Renal Outcomes With Tenofovir Alafenamide in Liver Transplant Recipients.

Tenofovir disoproxil fumarate (TDF) is associated with a higher risk of nephrotoxicity compared with entecavir (ETV) or tenofovir alafenamide (TAF).1,2 One-fifth of transplant recipients develop chronic kidney disease (CKD) within 5 years after transplantation, contributed by the use of nephrotoxic immunosuppressive medications.3 Prior studies conducted in the nontransplant setting reported superior renal safety in TAF compared with TDF but data in liver transplant (LT) recipients have so far been limited to small case series.1,4-6 Therefore, the goals of this study were to examine changes in renal function in a large multicenter cohort of LT recipients with chronic hepatitis B who were treated with TAF, TDF, or ETV for the prevention of hepatitis B virus (HBV) reinfection or reactivation from receipt of a positive HBV core antibody graft.

Racial and Ethnic Disparities in Characteristics and Care Patterns of Chronic Hepatitis B Patients in the United States.

BACKGROUND & AIMS: Chronic hepatitis B (CHB) disproportionately impacts foreign-born patients and those of Asian or Black race. Given the paucity of data, we aimed to study the impact of race and ethnicity on CHB patient characteristics and management. METHODS: A retrospective analysis of adult CHB patients using data recorded in the deidentified Optum Clinformatics Data Mart Database (January 2003‒March 2021) was performed. We characterized and examined the rates of receiving adequate treatment evaluation (measuring hepatitis B virus DNA and alanine transaminase) and hepatitis B virus treatment among the racial and ethnic groups. RESULTS: The study cohort included 42,140 patients: age, 51.9 ± 15.1 years; 56.1% male; 47% Asian; 26% White; 11% Black; and 7% Hispanic. Thirty-three percent of White and 48% of Asian patients had an annual household income greater than $100,000 US compared with 16% for Black and 25% for Hispanic patients (P < .001), with similar disparities in educational levels. Approximately one third of White (29.3%), Black (35.1%), and Hispanic (35.4%), and half of Asian (49.9%) patients received adequate evaluation (P < .001). Among patients who met American Association for the Study of Liver Diseases treatment criteria, treatment rates were similar among White (60.8%; P = .09) and Black (62.8%; P = .48), but lower among Hispanic (54.7%; P = .03), as compared with Asian patients (65.4%). On multivariable logistic regression adjusted for age, sex, provider type, viral co-infection, and fatty liver disease, Hispanic patients were less likely to receive treatment (adjusted hazard ratio, 0.69; 95% CI, 0.53‒0.91; P = .01) compared with Asian patients. CONCLUSIONS: Compared with Asian CHB patients, non-Asian patients were less likely to undergo adequate evaluation and Hispanic patients were less likely to receive treatment for CHB. Additional efforts are needed to improve CHB management, especially for non-Asian patients.

Predicting proteome allocation, overflow metabolism, and metal requirements in a model acetogen.

The unique capability of acetogens to ferment a broad range of substrates renders them ideal candidates for the biotechnological production of commodity chemicals. In particular the ability to grow with H2:CO2 or syngas (a mixture of H2/CO/CO2) makes these microorganisms ideal chassis for sustainable bioproduction. However, advanced design strategies for acetogens are currently hampered by incomplete knowledge about their physiology and our inability to accurately predict phenotypes. Here we describe the reconstruction of a novel genome-scale model of metabolism and macromolecular synthesis (ME-model) to gain new insights into the biology of the model acetogen Clostridium ljungdahlii. The model represents the first ME-model of a Gram-positive bacterium and captures all major central metabolic, amino acid, nucleotide, lipid, major cofactors, and vitamin synthesis pathways as well as pathways to synthesis RNA and protein molecules necessary to catalyze these reactions, thus significantly broadens the scope and predictability. Use of the model revealed how protein allocation and media composition influence metabolic pathways and energy conservation in acetogens and accurately predicted secretion of multiple fermentation products. Predicting overflow metabolism is of particular interest since it enables new design strategies, e.g. the formation of glycerol, a novel product for C. ljungdahlii, thus broadening the metabolic capability for this model microbe. Furthermore, prediction and experimental validation of changing secretion rates based on different metal availability opens the window into fermentation optimization and provides new knowledge about the proteome utilization and carbon flux in acetogens.

COBRAme: A computational framework for genome-scale models of metabolism and gene expression.

