Management of HBV/HIV-coinfected Patients

Coinfection with hepatitis B virus (HBV) and human immunodeficiency virus (HIV) is common as a result of shared routes of transmission, especially in high-risk groups such as injection drug users and persons with hemophilia. HIV is known to influence the natural history of HBV, hastening progression to end-stage liver disease and cirrhosis. Antiretroviral therapy for HIV, and associated immune reconstitution, may result in immune-mediated liver damage as HBV-infected hepatocytes are targeted. This can lead to liver enzyme elevations that may be misattributed to drug-related toxicity. Thus, it is important that HIV-infected patients be tested for HBV and that clinicians be aware of the possibility of atypical serologic markers of HBV in HIV-infected patients. In managing coinfected patients, control of HIV is the priority. In patients with controlled HIV who are candidates for HBV therapy, the goals are the same as in the HBV-monoinfected population: hepatitis B e antigen seroconversion, liver enzyme normalization, and HBV DNA suppression. Treatment options include interferon-based regimens, lamivudine, adefovir, tenofovir, and entecavir. All of these agents have been shown to be relatively effective in HBV-monoinfected patients. However, few randomized, controlled HBV treatment trials have been conducted in coinfected subjects, and thus additional studies are warranted.     

Outcomes of donor evaluation in adult-to-adult living donor liver transplantation

The purpose of donor evaluation for adult-to-adult living donor liver transplantation (LDLT) is to discover medical conditions that could increase the donor postoperative risk of complications and to determine whether the donor can yield a suitable graft for the recipient. We report the outcomes of LDLT donor candidates evaluated in a large multicenter study of LDLT. The records of all donor candidates and their respective recipients between 1998 and 2003 were reviewed as part of the Adult-to-Adult Living Donor Liver Transplantation Cohort Study (A2ALL). The outcomes of the evaluation were recorded along with demographic data on the donors and recipients. Of the 1011 donor candidates evaluated, 405 (40%) were accepted for donation. The donor characteristics associated with acceptance (P < 0.05) were younger age, lower body mass index, and biological or spousal relationship to the recipient. Recipient characteristics associated with donor acceptance were younger age, lower Model for End-stage Liver Disease score, and shorter time from listing to first donor evaluation. Other predictors of donor acceptance included earlier year of evaluation and transplant center. CONCLUSION: Both donor and recipient features appear to affect acceptance for LDLT. These findings may aid the donor evaluation process and allow an objective assessment of the likelihood of donor candidate acceptance.     

Kidney and liver transplantation in HIV-infected patients: case presentations and review

Until recently, HIV-infected patients have been excluded from consideration for solid organ transplantation. The relatively high mortality rates among HIV-infected transplant recipients observed in the era prior to the use of highly active antiretroviral therapy (HAART), coupled with long waiting times for cadaveric organs, made it difficult to support organ transplantation in this patient group. However, in response to the marked reductions in morbidity and mortality associated with HIV infection, several transplant centers have developed pilot studies or revised their clinical criteria to allow transplantation in this group of patients. We describe two cases, one kidney and one liver transplant recipient, and review the major clinical and research issues related to this topic. Reports of transplantations in the pre-HAART era highlight two important findings. First, some HIV-infected transplant recipients did very well with long survival periods. However, overall progression to AIDS and death appeared accelerated. We recently reported on our preliminary experience with 45 selected transplant recipients in the HAART era. One-year patient survival rates were similar to unmatched survival data from the United Network for Organ Sharing (UNOS) database. Median CD4+ T-cell counts remained stable in the follow-up period compared to pretransplant. HIV-1 RNA nearly uniformly continued to be suppressed below the limits of detection. Preliminary data are promising and support the current efforts to evaluate patient and graft survival among HIV-infected transplant recipients and to explore the mechanisms underlying the many potential complications of transplantation in this population.     

