Evaluating the Efficacy and Safety of EZC Pak, a 5-Day Combination Echinacea-Zinc-Vitamin C Dose Pack with or without Vitamin D, in the Management of Outpatient Upper Respiratory Infections.

Background: Growing antibiotic resistance is among the most serious threats to public health, with antibiotic misuse considered a leading driver of the problem. One of the largest areas of misuse is in outpatient upper respiratory infections (URIs). The purpose of this research is to evaluate the efficacy of EZC Pak, a combination Echinacea-Zinc-Vitamin C dose pack with or without Vitamin D, on the duration of illness and symptom severity of non-specific URIs as an alternative to antibiotics when none are deemed clinically necessary. A secondary analysis was carried out on patient satisfaction. Methods: A total of 360 patients across the United States were enrolled and randomized in a double-blind manner across two intervention groups, EZC Pak, EZC Pak+Vitamin D, and one placebo group. The study utilized a smartphone-based app to capture data. Once a participant reported the first URI symptom, they were instructed to take the intervention as directed and complete the daily symptom survey score until their symptoms resolved. Results: The average EZC Pak participant recovered 1.39 days (90% CI 1.05 to 1.73) faster than the average placebo participant (p=0.017). The average EZC Pak participant reported a 17.43% (90% CI 17.1 to 17.8) lower symptom severity score versus placebo (p=0.029). EZC Pak users reported 2.9 times higher patient satisfaction versus placebo users (p=0.012). The addition of Vitamin D neither benefited nor harmed illness duration or symptom severity. Conclusion: The findings support the potential use of EZC Pak as an alternative to patient request for antibiotics when none are deemed clinically necessary at the time of initial clinical presentation. The decision to replete vitamin D in the acute phase of URI is an individualized decision left to the patient and their clinician. EZC Pak may play a critical role in improving outpatient URI management and antibiotic stewardship (ClinicalTrials.gov number, NCT04943575).

A vital sign-based prediction algorithm for differentiating COVID-19 versus seasonal influenza in hospitalized patients.

Patients with influenza and SARS-CoV2/Coronavirus disease 2019 (COVID-19) infections have a different clinical course and outcomes. We developed and validated a supervised machine learning pipeline to distinguish the two viral infections using the available vital signs and demographic dataset from the first hospital/emergency room encounters of 3883 patients who had confirmed diagnoses of influenza A/B, COVID-19 or negative laboratory test results. The models were able to achieve an area under the receiver operating characteristic curve (ROC AUC) of at least 97% using our multiclass classifier. The predictive models were externally validated on 15,697 encounters in 3125 patients available on TrinetX database that contains patient-level data from different healthcare organizations. The influenza vs COVID-19-positive model had an AUC of 98.8%, and 92.8% on the internal and external test sets, respectively. Our study illustrates the potentials of machine-learning models for accurately distinguishing the two viral infections. The code is made available at https://github.com/ynaveena/COVID-19-vs-Influenza and may have utility as a frontline diagnostic tool to aid healthcare workers in triaging patients once the two viral infections start cocirculating in the communities.

A Vital Sign-based Prediction Algorithm for Differentiating COVID-19 Versus Seasonal Influenza in Hospitalized Patients.

Patients with influenza and SARS-CoV2/Coronavirus disease 2019 (COVID-19) infections have different clinical course and outcomes. We developed and validated a supervised machine learning pipeline to distinguish the two viral infections using the available vital signs and demographic dataset from the first hospital/emergency room encounters of 3,883 patients who had confirmed diagnoses of influenza A/B, COVID-19 or negative laboratory test results. The models were able to achieve an area under the receiver operating characteristic curve (ROC AUC) of at least 97% using our multiclass classifier. The predictive models were externally validated on 15,697 encounters in 3,125 patients available on TrinetX database that contains patient-level data from different healthcare organizations. The influenza vs. COVID-19-positive model had an AUC of 98%, and 92% on the internal and external test sets, respectively. Our study illustrates the potentials of machine-learning models for accurately distinguishing the two viral infections. The code is made available at https://github.com/ynaveena/COVID-19-vs-Influenza and may be have utility as a frontline diagnostic tool to aid healthcare workers in triaging patients once the two viral infections start cocirculating in the communities.

