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Background: Cigarette smoking remains one of the leading causes of preventable death worldwide. A worldwide study by the World Health Organization concluded that more than 8 million people die every year from smoking, tobacco consumption, and secondhand smoke. The most effective tobacco cessation programs require personalized human intervention combined with costly pharmaceutical supplementation, making them unaffordable or inaccessible to most tobacco users. Thus, digital interventions offer a promising alternative to these traditional methods. However, the leading smartphone apps available in the market today have either not been studied in a clinical setting or are unable to match the smoking cessation success rates of their expensive offline counterparts. We would like to understand whether QuitSure, a novel smoking cessation app built by Rapidkart Online Private Limited, is able to bridge this efficacy gap and deliver affordable and effective smoking cessation at scale., Objective: Our objective was to do an initial exploration into the engagement, efficacy, and safety of QuitSure based on the self-reported experiences of its users. Outcomes measured were program completion, the effect of program completion on smoking behavior, including self-reported cessation outcomes, and negative health events from using the app., Methods: All QuitSure registered users who created their accounts on the QuitSure app between April 1, 2021, and February 28, 2022, were sent an anonymized web-based survey. The survey results were added to their engagement data on the app to evaluate the feasibility and efficacy of the app as a smoking cessation intervention. The data were analyzed using descriptive statistics (frequencies and percentages) and the χ 2 test of independence., Results: In total, 1299 users who had completed the QuitSure program submitted the survey and satisfied the inclusion criteria of the study. Of these, 1286 participants had completed the program more than 30 days before filling out the survey, and 1040 (80.1%, 95% CI 79.1%-82.6%) of them had maintained prolonged abstinence for at least 30 days after program completion. A majority of participants (770/891, 86.4%) who were still maintaining abstinence at the time of submitting the survey did not experience any severe nicotine withdrawal symptoms, while 41.9% (373/891) experienced no mild withdrawal symptoms either. Smoking quantity prior to completing the program significantly affected quit rates (P<.001), with heavy smokers (>20 cigarettes per day) having a lower 30-day prolonged abstinence rate (relative risk=0.91; 95% CI 90.0%-96.2%) compared to lighter smokers. No additional adverse events outside of known nicotine withdrawal symptoms were reported., Conclusions: The nature of web-based surveys and cohort selection allows for extensive unknown biases. However, the efficacy rates of survey respondents who completed the program were high and provide a case for further investigation in the form of randomized controlled trials on the QuitSure tobacco cessation program., (©Gregory M Goldgof, Shweta Mishra, Kriti Bajaj. Originally published in JMIR Human Factors (https://humanfactors.jmir.org), 06.05.2024.)

Background: Transfusion-associated circulatory overload (TACO) is a severe adverse reaction (AR) contributing to the leading cause of mortality associated with transfusions. As strategies to mitigate TACO have been increasingly adopted, an update of prevalence rates and risk factors associated with TACO using the growing sources of electronic health record (EHR) data can help understand transfusion safety., Study Design and Methods: This retrospective study aimed to provide a timely and reproducible assessment of prevalence rates and risk factors associated with TACO. Novel natural language processing methods, now made publicly available on GitHub, were developed to extract ARs from 3178 transfusion reaction reports. Other patient-level data were extracted computationally from UCSF EHR between 2012 and 2022. The odds ratio estimates of risk factors were calculated using a multivariate logistic regression analysis with case-to-control matched on sex and age at a ratio of 1:5., Results: A total of 56,208 patients received transfusions (total 573,533 units) at UCSF during the study period and 102 patients developed TACO. The prevalence of TACO was estimated to be 0.2% per patient (102/total 56,208). Patients with a history of coagulopathy (OR, 1.36; 95% CI, 1.04-1.79) and transplant (OR, 1.99; 95% CI, 1.48-2.68) were associated with increased odds of TACO., Discussion: While TACO is a serious AR, events remained rare, even in populations enriched with high-risk patients. Novel computational methods can be used to find and continually surveil for transfusion ARs. Results suggest that patients with history or presence of coagulopathy and organ transplant should be carefully monitored to mitigate potential risks of TACO., (© 2023 The Authors. Transfusion published by Wiley Periodicals LLC on behalf of AABB.)