Hepatitis B virus genotypes in the United States: results of a nationwide study

BACKGROUND & AIMS: Hepatitis B virus (HBV) genotypes may be related to severity of liver disease and treatment response. The aims of this nationwide study were to determine the prevalence of HBV genotypes in the United States and the association between HBV genotypes and patient demographics, mode of infection, and clinical status. METHODS: A total of 694 consecutive chronic HBV-infected patients seen in 17 U.S. liver centers during a 1-year period were enrolled. Demographic, clinical, and laboratory data were collected. Sera were tested for HBV genotyping, precore, and core promoter variants by line-probe assays. RESULTS: All 7 HBV genotypes (A-G) were found, with genotypes A and C the most common. The prevalence of HBV genotypes was different in different regions of the United States. A strong correlation was found between HBV genotypes and ethnicity. HBV genotype A was prevalent among white and black patients, whereas genotypes B and C were most common among Asian patients. The predominant genotype among patients born in the United States, Europe, the Far East, and Southeast Asia were A, D, C, and B, respectively. Genotypes A and C were associated with a higher prevalence of hepatitis B e antigen. Precore variant was detected in 27% of patients and core promoter variant in 44% of patients. CONCLUSIONS: Our study suggests that the epidemiology of HBV infection in the United States may have changed over time as a result of immigration from countries with a high prevalence of HBV infection. HBV genotypes may account for the heterogeneity in disease manifestations among patients with chronic HBV infection.     

Treating chronic hepatitis B infection in patients who are pregnant or are undergoing immunosuppressive chemotherapy

As our understanding of the natural history of hepatitis B virus (HBV) infection increases, so do the patient circumstances for which anti-HBV therapy is considered. For example, patients with chronic HBV infection that is negative for hepatitis B surface antigen can experience hepatitis flares during or after cytotoxic chemotherapy and thus are potential candidates for anti-HBV therapy. Also, although passive-active immunoprophylaxis is highly effective in preventing the vertical transmission of HBV, high maternal serum HBV DNA concentrations have been associated with the failure of immunoprophylaxis; for this reason, clinicians may consider administering anti-HBV therapy during pregnancy. However, prophylactic anti-HBV therapy can be both complex and controversial. A satellite symposium conducted during the 57th Annual Meeting of the American Association for the Study of Liver Diseases (AASLD) in Boston, Massachusetts, presented approaches to treating HBV infection in patients who are pregnant and in those who are preparing to receive chemotherapy.     

5-HT4 and 5-HT2 receptors antagonistically influence gap junctional coupling between rat auricular myocytes

5-hydroxytryptamine-4 (5-HT₄) receptors have been proposed to contribute to the generation of atrial fibrillation in human atrial myocytes, but it is unclear if these receptors are present in the hearts of small laboratory animals (e.g. rat). In this study, we examined presence and functionality of 5-HT₄ receptors in auricular myocytes of newborn rats and their possible involvement in regulation of gap junctional intercellular communication (GJIC, responsible for the cell-to-cell propagation of the cardiac excitation). Western-blotting assays showed that 5-HT₄ receptors were present and real-time RT-PCR analysis revealed that 5-HT_4b was the predominant isoform. Serotonin (1 μM) significantly reduced cAMP concentration unless a selective 5-HT₄ inhibitor (GR113808 or ML10375, both 1 μM) was present. Serotonin also reduced the amplitude of L-type calcium currents and influenced the strength of GJIC without modifying the phosphorylation profiles of the different channel-forming proteins or connexins (Cxs), namely Cx40, Cx43 and Cx45. GJIC was markedly increased when serotonin exposure occurred in presence of a 5-HT₄ inhibitor but strongly reduced when 5-HT_2A and 5-HT_2B receptors were inhibited, showing that activation of these receptors antagonistically regulated GJIC. The serotoninergic response was completely abolished when 5-HT₄, 5-HT_2A and 5-HT_2B were simultaneously inhibited. A 24 h serotonin exposure strongly reduced Cx40 expression whereas Cx45 was less affected and Cx43 still less. In conclusion, this study revealed that 5-HT₄ (mainly 5-HT_4b), 5-HT_2A and 5-HT_2B receptors coexisted in auricular myocytes of newborn rat, that 5-HT₄ activation reduced cAMP concentration, I_CaL and intercellular coupling whereas 5-HT_2A or 5-HT_2B activation conversely enhanced GJIC.     

