Delineation of DNA and mRNA COVID-19 vaccine-induced immune responses in preclinical animal models.

Nucleic acid vaccines are designed based on genetic sequences (DNA or mRNA) of a target antigen to be expressed in vivo to drive a host immune response. In response to the COVID-19 pandemic, mRNA and DNA vaccines based on the SARS-CoV-2 Spike antigen were developed. Surprisingly, head-to-head characterizations of the immune responses elicited by each vaccine type has not been performed to date. Here, we have employed a range of preclinical animal models including the hamster, guinea pig, rabbit, and mouse to compare and delineate the immune response raised by DNA, administered intradermally (ID) with electroporation (EP) and mRNA vaccines (BNT162b2 or mRNA-1273), administered intramuscularly (IM), expressing the SARS-CoV-2 WT spike antigen. The results revealed clear differences in the quality and magnitude of the immune response between the two vaccine platforms. The DNA vaccine immune response was characterized by strong T cell responses, while the mRNA vaccine elicited robust humoral responses. The results may assist in guiding the disease target each vaccine type may be best matched against and suggest mechanisms to further enhance the breadth of each platform's immune response.

Automated high confidence compound identification of electron ionization mass spectra for nontargeted analysis.

Nontargeted analysis based on mass spectrometry is a rising practice in environmental monitoring for identifying contaminants of emerging concern. Nontargeted analysis performed using comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GC×GC/TOF-MS) generates large numbers of possible analytes. Moreover, the default spectral library similarity score-based search algorithm used by LECO® ChromaTOF® does not ensure that high similarity scores result in correct library matches. Therefore, an additional manual screening is necessary, but leads to human errors especially when dealing with large amounts of data. To improve the speed and accuracy of the chemical identification, we developed CINeMA.py (Classification Is Never Manual Again). This programming suite automates GC×GC/TOF-MS data interpretation by determining the confidence of a match between the observed analyte mass spectrum and the LECO® ChromaTOF® software generated library hit from the NIST Electron Ionization Mass Spectral (NIST EI-MS) library. Our script allows the user to evaluate the confidence of the match using an algorithmic method that mimics the manual curation process and two different machine learning approaches (neural networks and random forest). The script allows the user to adjust various parameters (e.g., similarity threshold) and study their effects on prediction accuracy. To test CINeMA.py, we used data from two different environmental contaminant studies: an EPA study on household dust and a study on stormwater runoff. Using a reference set based on the analysis performed by highly trained users of the ChromaTOF and GC×GC/TOF-MS systems, the random forest model had the highest prediction accuracies of 86% and 83% on the EPA and Stormwater data sets, respectively. The algorithmic approach had the second-best prediction accuracy (82% and 79%), while the neural network accuracy had the lowest (63% and 67%). All the approaches required less than 1 min to classify 986 observed analytes, whereas manual data analysis required hours or days to complete. Our methods were also able to detect high confidence matches missed during the manual review. Overall, CINeMA.py provides users with a powerful suite of tools that should significantly speed-up data analysis while reducing the possibilities of manual errors and discrepancies among users, and can be applicable to other GC/EI-MS instrument based nontargeted analysis.

INO-4800 DNA vaccine induces neutralizing antibodies and T cell activity against global SARS-CoV-2 variants.

Global surveillance has identified emerging SARS-CoV-2 variants of concern (VOC) associated with broadened host specificity, pathogenicity, and immune evasion to vaccine-induced immunity. Here we compared humoral and cellular responses against SARS-CoV-2 VOC in subjects immunized with the DNA vaccine, INO-4800. INO-4800 vaccination induced neutralizing antibodies against all variants tested, with reduced levels detected against B.1.351. IFNγ T cell responses were fully maintained against all variants tested.

Temporal variations in bacterial community diversity and composition throughout intensive care unit renovations.

BACKGROUND: Inanimate surfaces within a hospital serve as a reservoir of microbial life that may colonize patients and ultimately result in healthcare associated infections (HAIs). Critically ill patients in intensive care units (ICUs) are particularly vulnerable to HAIs. Little is known about how the microbiome of the ICU is established or what factors influence its evolution over time. A unique opportunity to bridge the knowledge gap into how the ICU microbiome evolves emerged in our health system, where we were able to characterize microbial communities in an established hospital ICU prior to closing for renovations, during renovations, and then after re-opening. RESULTS: We collected swab specimens from ICU bedrails, computer keyboards, and sinks longitudinally at each renovation stage, and analyzed the bacterial compositions on these surfaces by 16S rRNA gene sequencing. Specimens collected before ICU closure had the greatest alpha diversity, while specimens collected after the ICU had been closed for over 300 days had the least. We sampled the ICU during the 45 days after re-opening; however, within that time frame, the alpha diversity never reached pre-closure levels. There were clear and significant differences in microbiota compositions at each renovation stage, which was driven by environmental bacteria after closure and human-associated bacteria after re-opening and before closure. CONCLUSIONS: Overall, we identified significant differences in microbiota diversity and community composition at each renovation stage. These data help to decipher the evolution of the microbiome in the most critical part of the hospital and demonstrate the significant impacts that microbiota from patients and staff have on the evolution of ICU surfaces. Video Abstract.

Low triiodothyronine predicts mortality in critically ill patients.

OBJECTIVE: Alteration in thyroid hormones are seen in critically ill patients admitted to intensive care units. Our objective was to study the thyroid hormone profile, prolactin and, glycosylated hemoglobin (HbA1c) at admission and analyze their correlation with mortality. MATERIALS AND METHODS: In this single centre, prospective, observational study, 100 consecutive patients (52M; 48F) admitted to medical ICU irrespective of diagnosis were included. Patients with previous thyroid disorders and drugs affecting thyroid function were excluded. All participants underwent complete physical examination and a single fasting blood sample obtained at admission was analyzed for total triiodothyronine (T3), total thyroxine (T4), thyroid stimulating hormone (TSH), HbA1c, and prolactin. The patients were divided into two groups: Group 1 - survivors (discharged from the hospital) and Group 2 - nonsurvivors (patients succumbed to their illness inside the hospital). The data were analyzed by appropriate statistical methods and a P-value of <0.05 was considered significant. RESULTS: The mean age of the participants was 58.7 ± 16.9 years and the mean duration of ICU stay was 3.3 ± 3.1 days. A total of 64 patients survived, whereas remaining 36 succumbed to their illness. The baseline demographic profile was comparable between survivors and nonsurvivors. Nonsurvivors had low T3 when compared with survivors (49.1 ± 32.7 vs. 66.2 ± 30.1, P = 0.0044). There was no significant difference observed between survivors and nonsurvivors with respect to T4, TSH, HbA1c, and prolactin. CONCLUSION: Our study showed that low T3 is an important marker of mortality in critically ill patients. Admission HbA1c, prolactin, T4, and TSH did not vary between survivors and nonsurvivors.