In vitro evolution and whole genome analysis were used to comprehensively identify the genetic determinants of chemical resistance in Saccharomyces cerevisiae. Sequence analysis identified many genes contributing to the resistance phenotype as well as numerous amino acids in potential targets that may play a role in compound binding. Our work shows that compound-target pairs can be conserved across multiple species. The set of 25 most frequently mutated genes was enriched for transcription factors, and for almost 25 percent of the compounds, resistance was mediated by one of 100 independently derived, gain-of-function SNVs found in a 170 amino acid domain in the two Zn 2 C 6 transcription factors YRR1 and YRM1 (p < 1 × 10 -100 ). This remarkable enrichment for transcription factors as drug resistance genes highlights their important role in the evolution of antifungal xenobiotic resistance and underscores the challenge to develop antifungal treatments that maintain potency., (© 2022. The Author(s).)

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes coronavirus disease-19 (COVID-19), has emerged as the cause of a global pandemic. We used RNA sequencing to analyze 286 nasopharyngeal (NP) swab and 53 whole-blood (WB) samples from 333 patients with COVID-19 and controls. Overall, a muted immune response was observed in COVID-19 relative to other infections (influenza, other seasonal coronaviruses, and bacterial sepsis), with paradoxical down-regulation of several key differentially expressed genes. Hospitalized patients and outpatients exhibited up-regulation of interferon-associated pathways, although heightened and more robust inflammatory responses were observed in hospitalized patients with more clinically severe illness. Two-layer machine learning-based host classifiers consisting of complete (>1000 genes), medium (<100), and small (<20) gene biomarker panels identified COVID-19 disease with 85.1-86.5% accuracy when benchmarked using an independent test set. SARS-CoV-2 infection has a distinct biosignature that differs between NP swabs and WB and can be leveraged for COVID-19 diagnosis., (Copyright © 2021 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).)

Background: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection can be detected indirectly by measuring the host immune response. For some viruses, antibody concentrations correlate with host protection and viral neutralization, but in rare cases, antiviral antibodies can promote disease progression. Elucidation of the kinetics and magnitude of the SARS-CoV-2 antibody response is essential to understand the pathogenesis of coronavirus disease 2019 (COVID-19) and identify potential therapeutic targets., Methods: Sera (n = 533) from patients with real-time polymerase chain reaction-confirmed COVID-19 (n = 94 with acute infections and n = 59 convalescent patients) were tested using a high-throughput quantitative immunoglobulin M (IgM) and immunoglobulin G (IgG) assay that detects antibodies to the spike protein receptor binding domain and nucleocapsid protein. Individual and serial samples covered the time of initial diagnosis, during the disease course, and following recovery. We evaluated antibody kinetics and correlation between magnitude of the response and disease severity., Results: Patterns of SARS-CoV-2 antibody production varied considerably. Among 52 patients with 3 or more serial specimens, 44 (84.6%) and 42 (80.8%) had observed IgM and IgG seroconversion at a median of 8 and 10 days, respectively. Compared to those with milder disease, peak measurements were significantly higher for patients admitted to the intensive care unit for all time intervals between 6 and 20 days for IgM, and all intervals after 5 days for IgG., Conclusions: High-sensitivity assays with a robust dynamic range provide a comprehensive picture of host antibody response to SARS-CoV-2. IgM and IgG responses were significantly higher in patients with severe than mild disease. These differences may affect strategies for seroprevalence studies, therapeutics, and vaccine development., (© The Author(s) 2020. Published by Oxford University Press for the Infectious Diseases Society of America. All rights reserved. For permissions, e-mail: journals.permissions@oup.com.)

