Triosephosphate isomerase I170V alters catalytic site, enhances stability and induces pathology in a Drosophila model of TPI deficiency.

Triosephosphate isomerase (TPI) is a glycolytic enzyme which homodimerizes for full catalytic activity. Mutations of the TPI gene elicit a disease known as TPI Deficiency, a glycolytic enzymopathy noted for its unique severity of neurological symptoms. Evidence suggests that TPI Deficiency pathogenesis may be due to conformational changes of the protein, likely affecting dimerization and protein stability. In this report, we genetically and physically characterize a human disease-associated TPI mutation caused by an I170V substitution. Human TPI(I170V) elicits behavioral abnormalities in Drosophila. An examination of hTPI(I170V) enzyme kinetics revealed this substitution reduced catalytic turnover, while assessments of thermal stability demonstrated an increase in enzyme stability. The crystal structure of the homodimeric I170V mutant reveals changes in the geometry of critical residues within the catalytic pocket. Collectively these data reveal new observations of the structural and kinetic determinants of TPI Deficiency pathology, providing new insights into disease pathogenesis.

The reliability of tweets as a supplementary method of seasonal influenza surveillance.

BACKGROUND: Existing influenza surveillance in the United States is focused on the collection of data from sentinel physicians and hospitals; however, the compilation and distribution of reports are usually delayed by up to 2 weeks. With the popularity of social media growing, the Internet is a source for syndromic surveillance due to the availability of large amounts of data. In this study, tweets, or posts of 140 characters or less, from the website Twitter were collected and analyzed for their potential as surveillance for seasonal influenza. OBJECTIVE: There were three aims: (1) to improve the correlation of tweets to sentinel-provided influenza-like illness (ILI) rates by city through filtering and a machine-learning classifier, (2) to observe correlations of tweets for emergency department ILI rates by city, and (3) to explore correlations for tweets to laboratory-confirmed influenza cases in San Diego. METHODS: Tweets containing the keyword "flu" were collected within a 17-mile radius from 11 US cities selected for population and availability of ILI data. At the end of the collection period, 159,802 tweets were used for correlation analyses with sentinel-provided ILI and emergency department ILI rates as reported by the corresponding city or county health department. Two separate methods were used to observe correlations between tweets and ILI rates: filtering the tweets by type (non-retweets, retweets, tweets with a URL, tweets without a URL), and the use of a machine-learning classifier that determined whether a tweet was "valid", or from a user who was likely ill with the flu. RESULTS: Correlations varied by city but general trends were observed. Non-retweets and tweets without a URL had higher and more significant (P<.05) correlations than retweets and tweets with a URL. Correlations of tweets to emergency department ILI rates were higher than the correlations observed for sentinel-provided ILI for most of the cities. The machine-learning classifier yielded the highest correlations for many of the cities when using the sentinel-provided or emergency department ILI as well as the number of laboratory-confirmed influenza cases in San Diego. High correlation values (r=.93) with significance at P<.001 were observed for laboratory-confirmed influenza cases for most categories and tweets determined to be valid by the classifier. CONCLUSIONS: Compared to tweet analyses in the previous influenza season, this study demonstrated increased accuracy in using Twitter as a supplementary surveillance tool for influenza as better filtering and classification methods yielded higher correlations for the 2013-2014 influenza season than those found for tweets in the previous influenza season, where emergency department ILI rates were better correlated to tweets than sentinel-provided ILI rates. Further investigations in the field would require expansion with regard to the location that the tweets are collected from, as well as the availability of more ILI data.

Cellular Therapy During COVID-19: Lessons Learned and Preparing for Subsequent Waves.

An evidence-based triage plan for cellular therapy distribution is critical in the face of emerging constraints on healthcare resources. We evaluated the impact of treatment delays related to COVID-19 on patients scheduled to undergo hematopoietic cell transplantation (HCT) or chimeric antigen receptor T-cell (CAR-T) therapy at our center. Data were collected in real time between March 19 and May 11, 2020, for patients who were delayed to cellular therapy. We evaluated the proportion of delayed patients who ultimately received cellular therapy, reasons for not proceeding to cellular therapy, and changes in disease and health status during delay. A total of 85 patients were delayed, including 42 patients planned for autologous HCT, 36 patients planned for allogeneic HCT, and 7 patients planned for CAR-T therapy. Fifty-six of these patients (66%) since received planned therapy. Five patients died during the delay. The most common reason for not proceeding to autologous HCT was good disease control in patients with plasma cell dyscrasias (75%). The most common reason for not proceeding to allogeneic HCT was progression of disease (42%). All patients with acute leukemia who progressed had measurable residual disease (MRD) at the time of delay, whereas no patient without MRD at the time of delay progressed. Six patients (86%) ultimately received CAR-T therapy, including 3 patients who progressed during the delay. For patients with high-risk disease such as acute leukemia, and particularly those with MRD at the time of planned HCT, treatment delay can result in devastating outcomes and should be avoided if at all possible.