Reverse TCR repertoire evolution toward dominant low-affinity clones during chronic CMV infection.

Adaptive evolution is a key feature of T cell immunity. During acute immune responses, T cells harboring high-affinity T cell antigen receptors (TCRs) are preferentially expanded, but whether affinity maturation by clonal selection continues through the course of chronic infections remains unresolved. Here we investigated the evolution of the TCR repertoire and its affinity during the course of infection with cytomegalovirus, which elicits large T cell populations in humans and mice. Using single-cell and bulk TCR sequencing and structural affinity analyses of cytomegalovirus-specific T cells, and through the generation and in vivo monitoring of defined TCR repertoires, we found that the immunodominance of high-affinity T cell clones declined during the chronic infection phase, likely due to cellular senescence. These data showed that under conditions of chronic antigen exposure, low-affinity TCRs preferentially expanded within the TCR repertoire, with implications for immunotherapeutic strategies.

Liver Is a Generative Site for the B Cell Response to Ehrlichia muris.

The B cell response to Ehrlichia muris is dominated by plasmablasts (PBs), with few-if any-germinal centers (GCs), yet it generates protective immunoglobulin M (IgM) memory B cells (MBCs) that express the transcription factor T-bet and harbor V-region mutations. Because Ehrlichia prominently infects the liver, we investigated the nature of liver B cell response and that of the spleen. B cells within infected livers proliferated and underwent somatic hypermutation (SHM). Vh-region sequencing revealed trafficking of clones between the spleen and liver and often subsequent local clonal expansion and intraparenchymal localization of T-bet+ MBCs. T-bet+ MBCs expressed MBC subset markers CD80 and PD-L2. Many T-bet+ MBCs lacked CD11b or CD11c expression but had marginal zone (MZ) B cell phenotypes and colonized the splenic MZ, revealing T-bet+ MBC plasticity. Hence, liver and spleen are generative sites of B cell responses, and they include V-region mutation and result in liver MBC localization.

Scikit-ribo Enables Accurate Estimation and Robust Modeling of Translation Dynamics at Codon Resolution.

Ribosome profiling (Ribo-seq) is a powerful technique for measuring protein translation; however, sampling errors and biological biases are prevalent and poorly understood. Addressing these issues, we present Scikit-ribo (https://github.com/schatzlab/scikit-ribo), an open-source analysis package for accurate genome-wide A-site prediction and translation efficiency (TE) estimation from Ribo-seq and RNA sequencing data. Scikit-ribo accurately identifies A-site locations and reproduces codon elongation rates using several digestion protocols (r = 0.99). Next, we show that the commonly used reads per kilobase of transcript per million mapped reads-derived TE estimation is prone to biases, especially for low-abundance genes. Scikit-ribo introduces a codon-level generalized linear model with ridge penalty that correctly estimates TE, while accommodating variable codon elongation rates and mRNA secondary structure. This corrects the TE errors for over 2,000 genes in S. cerevisiae, which we validate using mass spectrometry of protein abundances (r = 0.81), and allows us to determine the Kozak-like sequence directly from Ribo-seq. We conclude with an analysis of coverage requirements needed for robust codon-level analysis and quantify the artifacts that can occur from cycloheximide treatment.

The DEAD-Box Protein Dhh1p Couples mRNA Decay and Translation by Monitoring Codon Optimality.

A major determinant of mRNA half-life is the codon-dependent rate of translational elongation. How the processes of translational elongation and mRNA decay communicate is unclear. Here, we establish that the DEAD-box protein Dhh1p is a sensor of codon optimality that targets an mRNA for decay. First, we find mRNAs whose translation elongation rate is slowed by inclusion of non-optimal codons are specifically degraded in a Dhh1p-dependent manner. Biochemical experiments show Dhh1p is preferentially associated with mRNAs with suboptimal codon choice. We find these effects on mRNA decay are sensitive to the number of slow-moving ribosomes on an mRNA. Moreover, we find Dhh1p overexpression leads to the accumulation of ribosomes specifically on mRNAs (and even codons) of low codon optimality. Lastly, Dhh1p physically interacts with ribosomes in vivo. Together, these data argue that Dhh1p is a sensor for ribosome speed, targeting an mRNA for repression and subsequent decay.

