Removing bovine leukemia virus infected animals with high proviral load leads to lowering within-herd prevalence and new case reduction.

Bovine leukosis is prevalent in the North American dairy industry and its effect on animal health and production is widely documented. However, not all bovine leukemia virus (BLV) infected animals transmit the virus equally. Animals with high BLV proviral loads (HPL) are associated with higher transmission risks and therefore, their removal may reduce transmission and eventually within-herd prevalence. We aimed to evaluate the impact of selectively removing HPL cows on the within-herd BLV prevalence and incidence rate of BLV infection in 10 dairy herds. Annual blood and/or milk samples were collected from adult cows over 3 years. Positivity with BLV were determined by ELISA tests and proviral loads in blood of BLV-positive animals were estimated with BLV SS1 quantitative PCR assays. Herd managers were encouraged to consider the proviral load when making culling decisions and implement BLV control practices. High proviral load cows had the highest relative risk of removal indicating the farmers prioritized HPL cows for culling. The within-herd BLV prevalence decreased significantly in 4 herds whereas BLV incidence rate decreased in 9 herds. Over the 3 years, the proviral load demonstrated a relatively stable level, suggesting a single proviral load test in an adult cow may suffice to make culling decisions.

Effects of bovine leukemia virus seropositivity and proviral load on milk, fat, and protein production of dairy cows.

The objective was to evaluate the effects of bovine leukemia virus (BLV) infection, as determined by BLV seropositivity and proviral load, on 305-d milk, fat, and protein production of dairy cows. A cross-sectional study was conducted among 1,712 cows from 9 dairy herds in Alberta, Canada. The BLV status was assessed using an antibody ELISA, whereas BLV proviral load in BLV-seropositive cattle was determined with quantitative PCR. Dairy Herd Improvement 305-d milk, fat, and protein production data were obtained for all enrolled cattle. Differences in these milk end points were assessed in 2 ways: first, by categorizing cows based on BLV serostatus (i.e., BLV positive or negative), and second, by categorizing based on BLV proviral load (i.e., BLV negative, low proviral load [LPL] BLV positive, and high proviral load [HPL] BLV positive). A mixed-effect multivariable linear regression model was used to assess differences in milk parameters. We found that BLV positivity, adjusted for parity and natural log-transformed somatic cell count (SCC), was not associated with reduction in 305-d milk, fat, or protein production. However, significant reductions in 305-d milk, fat, and protein yield occurred in HPL cows, but not in LPL cows, compared with BLV-negative cows, when adjusted for parity number and natural log-transformed SCC. In summary, BLV proviral load may predict effects of BLV infection on milk, fat, and protein production.

An immunoinformatics study reveals a new BoLA-DR-restricted CD4+ T cell epitopes on the Gag protein of bovine leukemia virus.

Bovine leukemia virus (BLV) is the causative agent of enzootic bovine leucosis (EBL), which has been reported worldwide. The expression of viral structural proteins: surface glycoprotein (gp51) and three core proteins - p15 (matrix), p24 (capsid), and p12 (nucleocapsid) induce a strong humoral and cellular immune response at first step of infection. CD4+ T-cell activation is generally induced by bovine leukocyte antigen (BoLA) region- positive antigen-presenting cells (APC) after processing of an exogenous viral antigen. Limited data are available on the BLV epitopes from the core proteins recognized by CD4+ T-cells. Thus, immunoinformatic analysis of Gag sequences obtained from 125 BLV isolates from Poland, Canada, Pakistan, Kazakhstan, Moldova and United States was performed to identify the presence of BoLA-DRB3 restricted CD4+ T-cell epitopes. The 379 15-mer overlapping peptides spanning the entire Gag sequence were run in BoLA-DRB3 allele-binding regions using a BoLA-DRB- peptide binding affinity prediction algorithm. The analysis identified 22 CD4+ T-cell peptide epitopes of variable length ranging from 17 to 22 amino acids. The predicted epitopes interacted with 73 different BoLA-DRB3 alleles found in BLV-infected cattle. Importantly, two epitopes were found to be linked with high proviral load in PBMC. A majority of dominant and subdominant epitopes showed high conservation across different viral strains, and therefore could be attractive targets for vaccine development.

Adipose Tissue Insulin Resistance Predicts the Severity of Liver Fibrosis in Patients With Type 2 Diabetes and NAFLD.