A number of recent papers have shown experimental evidence that suggests it is possible to build highly accurate deep neural network models to detect COVID-19 from chest X-ray images. In this paper, we show that good generalization to unseen sources has not been achieved. Experiments with richer data sets than have previously been used show models have high accuracy on seen sources, but poor accuracy on unseen sources. The reason for the disparity is that the convolutional neural network model, which learns features, can focus on differences in X-ray machines or in positioning within the machines, for example. Any feature that a person would clearly rule out is called a confounding feature. Some of the models were trained on COVID-19 image data taken from publications, which may be different than raw images. Some data sets were of pediatric cases with pneumonia where COVID-19 chest X-rays are almost exclusively from adults, so lung size becomes a spurious feature that can be exploited. In this work, we have eliminated many confounding features by working with as close to raw data as possible. Still, deep learned models may leverage source specific confounders to differentiate COVID-19 from pneumonia preventing generalizing to new data sources (i.e. external sites). Our models have achieved an AUC of 1.00 on seen data sources but in the worst case only scored an AUC of 0.38 on unseen ones. This indicates that such models need further assessment/development before they can be broadly clinically deployed. An example of fine-tuning to improve performance at a new site is given.

Appropriate use and interpretation of serological tests for assessments of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) exposure, infection and potential immunity require accurate data on assay performance. We conducted a head-to-head evaluation of ten point-of-care-style lateral flow assays (LFAs) and two laboratory-based enzyme-linked immunosorbent assays to detect anti-SARS-CoV-2 IgM and IgG antibodies in 5-d time intervals from symptom onset and studied the specificity of each assay in pre-coronavirus disease 2019 specimens. The percent of seropositive individuals increased with time, peaking in the latest time interval tested (>20 d after symptom onset). Test specificity ranged from 84.3% to 100.0% and was predominantly affected by variability in IgM results. LFA specificity could be increased by considering weak bands as negative, but this decreased detection of antibodies (sensitivity) in a subset of SARS-CoV-2 real-time PCR-positive cases. Our results underline the importance of seropositivity threshold determination and reader training for reliable LFA deployment. Although there was no standout serological assay, four tests achieved more than 80% positivity at later time points tested and more than 95% specificity.

Given the limited availability of serological testing to date, the seroprevalence of SARS-CoV-2-specific antibodies in different populations has remained unclear. Here, we report very low SARS-CoV-2 seroprevalence in two San Francisco Bay Area populations. Seroreactivity was 0.26% in 387 hospitalized patients admitted for non-respiratory indications and 0.1% in 1,000 blood donors in early April 2020. We additionally describe the longitudinal dynamics of immunoglobulin-G (IgG), immunoglobulin-M (IgM), and in vitro neutralizing antibody titers in COVID-19 patients. The median time to seroconversion ranged from 10.3-11.0 days for these 3 assays. Neutralizing antibodies rose in tandem with immunoglobulin titers following symptom onset, and positive percent agreement between detection of IgG and neutralizing titers was >93%. These findings emphasize the importance of using highly accurate tests for surveillance studies in low-prevalence populations, and provide evidence that seroreactivity using SARS-CoV-2 anti-nucleocapsid protein IgG and anti-spike IgM assays are generally predictive of in vitro neutralizing capacity.

A promising new compound class for treating human malaria is the imidazolopiperazines (IZP) class. IZP compounds KAF156 (Ganaplacide) and GNF179 are effective against Plasmodium symptomatic asexual blood-stage infections, and are able to prevent transmission and block infection in animal models. But despite the identification of resistance mechanisms in P. falciparum, the mode of action of IZPs remains unknown. To investigate, we here combine in vitro evolution and genome analysis in Saccharomyces cerevisiae with molecular, metabolomic, and chemogenomic methods in P. falciparum. Our findings reveal that IZP-resistant S. cerevisiae clones carry mutations in genes involved in Endoplasmic Reticulum (ER)-based lipid homeostasis and autophagy. In Plasmodium, IZPs inhibit protein trafficking, block the establishment of new permeation pathways, and cause ER expansion. Our data highlight a mechanism for blocking parasite development that is distinct from those of standard compounds used to treat malaria, and demonstrate the potential of IZPs for studying ER-dependent protein processing.

Given that many antifungal medications are susceptible to evolved resistance, there is a need for novel drugs with unique mechanisms of action. Inhibiting the essential proton pump Pma1p, a P-type ATPase, is a potentially effective therapeutic approach that is orthogonal to existing treatments. We identify NSC11668 and hitachimycin as structurally distinct antifungals that inhibit yeast ScPma1p. These compounds provide new opportunities for drug discovery aimed at this important target.