Connections Underlying Translation and mRNA Stability.

Gene expression and regulation in organisms minimally depends on transcription by RNA polymerase and on the stability of the RNA product (for both coding and non-coding RNAs). For coding RNAs, gene expression is further influenced by the amount of translation by the ribosome and by the stability of the protein product. The stabilities of these two classes of RNA, non-coding and coding, vary considerably: tRNAs and rRNAs tend to be long lived while mRNAs tend to be more short lived. Even among mRNAs, however, there is a considerable range in stability (ranging from seconds to hours in bacteria and up to days in metazoans), suggesting a significant role for stability in the regulation of gene expression. Here, we review recent experiments from bacteria, yeast and metazoans indicating that the stability of most mRNAs is broadly impacted by the actions of ribosomes that translate them. Ribosomal recognition of defective mRNAs triggers "mRNA surveillance" pathways that target the mRNA for degradation [Shoemaker and Green (2012) ]. More generally, even the stability of perfectly functional mRNAs appears to be dictated by overall rates of translation by the ribosome [Herrick et al. (1990), Presnyak et al. (2015) ]. Given that mRNAs are synthesized for the purpose of being translated into proteins, it is reassuring that such intimate connections between mRNA and the ribosome can drive biological regulation. In closing, we consider the likelihood that these connections between protein synthesis and mRNA stability are widespread or whether other modes of regulation dominate the mRNA stability landscape in higher organisms.

Synthesis at the Speed of Codons.

The possibility that different mRNA sequences encoding identical peptides are translated dissimilarly has long been of great interest. Recent work by Yu and co-workers provides striking evidence that mRNA sequences influence the rate of protein synthesis, and lends support to the emerging idea that mRNA sequence informs protein folding.

Ribosomes slide on lysine-encoding homopolymeric A stretches.

Protein output from synonymous codons is thought to be equivalent if appropriate tRNAs are sufficiently abundant. Here we show that mRNAs encoding iterated lysine codons, AAA or AAG, differentially impact protein synthesis: insertion of iterated AAA codons into an ORF diminishes protein expression more than insertion of synonymous AAG codons. Kinetic studies in E. coli reveal that differential protein production results from pausing on consecutive AAA-lysines followed by ribosome sliding on homopolymeric A sequence. Translation in a cell-free expression system demonstrates that diminished output from AAA-codon-containing reporters results from premature translation termination on out of frame stop codons following ribosome sliding. In eukaryotes, these premature termination events target the mRNAs for Nonsense-Mediated-Decay (NMD). The finding that ribosomes slide on homopolymeric A sequences explains bioinformatic analyses indicating that consecutive AAA codons are under-represented in gene-coding sequences. Ribosome 'sliding' represents an unexpected type of ribosome movement possible during translation.

Improved atomistic Monte Carlo simulations demonstrate that poly-L-proline adopts heterogeneous ensembles of conformations of semi-rigid segments interrupted by kinks.

Poly-L-proline (PLP) polymers are useful mimics of biologically relevant proline-rich sequences. Biophysical and computational studies of PLP polymers in aqueous solutions are challenging because of the diversity of length scales and the slow time scales for conformational conversions. We describe an atomistic simulation approach that combines an improved ABSINTH implicit solvation model, with conformational sampling based on standard and novel Metropolis Monte Carlo moves. Refinements to forcefield parameters were guided by published experimental data for proline-rich systems. We assessed the validity of our simulation results through quantitative comparisons to experimental data that were not used in refining the forcefield parameters. Our analysis shows that PLP polymers form heterogeneous ensembles of conformations characterized by semirigid, rod-like segments interrupted by kinks, which result from a combination of internal cis peptide bonds, flexible backbone ψ angles, and the coupling between ring puckering and backbone degrees of freedom.