The persistence of drug resistant cell populations following chemotherapeutic treatment is a significant challenge in the clinical management of cancer. Resistant subpopulations arise via both cell intrinsic and extrinsic mechanisms. Extrinsic factors in the microenvironment, including neighboring cells, glycosaminoglycans, and fibrous proteins impact therapy response. Elevated levels of extracellular fibrous proteins are associated with tumor progression and cause the surrounding tissue to stiffen through changes in structure and composition of the extracellular matrix (ECM). We sought to determine how this progressively stiffening microenvironment affects the sensitivity of breast cancer cells to chemotherapeutic treatment. MDA-MB-231 triple negative breast carcinoma cells cultured in a 3D alginate-based hydrogel system displayed a stiffness-dependent response to the chemotherapeutic doxorubicin. MCF7 breast carcinoma cells cultured in the same conditions did not exhibit this stiffness-dependent resistance to the drug. This differential therapeutic response was coordinated with nuclear translocation of YAP, a marker of mesenchymal differentiation. The stiffness-dependent response was lost when cells were transferred from 3D to monolayer cultures, suggesting that endpoint ECM conditions largely govern the response to doxorubicin. To further examine this response, we utilized a platform capable of dynamic ECM stiffness modulation to allow for a change in matrix stiffness over time. We found that MDA-MB-231 cells have a stiffness-dependent resistance to doxorubicin and that duration of exposure to ECM stiffness is sufficient to modulate this response. These results indicate the need for additional tools to integrate mechanical stiffness with therapeutic response and inform decisions for more effective use of chemotherapeutics in the clinic.
The CRISPR-Cas9 system has increased the speed and precision of genetic editing in cells and animals. However, model generation for drug development is still expensive and time-consuming, demanding more target flexibility and faster turnaround times with high reproducibility. The generation of a tightly controlled ObLiGaRe doxycycline inducible SpCas9 (ODInCas9) transgene and its use in targeted ObLiGaRe results in functional integration into both human and mouse cells culminating in the generation of the ODInCas9 mouse. Genomic editing can be performed in cells of various tissue origins without any detectable gene editing in the absence of doxycycline. Somatic in vivo editing can model non-small cell lung cancer (NSCLC) adenocarcinomas, enabling treatment studies to validate the efficacy of candidate drugs. The ODInCas9 mouse allows robust and tunable genome editing granting flexibility, speed and uniformity at less cost, leading to high throughput and practical preclinical in vivo therapeutic testing.
Background Factors that lead to successful SARS-CoV-2 transmission are still not well described. We investigated the association between a case’s viral load and the risk of transmission to contacts in the context of other exposure-related factors. Methods Data were generated through routine testing and contact tracing at a large university. Case viral loads were obtained from cycle threshold values associated with a positive polymerase chain reaction test result from October 1, 2020 to April 15, 2021. Cases were included if they had at least one contact who tested 3–14 days after the exposure. Case-contact pairs were formed by linking index cases with contacts. Chi-square tests were used to evaluate differences in proportions of contacts testing positive. Generalized estimating equation models with a log link were used to evaluate whether viral load and other exposure-related factors were associated with a contact testing positive. Results Median viral load among the 212 cases included in the study was 5.6 (1.8–10.4) log10 RNA copies per mL of saliva. Among 365 contacts, 70 (19%) tested positive following their exposure; 36 (51%) were exposed to a case that was asymptomatic or pre-symptomatic on the day of exposure. The proportion of contacts that tested positive increased monotonically with index case viral load (12%, 23% and 25% corresponding to < 5, 5–8 and > 8 log10 copies per mL, respectively; X2 = 7.18, df = 2, p = 0.03). Adjusting for cough, time between test and exposure, and physical contact, the risk of transmission to a close contact was significantly associated with viral load (RR = 1.27, 95% CI 1.22–1.32). Conclusions Further research is needed to understand whether these relationships persist for newer variants. For those variants whose transmission advantage is mediated through a high viral load, public health measures could be scaled accordingly. Index cases with higher viral loads could be prioritized for contact tracing and recommendations to quarantine contacts could be made according to the likelihood of transmission based on risk factors such as viral load.