Genome-scale models of metabolism and macromolecular expression (ME-models) explicitly compute the optimal proteome composition of a growing cell. ME-models expand upon the well-established genome-scale models of metabolism (M-models), and they enable a new fundamental understanding of cellular growth. ME-models have increased predictive capabilities and accuracy due to their inclusion of the biosynthetic costs for the machinery of life, but they come with a significant increase in model size and complexity. This challenge results in models which are both difficult to compute and challenging to understand conceptually. As a result, ME-models exist for only two organisms (Escherichia coli and Thermotoga maritima) and are still used by relatively few researchers. To address these challenges, we have developed a new software framework called COBRAme for building and simulating ME-models. It is coded in Python and built on COBRApy, a popular platform for using M-models. COBRAme streamlines computation and analysis of ME-models. It provides tools to simplify constructing and editing ME-models to enable ME-model reconstructions for new organisms. We used COBRAme to reconstruct a condensed E. coli ME-model called iJL1678b-ME. This reformulated model gives functionally identical solutions to previous E. coli ME-models while using 1/6 the number of free variables and solving in less than 10 minutes, a marked improvement over the 6 hour solve time of previous ME-model formulations. Errors in previous ME-models were also corrected leading to 52 additional genes that must be expressed in iJL1678b-ME to grow aerobically in glucose minimal in silico media. This manuscript outlines the architecture of COBRAme and demonstrates how ME-models can be created, modified, and shared most efficiently using the new software framework.

Networks of energetic and metabolic interactions define dynamics in microbial communities.

Microorganisms form diverse communities that have a profound impact on the environment and human health. Recent technological advances have enabled elucidation of community diversity at high resolution. Investigation of microbial communities has revealed that they often contain multiple members with complementing and seemingly redundant metabolic capabilities. An understanding of the communal impacts of redundant metabolic capabilities is currently lacking; specifically, it is not known whether metabolic redundancy will foster competition or motivate cooperation. By investigating methanogenic populations, we identified the multidimensional interspecies interactions that define composition and dynamics within syntrophic communities that play a key role in the global carbon cycle. Species-specific genomes were extracted from metagenomic data using differential coverage binning. We used metabolic modeling leveraging metatranscriptomic information to reveal and quantify a complex intertwined system of syntrophic relationships. Our results show that amino acid auxotrophies create additional interdependencies that define community composition and control carbon and energy flux through the system while simultaneously contributing to overall community robustness. Strategic use of antimicrobials further reinforces this intricate interspecies network. Collectively, our study reveals the multidimensional interactions in syntrophic communities that promote high species richness and bolster community stability during environmental perturbations.

Antiviral Therapy Utilization and 10-Year Outcomes in Resected Hepatitis B Virus- and Hepatitis C Virus-Related Hepatocellular Carcinoma.

PURPOSE: There are limited data on antiviral treatment utilization and its impact on long-term outcomes of hepatitis B virus (HBV)- and hepatitis C virus (HCV)-related hepatocellular carcinoma (HCC) after hepatic resection. We aimed to determine the utilization and impact of antivirals in HBV- and HCV-related HCC. METHODS: This cohort study included 1,906 participants (1,054 HBV-related HCC and 852 HCV-related HCC) from 12 international sites. All participants had HBV- or HCV-related HCC and underwent curative surgical resection. The primary outcome was the utilization of antiviral therapy, and the secondary outcome was long-term overall survival (OS). RESULTS: The mean (±standard deviation [SD]) age was 62.1 (±11.3) years, 74% were male, and 84% were Asian. A total of 47% of the total cohort received antiviral therapy during a mean (±SD) follow-up of 5.0 (±4.3) years. The overall antiviral utilization for participants with HBV-related HCC was 57% and declined over time, from 65% before 2010, to 60% from 2010 to 2015, to 47% beyond 2015, P < .0001. The overall utilization of antivirals for HCV-related HCC was 35% and increased over time, from 24% before 2015 to 74% from 2015 and beyond, P < .0001. The 10-year OS was lower in untreated participants for both HBV (58% v 61%) and HCV participants (38% v 82%; both P < .0001). On multivariable Cox regression analysis adjusted for relevant confounders, antiviral therapy initiated before or within 6 months of HCC diagnosis was independently associated with lower mortality in both HBV- (adjusted hazard ratio [aHR], 0.60 [95% CI, 0.43 to 0.83]; P = .002) and HCV-related HCC (aHR, 0.18 [95% CI, 0.11 to 0.31]; P < .0001). CONCLUSION: Antiviral therapy is associated with long-term survival in people with HBV- or HCV-related HCC who undergo curative resection but is severely underutilized.