CONTEXT: Although type 2 diabetes (T2D) is a risk factor for liver fibrosis in nonalcoholic fatty liver disease (NAFLD), the specific contribution of insulin resistance (IR) relative to other factors is unknown. OBJECTIVE: Assess the impact on liver fibrosis in NAFLD of adipose tissue (adipose tissue insulin resistance index [adipo-IR]) and liver (Homeostatic Model Assessment of Insulin Resistance [HOMA-IR]) IR in people with T2D and NAFLD. DESIGN: Participants were screened by elastography in the outpatient clinics for hepatic steatosis and fibrosis, including routine metabolites, cytokeratin-18 (a marker of hepatocyte apoptosis/steatohepatitis), and HOMA-IR/adipo-IR. SETTING: University ambulatory care practice. PARTICIPANTS: A total of 483 participants with T2D. INTERVENTION: Screening for steatosis and fibrosis with elastography. MAIN OUTCOME MEASURES: Liver steatosis (controlled attenuation parameter), fibrosis (liver stiffness measurement), and measurements of IR (adipo-IR, HOMA-IR) and fibrosis (cytokeratin-18). RESULTS: Clinically significant liver fibrosis (stage F ≥ 2 = liver stiffness measurement ≥8.0 kPa) was found in 11%, having more features of the metabolic syndrome, lower adiponectin, and higher aspartate aminotransferase (AST), alanine aminotransferase, liver fat, and cytokeratin-18 (P < 0.05-0.01). In multivariable analysis including just clinical variables (model 1), obesity (body mass index [BMI]) had the strongest association with fibrosis (odds ratio, 2.56; CI, 1.87-3.50; P < 0.01). When metabolic measurements and cytokeratin-18 were included (model 2), only BMI, AST, and liver fat remained significant. When fibrosis stage was adjusted for BMI, AST, and steatosis (model 3), only Adipo-IR remained strongly associated with fibrosis (OR, 1.51; CI, 1.05-2.16; P = 0.03), but not BMI, hepatic IR, or steatosis. CONCLUSIONS: These findings pinpoint to the central role of dysfunctional, insulin-resistant adipose tissue to advanced fibrosis in T2D, beyond simply BMI or steatosis. The clinical implication is that targeting adipose tissue should be the priority of treatment in NAFLD.

Assessing strategies to target screening for advanced liver fibrosis among overweight and obese patients.

Aim: The optimal screening strategy for advanced liver fibrosis in overweight and obese patients is unknown. The aim of this study is to compare the performance of different strategies to select patients at high risk of advanced liver fibrosis for screening using non-invasive tools. Methods: All patients underwent: liver 1H-MRS and percutaneous liver biopsy (in those with nonalcoholic fatty liver disease [NAFLD]). Unique selection strategies were compared to determine the best screening algorithm: (A) A "metabolic approach": selecting patients based on HOMA-IR ≥ 3; (B) A "diabetes approach": selecting only patients with type 2 diabetes; (C) An "imaging approach": selecting patients with hepatic steatosis based on 1H-MRS; (D) A "liver biochemistry approach": selecting patients with elevated ALT (i.e., ≥ 30 IU/L for males and ≥ 19 IU/L for females); and (E) Universal screening of overweight and obese patients. FIB-4 index, NAFLD fibrosis score, and APRI were applied as screening strategies. Results: A total of 275 patients were included in the study. Patients with advanced fibrosis (n = 29) were matched for age, gender, ethnicity, and BMI. Selecting patients by ALT elevation provided the most effective strategy, limiting the false positive rate while maintaining the sensitivity compared to universal screening. Selecting patients by any other strategy did not contribute to increasing the sensitivity of the approach and resulted in more false positive results. Conclusion: Universal screening of overweight/obese patients for advanced fibrosis with non-invasive tools is unwarranted, as selection strategies based on elevated ALT levels lead to the same sensitivity with a lower false positive rate (i.e., fewer patients that would require a liver biopsy or referral to hepatology).

Advanced Liver Fibrosis Is Common in Patients With Type 2 Diabetes Followed in the Outpatient Setting: The Need for Systematic Screening.

OBJECTIVE: Assess the prevalence of nonalcoholic fatty liver disease (NAFLD) and of liver fibrosis associated with nonalcoholic steatohepatitis in unselected patients with type 2 diabetes mellitus (T2DM). RESEARCH DESIGN AND METHODS: A total of 561 patients with T2DM (age: 60 ± 11 years; BMI: 33.4 ± 6.2 kg/m2; and HbA1c: 7.5 ± 1.8%) attending primary care or endocrinology outpatient clinics and unaware of having NAFLD were recruited. At the visit, volunteers were invited to be screened by elastography for steatosis and fibrosis by controlled attenuation parameter (≥274 dB/m) and liver stiffness measurement (LSM; ≥7.0 kPa), respectively. Secondary causes of liver disease were ruled out. Diagnostic panels for prediction of advanced fibrosis, such as AST-to-platelet ratio index (APRI) and Fibrosis-4 (FIB-4) index, were also measured. A liver biopsy was performed if results were suggestive of fibrosis. RESULTS: The prevalence of steatosis was 70% and of fibrosis 21% (LSM ≥7.0 kPa). Moderate fibrosis (F2: LSM ≥8.2 kPa) was present in 6% and severe fibrosis or cirrhosis (F3-4: LSM ≥9.7 kPa) in 9%, similar to that estimated by FIB-4 and APRI panels. Noninvasive testing was consistent with liver biopsy results. Elevated AST or ALT ≥40 units/L was present in a minority of patients with steatosis (8% and 13%, respectively) or with liver fibrosis (18% and 28%, respectively). This suggests that AST/ALT alone are insufficient as initial screening. However, performance may be enhanced by imaging (e.g., transient elastography) and plasma diagnostic panels (e.g., FIB-4 and APRI). CONCLUSIONS: Moderate-to-advanced fibrosis (F2 or higher), an established risk factor for cirrhosis and overall mortality, affects at least one out of six (15%) patients with T2DM. These results support the American Diabetes Association guidelines to screen for clinically significant fibrosis in patients with T2DM with steatosis or elevated ALT.