Multidisciplinary management of hepatocellular carcinoma

Hepatocellular carcinoma is a leading cause of death in patients with cirrhosis. Management algorithms continually are increasing in sophistication and involve application of single and multimodality treatments, including liver transplantation, hepatic resection, ablation, transarterial chemoembolization, radioembolization, and systemic chemotherapy. These treatments have been shown to increase survival times. As many as 75% of patients with limited-stage disease who are given curative therapies survive 5 years, whereas less than 20% of untreated patients survive 1 year. Treatment can be optimized based on the patient's tumor stage, hepatic reserve, and functional status. However, because of the heterogeneity in presentation among patients, a multidisciplinary approach is required to treat hepatocellular carcinoma, involving hepatologists, surgeons, interventional radiologists, and oncologists. We present each specialist's viewpoint on controversies and advances in the management of hepatocellular carcinoma.     

A machine-learning approach to map landscape connectivity in Aedes aegypti with genetic and environmental data

Mapping landscape connectivity is important for controlling invasive species and disease vectors. Current landscape genetics methods are often constrained by the subjectivity of creating resistance surfaces and the difficulty of working with interacting and correlated environmental variables. To overcome these constraints, we combine the advantages of a machine-learning framework and an iterative optimization process to develop a method for integrating genetic and environmental (e.g., climate, land cover, human infrastructure) data. We validate and demonstrate this method for the Aedes aegypti mosquito, an invasive species and the primary vector of dengue, yellow fever, chikungunya, and Zika. We test two contrasting metrics to approximate genetic distance and find Cavalli-Sforza–Edwards distance (CSE) performs better than linearized F_ST. The correlation (R) between the model’s predicted genetic distance and actual distance is 0.83. We produce a map of genetic connectivity for Ae. aegypti’s range in North America and discuss which environmental and anthropogenic variables are most important for predicting gene flow, especially in the context of vector control.

