in patients who have changes in multiple physiological factors.

The immature rat as a potential model for chemical risks to children: Ontogeny of selected hepatic P450s.

Concern about potential susceptibilities of infants and children to chemicals has led to the consideration of immature rodents as potential test surrogates. Maturation of some hepatic microsomal cytochrome P450s (CYPs), that participate in metabolic activation of organic solvents and polycyclic aromatic hydrocarbons (PAHs), may differ significantly between humans and rodents. The present investigation was undertaken to delineate the ontogeny of selected hepatic CYPs in male and female Sprague-Dawley (S-D) rats, and to contrast them with developmental profiles in humans. Microsomes were prepared from the liver of sexed and unsexed postnatal day (PND) 1-90 rats, and total CYP450 levels, as well as CYP1A1/2, CYP2E1 and CYP2B1/2 activities and protein, were quantified. CYP1A1/2 and CYP2E1 activity and expression rose rapidly after birth, peaked from PND 21-40/50, and declined substantially to adult values by PND 90. The same ontogenic profiles were manifested when the enzyme activities were expressed per entire liver or liver normalized to body weight. CYP1A1/2 and CYP2E1 activity and protein expression were well correlated. CYP2B1/2 activity peaked abruptly on PND 21 and declined irregularly to adult values. These patterns are in contrast to human CYP1A2 and CYP2E1, which are reported to progressively increase in liver during the first few months to years of life. The three CYP protein developmental profiles were largely gender independent in rats. The immature rat does not appear to be a suitable model for assessing risks posed to infants and children by chemicals metabolically activated by CYP2E1, based on the findings of greater carbon tetrachloride hepatotoxicity in preweanlings and weanlings than in adult animals. Additional studies are required to determine whether immature S-D rats may be used as an animal model for substrates of other CYPs, as total CYP450 levels in the liver progressively rose during maturation, similarly to humans.

Molecular characterization of PoGT8D and PoGT43B, two secondary wall-associated glycosyltransferases in poplar.

Dicot wood is mainly composed of cellulose, lignin and glucuronoxylan (GX). Although the biosynthetic genes for cellulose and lignin have been studied intensively, little is known about the genes involved in the biosynthesis of GX during wood formation. Here, we report the molecular characterization of two genes, PoGT8D and PoGT43B, which encode putative glycosyltransferases, in the hybrid poplar Populus alba x tremula. The predicted amino acid sequences of PoGT8D and PoGT43B exhibit 89 and 75% similarity to the Arabidopsis thaliana IRREGULAR XYLEM8 (IRX8) and IRX9, respectively, both of which have been shown to be required for GX biosynthesis. The PoGT8D and PoGT43B genes were found to be expressed in cells undergoing secondary wall thickening, including the primary xylem, secondary xylem and phloem fibers in stems, and the secondary xylem in roots. Both PoGT8D and PoGT43B are predicted to be type II membrane proteins and shown to be targeted to Golgi. Overexpression of PoGT43B in the irx9 mutant was able to rescue the defects in plant size and secondary wall thickness and partially restore the xylose content. Taken together, our results demonstrate that PoGT8D and PoGT43B are Golgi-localized, secondary wall-associated proteins, and PoGT43B is a functional ortholog of IRX9 involved in GX biosynthesis during wood formation.

The PARVUS gene is expressed in cells undergoing secondary wall thickening and is essential for glucuronoxylan biosynthesis.

Xylan, cellulose and lignin are the three major components of secondary walls in wood, and elucidation of the biosynthetic pathway of xylan is of importance for potential modification of secondary wall composition to produce wood with improved properties. So far, three Arabidopsis glycosyltransferases, FRAGILE FIBER8, IRREGULAR XYLEM8 and IRREGULAR XYLEM9, have been implicated in glucuronoxylan (GX) biosynthesis. In this study, we demonstrate that PARVUS, which is a member of family GT8, is required for the biosynthesis of the tetrasaccharide primer sequence, beta-D-Xyl-(1 --> 3)-alpha-l-Rha-(1 --> 2)-alpha-D-GalA-(1 --> 4)-D-Xyl, located at the reducing end of GX. The PARVUS gene is expressed during secondary wall biosynthesis in fibers and vessels, and its encoded protein is predominantly localized in the endoplasmic reticulum. Mutation of the PARVUS gene leads to a drastic reduction in secondary wall thickening and GX content. Structural analysis of GX using (1)H-nuclear magnetic resonance (NMR) spectroscopy revealed that the parvus mutation causes a loss of the tetrasaccharide primer sequence at the reducing end of GX and an absence of glucuronic acid side chains in GX. Activity assay showed that the xylan xylosyltransferase and glucuronyltransferase activities were not affected in the parvus mutant. Together, these findings implicate a possible role for PARVUS in the initiation of biosynthesis of the GX tetrasaccharide primer sequence and provide novel insights into the mechanisms of GX biosynthesis.