A simple agglutination system for rapid antigen detection from large sample volumes with enhanced sensitivity.

Lateral flow assays (LFAs) provide a simple and quick option for diagnosis and are widely adopted for point-of-care or at-home tests. However, their sensitivity is often limited. Most LFAs only allow 50 µL samples while various sample types such as saliva could be collected in much larger volumes. Adapting LFAs to accommodate larger sample volumes can improve assay sensitivity by increasing the number of target analytes available for detection. Here, a simple agglutination system comprising biotinylated antibody (Ab) and streptavidin (SA) is presented. The Ab and SA agglutinate into large aggregates due to multiple biotins per Ab and multiple biotin binding sites per SA. Dynamic light scattering (DLS) measurements showed that the agglutinated aggregate could reach a diameter of over 0.5 µm and over 1.5 µm using poly-SA. Through both experiments and Monte Carlo modeling, we found that high valency and equivalent concentrations of the two aggregating components were critical for successful agglutination. The simple agglutination system enables antigen capture from large sample volumes with biotinylated Ab and a swift transition into aggregates that can be collected via filtration. Combining the agglutination system with conventional immunoassays, an agglutination assay is proposed that enables antigen detection from large sample volumes using an in-house 3D-printed device. As a proof-of-concept, we developed an agglutination assay targeting SARS-CoV-2 nucleocapsid antigen for COVID-19 diagnosis from saliva. The assay showed a 10-fold sensitivity enhancement when increasing sample volume from 50 µL to 2 mL, with a final limit of detection (LoD) of 10 pg mL-1 (∼250 fM). The assay was further validated in negative saliva spiked with gamma-irradiated SARS-CoV-2 and showed an LoD of 250 genome copies per µL. The proposed agglutination assay can be easily developed from existing LFAs to facilitate the processing of large sample volumes for improved sensitivity.

Exosome Processing and Characterization Approaches for Research and Technology Development.

Exosomes are extracellular vesicles that share components of their parent cells and are attractive in biotechnology and biomedical research as potential disease biomarkers as well as therapeutic agents. Crucial to realizing this potential is the ability to manufacture high-quality exosomes; however, unlike biologics such as proteins, exosomes lack standardized Good Manufacturing Practices for their processing and characterization. Furthermore, there is a lack of well-characterized reference exosome materials to aid in selection of methods for exosome isolation, purification, and analysis. This review informs exosome research and technology development by comparing exosome processing and characterization methods and recommending exosome workflows. This review also provides a detailed introduction to exosomes, including their physical and chemical properties, roles in normal biological processes and in disease progression, and summarizes some of the on-going clinical trials.

Model-based assessment of mammalian cell metabolic functionalities using omics data.

Omics experiments are ubiquitous in biological studies, leading to a deluge of data. However, it is still challenging to connect changes in these data to changes in cell functions because of complex interdependencies between genes, proteins, and metabolites. Here, we present a framework allowing researchers to infer how metabolic functions change on the basis of omics data. To enable this, we curated and standardized lists of metabolic tasks that mammalian cells can accomplish. Genome-scale metabolic networks were used to define gene sets associated with each metabolic task. We further developed a framework to overlay omics data on these sets and predict pathway usage for each metabolic task. We demonstrated how this approach can be used to quantify metabolic functions of diverse biological samples from the single cell to whole tissues and organs by using multiple transcriptomic datasets. To facilitate its adoption, we integrated the approach into GenePattern (www.genepattern.org-CellFie).

GABA-modulating bacteria of the human gut microbiota.