Although testing, contact tracing, and case isolation programs can mitigate COVID-19 transmission and allow the relaxation of social distancing measures, few countries worldwide have succeeded in scaling such efforts to levels that suppress spread. The efficacy of test-trace-isolate likely depends on the speed and extent of follow-up and the prevalence of SARS-CoV-2 in the community. Here, we use a granular model of COVID-19 transmission to estimate the public health impacts of test-trace-isolate programs across a range of programmatic and epidemiological scenarios, based on testing and contact tracing data collected on a university campus and surrounding community in Austin, TX, between October 1, 2020, and January 1, 2021. The median time between specimen collection from a symptomatic case and quarantine of a traced contact was 2 days (interquartile range [IQR]: 2 to 3) on campus and 5 days (IQR: 3 to 8) in the community. Assuming a reproduction number of 1.2, we found that detection of 40% of all symptomatic cases followed by isolation is expected to avert 39% (IQR: 30% to 45%) of COVID-19 cases. Contact tracing is expected to increase the cases averted to 53% (IQR: 42% to 58%) or 40% (32% to 47%), assuming the 2- and 5-day delays estimated on campus and in the community, respectively. In a tracing-accelerated scenario, in which 75% of contacts are notified the day after specimen collection, cases averted increase to 68% (IQR: 55% to 72%). An accelerated contact tracing program leveraging rapid testing and electronic reporting of test results can significantly curtail local COVID-19 transmission.
Colleges and universities in the United States have relied on various measures during the COVID-19 pandemic to prevent transmission of SARS-CoV-2, the virus that causes COVID-19, including implementing testing programs (1-3). These programs have permitted a safer return to campus for students by identifying infected persons and temporarily isolating them from the campus population (2,3). The University of Texas at Austin (UT Austin) implemented COVID-19 prevention measures in Fall 2020* including the following testing programs: clinic-based diagnostic testing, voluntary community screening, and targeted screening (testing of specific student populations in situations of increased transmission risk). During September 30-November 30, 2020, UT Austin students participated in tests for SARS-CoV-2, which resulted in the detection of 401 unique student cases of COVID-19 from among 32,401 tests conducted. † Among students who participated in one targeted screening program for students attending campus events, 18 (37.5%) of 48 infected students were asymptomatic at the time of their positive test result compared with 45 (23%) of 195 students identified through community testing and nine (5.8%) of 158 students identified through clinic-based testing. Targeted screening also identified a different population of students than did clinic-based and community testing programs. Infected students tested through targeted screening were more likely to be non-Hispanic White persons (chi square = 20.42; p<0.03), less likely to engage in public health measures, and more likely to have had interactions in settings where the risk for SARS-CoV-2 transmission is higher, such as restaurants, gyms, and residence halls. In addition to clinic-based SARS-CoV-2 testing at colleges and universities, complementary testing programs such as community and targeted screening might enhance efforts to identify and control SARS-CoV-2 transmission, especially among asymptomatic persons and disproportionately affected populations that might not otherwise be reached.During September 30-November 30, 2020, UT Austin employed the following SARS-CoV-2 testing programs: 1) clinic-based diagnostic testing administered by University Health Services for persons who were symptomatic or reported * https://protect.utexas.edu/ † A COVID-19 case was defined as a positive SARS-CoV-2 nucleic acid amplification test or antigen test result.
Communities worldwide have used vaccines and facemasks to mitigate the COVID-19 pandemic. When an individual opts to vaccinate or wear a mask, they may lower their own risk of becoming infected as well as the risk that they pose to others while infected. The first benefit–reducing susceptibility–has been established across multiple studies, while the second–reducing infectivity–is less well understood. Using a new statistical method, we estimate the efficacy of vaccines and facemasks at reducing both types of risks from contact tracing data collected in an urban setting. We find that vaccination reduced the risk of onward transmission by 40.7% [95% CI 25.8–53.2%] during the Delta wave and 31.0% [95% CI 19.4–40.9%] during the Omicron wave and that mask wearing reduced the risk of infection by 64.2% [95% CI 5.8–77.3%] during the Omicron wave. By harnessing commonly-collected contact tracing data, the approach can broadly provide timely and actionable estimates of intervention efficacy against a rapidly evolving pathogen.
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