Association of pellicle growth morphological characteristics and clinical presentation of Mycobacterium tuberculosis isolates

Trehalose dimycolate (TDM) is a mycobacterial glycolipid that is released from the surface of virulent M. tuberculosis. We evaluated the rate of growth, colony characteristics and production of TDM by Mycobacterium tuberculosis strains isolated from different clinical sites. Since detergent removes TDM from organisms, we analyzed growth rate and colony morphology of 79 primary clinical isolates grown as pellicles on the surface of detergent free Middlebrook 7H9 media. The genotype of each had been previously characterized. TDM production was measured by thin layer chromatography on 32 of these isolates. We found that strains isolated from pulmonary sites produced large amounts of TDM, grew rapidly as thin spreading pellicles, showed early cording (<1 week) and climbed the sides of the dish. In contrast, the extrapulmonary isolates (lymph node and bone marrow) produced less TDM (p<0.01), grew as discrete patches with little tendency to spread or climb the walls (p<0.02). The Beijing pulmonary (BP) isolates produced more TDM than non Beijing pulmonary isolates. The largest differences were observed in Beijing strains. The Beijing pulmonary isolates produced more TDM and grew faster than the Beijing extrapulmonary isolates (p<0.01). This was true even when the pulmonary and extrapulmonary isolates were derived from the same clade. These growth characteristics were consistently observed only on the first passage after primary isolation. This suggests that the differences in growth rate and TDM production observed reflect differences in gene expression patterns of pulmonary and extrapulmonary infections, that Mycobacterium tuberculosis in the lung grows more rapidly and produces more TDM than it does in extrapulmonary sites. This provides new opportunities to investigate gene expression of Mycobacterium tuberculosis in human.^

Clinical and epidemiological characteristics of breast cancer metastases

Introduction. Breast cancer is a highly variable disease, and long-term outcomes for individual patients are difficult to predict. We evaluated a retrospective cohort of early stage breast cancer (ESBC) patients based on a variety of clinical and epidemiological factors, specifically looking at the distribution of metastasis and associations with these clinical and epidemiological factors. ^ Methods. Patients were derived from the Early Stage Breast Cancer Repository (ESBCR) with a breast cancer diagnosed between 1985 and 2000. We conducted univariate and multivariate analysis of the data to evaluate associations between characteristics and occurrence of overall, visceral, and bone metastasis. Visceral metastasis was defined as lung, liver, peritoneal, lymph node (thoracic, abdominal, pelvis), and contralateral breast cancer. ^ Results. Overall, 394 (16%) patients developed a metastasis. Of these, 83% were visceral and 17% were bone. Multivariate analyses identified the following variables to be associated with metastasis: Any metastasis: age at diagnosis, stage, ER/PR status, hormone treatment, and type of surgery (1)Visceral metastasis: age at diagnosis, stage, hormone treatment, and type of surgery (2) Bone metastasis –Alcohol use, stage, and ER/PR status ^ Discussion/conclusion. ER-/PR- status has previously been found to be associated with bone metastasis, as we confirm in our cohort. We report an association between alcohol use and bone metastasis whereas previous studies find an association with recurrence. Distribution of metastases varies from previous studies. Typically, previous studies reported bone metastasis >30%, yet our study found 17%. Previous studies varied in design, and definition of visceral metastasis. Future research is needed to further elucidate prognostic factors associated with specific metastases A more thorough understanding of what might predict which ESBC patients will develop metastases can help direct future treatment. Future studies of this nature could include the Perou intrinsic subtypes, biomarkers like Ki-67, and genetic analyses such as Oncotype DX or MammaPrint.^