The gut microbiota affects many important host functions, including the immune response and the nervous system1. However, while substantial progress has been made in growing diverse microorganisms of the microbiota2, 23-65% of species residing in the human gut remain uncultured3,4, which is an obstacle for understanding their biological roles. A likely reason for this unculturability is the absence in artificial media of key growth factors that are provided by neighbouring bacteria in situ5,6. In the present study, we used co-culture to isolate KLE1738, which required the presence of Bacteroides fragilis to grow. Bioassay-driven purification of B. fragilis supernatant led to the isolation of the growth factor, which, surprisingly, is the major inhibitory neurotransmitter GABA (γ-aminobutyric acid). GABA was the only tested nutrient that supported the growth of KLE1738, and a genome analysis supported a GABA-dependent metabolism mechanism. Using growth of KLE1738 as an indicator, we isolated a variety of GABA-producing bacteria, and found that Bacteroides ssp. produced large quantities of GABA. Genome-based metabolic modelling of the human gut microbiota revealed multiple genera with the predicted capability to produce or consume GABA. A transcriptome analysis of human stool samples from healthy individuals showed that GABA-producing pathways are actively expressed by Bacteroides, Parabacteroides and Escherichia species. By coupling 16S ribosmal RNA sequencing with functional magentic resonance imaging in patients with major depressive disorder, a disease associated with an altered GABA-mediated response, we found that the relative abundance levels of faecal Bacteroides are negatively correlated with brain signatures associated with depression.

Recon 2.2: from reconstruction to model of human metabolism.

INTRODUCTION: The human genome-scale metabolic reconstruction details all known metabolic reactions occurring in humans, and thereby holds substantial promise for studying complex diseases and phenotypes. Capturing the whole human metabolic reconstruction is an on-going task and since the last community effort generated a consensus reconstruction, several updates have been developed. OBJECTIVES: We report a new consensus version, Recon 2.2, which integrates various alternative versions with significant additional updates. In addition to re-establishing a consensus reconstruction, further key objectives included providing more comprehensive annotation of metabolites and genes, ensuring full mass and charge balance in all reactions, and developing a model that correctly predicts ATP production on a range of carbon sources. METHODS: Recon 2.2 has been developed through a combination of manual curation and automated error checking. Specific and significant manual updates include a respecification of fatty acid metabolism, oxidative phosphorylation and a coupling of the electron transport chain to ATP synthase activity. All metabolites have definitive chemical formulae and charges specified, and these are used to ensure full mass and charge reaction balancing through an automated linear programming approach. Additionally, improved integration with transcriptomics and proteomics data has been facilitated with the updated curation of relationships between genes, proteins and reactions. RESULTS: Recon 2.2 now represents the most predictive model of human metabolism to date as demonstrated here. Extensive manual curation has increased the reconstruction size to 5324 metabolites, 7785 reactions and 1675 associated genes, which now are mapped to a single standard. The focus upon mass and charge balancing of all reactions, along with better representation of energy generation, has produced a flux model that correctly predicts ATP yield on different carbon sources. CONCLUSION: Through these updates we have achieved the most complete and best annotated consensus human metabolic reconstruction available, thereby increasing the ability of this resource to provide novel insights into normal and disease states in human. The model is freely available from the Biomodels database (http://identifiers.org/biomodels.db/MODEL1603150001).

Reconstruction and modeling protein translocation and compartmentalization in Escherichia coli at the genome-scale.

BACKGROUND: Membranes play a crucial role in cellular functions. Membranes provide a physical barrier, control the trafficking of substances entering and leaving the cell, and are a major determinant of cellular ultra-structure. In addition, components embedded within the membrane participate in cell signaling, energy transduction, and other critical cellular functions. All these processes must share the limited space in the membrane; thus it represents a notable constraint on cellular functions. Membrane- and location-based processes have not yet been reconstructed and explicitly integrated into genome-scale models. RESULTS: The recent genome-scale model of metabolism and protein expression in Escherichia coli (called a ME-model) computes the complete composition of the proteome required to perform whole cell functions. Here we expand the ME-model to include (1) a reconstruction of protein translocation pathways, (2) assignment of all cellular proteins to one of four compartments (cytoplasm, inner membrane, periplasm, and outer membrane) and a translocation pathway, (3) experimentally determined translocase catalytic and porin diffusion rates, and (4) a novel membrane constraint that reflects cell morphology. Comparison of computations performed with this expanded ME-model, named iJL1678-ME, against available experimental data reveals that the model accurately describes translocation pathway expression and the functional proteome by compartmentalized mass. CONCLUSION: iJL1678-ME enables the computation of cellular phenotypes through an integrated computation of proteome composition, abundance, and activity in four cellular compartments (cytoplasm, periplasm, inner and outer membrane). Reconstruction and validation of the model has demonstrated that the iJL1678-ME is capable of capturing the functional content of membranes, cellular compartment-specific composition, and that it can be utilized to examine the effect of perturbing an expanded set of network components. iJL1678-ME takes a notable step towards the inclusion of cellular ultra-structure in genome-scale models.