Landscape genetics—explicitly quantifying the effects of a heterogenous landscape on gene flow—is an important tool for both conservation biology and the control of invasive species and disease vectors including the “yellow fever mosquito” (Aedes aegypti) (1, 2). We demonstrate that current limitations in landscape genetics can be addressed with a machine-learning approach integrated into an iterative optimization process. Isolation by distance (IBD) is a classical model in population genetics that assumes dispersal is limited in proportion to geographic distance, resulting in increasing genetic differentiation with increasing geographic distance between populations (3–5). Although this pattern is commonly seen in nature, factors such as history and dispersal limitations caused by the environment (i.e., “isolation by resistance”) (6) can produce deviations from IBD. Landscape resistance (alias friction) and its inverse, connectivity, determine how organisms move through a landscape (7). Modeling landscape connectivity can be used to identify the environmental variables that affect the organisms’ gene flow and genetic structure; predict how climate and land use change will affect their gene flow and distribution in the future; and inform conservation, vector control, and other management decisions (1, 8–13). Our goals are to use environmental data (the predictors) to build a model of genetic connectivity (the observed data) that improves on IBD and to identify environmental drivers of gene flow patterns.We implement a machine-learning approach that offers a number of advantages over classical methods in landscape genetics: The machine-learning approach is more objective, it allows the inclusion of correlated variables, and it is able to account for different shapes and magnitudes of correlations between predictor and response variables at different locations in the landscape (14–17). In comparison, a common approach in landscape genetics called resistance surface mapping involves the subjective process of creating resistance surfaces for environmental variables, in which each pixel represents a hypothesized resistance to the organism’s movement often based on expert opinion (6, 18). Effective landscape distances through the resistance surfaces can be found with least cost path or circuit theory analysis (19) and then analyzed for associations with genetic distance (20).One option to circumvent the subjectivity of creating resistance surfaces is to model genetic connectivity directly from environmental data. Bouyer et al. (7) took this approach and used a maximum-likelihood method to integrate genetic data and environmental data to map landscape resistance in tsetse flies. Additionally, they introduced an iterative optimization approach in which each subsequent iteration used least cost path lines through the previously predicted resistance surface—an improvement over modeling organism movement as straight lines (16, 17). While this presented a major advance, the maximum-likelihood methodology requires exclusion of correlated data, establishing the relationship between environmental variables and genetic distance before building the model, and transforming or discretizing nonlinear relationships. Additionally, this approach assumes one relationship between each environmental variable and the genetic data across the whole landscape. To build on previous advances while overcoming some of their limitations, we combine iterative optimization with a machine-learning method called random forest (RF).RF is a nonlinear classification and regression tree analysis that can handle many inputs, including redundant or irrelevant variables, as well as continuous and categorical data types (14, 15). RF creates many internal training/testing subdatasets and aggregates the predictors, resulting in stable and consistent results that generally do not overfit the data and can be evaluated through validation processes (14). It is easier to tune and less likely to overfit noisy data than another machine-learning method we considered, gradient boosting (21). Additionally, RF has been successfully incorporated into ecological studies (22) and a small number of landscape genetics studies (16, 17, 23). These studies considered only the environmental predictor values at the genetic collection sites (23) or along straight lines between each pair of sites (16, 17), in contrast to the least cost path analysis we implement here (7).We demonstrate the efficacy of our method to map landscape connectivity for an important disease vector. Ae. aegypti is highly invasive and the primary vector of yellow fever, Zika, dengue, and chikungunya. Except for yellow fever, there are no reliable, widely used vaccines for these diseases, so vector control is essential. Ae. aegypti originated in Africa and is now found throughout the tropics and increasingly in temperate regions (24–26). The species is temperature constrained, preferring warm, humid areas close to humans (the females’ preferred source for bloodmeals outside their native African range) (27). In the United States, it has a patchy distribution throughout southern states, especially Texas, Florida, and California (28). Although Ae. aegypti can disperse >1 km, its usual lifetime dispersal is only around 200 m (29–32). Passive “hitchhiking” via human transportation networks is responsible for long-distance invasions and worldwide spread of Ae. aegypti and its close relative (33–35). Climate change is also expanding the range of Aedes species, which could expose nearly 1 billion additional people to diseases carried by these mosquitoes for the first time (26).Although IBD is common in nature and a helpful null model in landscape genetics (20), geographic distance is often an inadequate sole predictor of genetic distance (as in the case of our dataset; SI Appendix, Fig. S1). Therefore, a more complex model is needed to explain and predict genetic distance and corresponding landscape connectivity. In this paper we introduce an iterative machine-learning approach to integrate environmental predictors and genetic observation data and apply it to map landscape connectivity for the Ae. aegypti mosquito in North America. We also find and examine the most important variables for building the connectivity model and provide validation of our proposed method.     

Use of a hepatitis C virus (HCV) RNA‐positive donor in a treated HCV RNA‐negative liver transplant recipient

The shortage of livers has led most transplant centers to use extended criteria donors. Hepatitis C virus (HCV) RNA‐positive donor organs are typically not given to patients who have cleared HCV. A 64‐year‐old male with chronic hepatitis C, genotype 1b was listed for LT with hepatocellular carcinoma. While on the waiting list, the patient was treated with sofosbuvir, ledipasvir, and ribavirin and achieved an HCV RNA <15 IU/mL by week 10. At week 18 of a planned 24‐week treatment course, the patient underwent deceased‐donor LT and received an organ from an anti‐HCV‐positive donor. Treatment was stopped at LT. At week 3 post LT, HCV RNA was detectable and revealed a genotype 3 HCV infection, compatible with transplantation of an organ with established infection. With retreatment with sofosbuvir, daclatasvir, and ribavirin for 12 weeks, the patient achieved a sustained virologic response. This report highlights how antiviral therapies can be used to optimize the outcomes of HCV‐infected transplant patients.