The WHO estimates around a million children contract tuberculosis (TB) annually with over 80 000 deaths from dissemination of infection outside of the lungs. The insidious onset and association with skin test anergy suggests failure of the immune system to both recognise and respond to infection. To understand the immune mechanisms, we studied genome-wide whole blood RNA expression in children with TB meningitis (TBM). Findings were validated in a second cohort of children with TBM and pulmonary TB (PTB), and functional T-cell responses studied in a third cohort of children with TBM, other extrapulmonary TB (EPTB) and PTB. The predominant RNA transcriptional response in children with TBM was decreased abundance of multiple genes, with 140/204 (68%) of all differentially regulated genes showing reduced abundance compared to healthy controls. Findings were validated in a second cohort with concordance of the direction of differential expression in both TBM (r2 = 0.78 p = 2x10-16) and PTB patients (r2 = 0.71 p = 2x10-16) when compared to a second group of healthy controls. Although the direction of expression of these significant genes was similar in the PTB patients, the magnitude of differential transcript abundance was less in PTB than in TBM. The majority of genes were involved in activation of leucocytes (p = 2.67E-11) and T-cell receptor signalling (p = 6.56E-07). Less abundant gene expression in immune cells was associated with a functional defect in T-cell proliferation that recovered after full TB treatment (p<0.0003). Multiple genes involved in T-cell activation show decreased abundance in children with acute TB, who also have impaired functional T-cell responses. Our data suggest that childhood TB is associated with an acquired immune defect, potentially resulting in failure to contain the pathogen. Elucidation of the mechanism causing the immune paresis may identify new treatment and prevention strategies.

Recent advances in cell-based, high-throughput phenotypic screening have identified new chemical compounds that are active against eukaryotic pathogens. A challenge to their future development lies in identifying these compounds' molecular targets and binding modes. In particular, subsequent structure-based chemical optimization and target-based screening require a detailed understanding of the binding event. Here, we use directed evolution and whole-genome sequencing of a drug-sensitive S. cerevisiae strain to identify the yeast ortholog of TcCyp51, lanosterol-14-alpha-demethylase (TcCyp51), as the target of MMV001239, a benzamide compound with activity against Trypanosoma cruzi, the etiological agent of Chagas disease. We show that parasites treated with MMV0001239 phenocopy parasites treated with another TcCyp51 inhibitor, posaconazole, accumulating both lanosterol and eburicol. Direct drug-protein binding of MMV0001239 was confirmed through spectrophotometric binding assays and X-ray crystallography, revealing a binding site shared with other antitrypanosomal compounds that target Cyp51. These studies provide a new probe chemotype for TcCyp51 inhibition.

A major cause of the paucity of new starting points for drug discovery is the lack of interaction between academia and industry. Much of the global resource in biology is present in universities, whereas the focus of medicinal chemistry is still largely within industry. Open source drug discovery, with sharing of information, is clearly a first step towards overcoming this gap. But the interface could especially be bridged through a scale-up of open sharing of physical compounds, which would accelerate the finding of new starting points for drug discovery. The Medicines for Malaria Venture Malaria Box is a collection of over 400 compounds representing families of structures identified in phenotypic screens of pharmaceutical and academic libraries against the Plasmodium falciparum malaria parasite. The set has now been distributed to almost 200 research groups globally in the last two years, with the only stipulation that information from the screens is deposited in the public domain. This paper reports for the first time on 236 screens that have been carried out against the Malaria Box and compares these results with 55 assays that were previously published, in a format that allows a meta-analysis of the combined dataset. The combined biochemical and cellular assays presented here suggest mechanisms of action for 135 (34%) of the compounds active in killing multiple life-cycle stages of the malaria parasite, including asexual blood, liver, gametocyte, gametes and insect ookinete stages. In addition, many compounds demonstrated activity against other pathogens, showing hits in assays with 16 protozoa, 7 helminths, 9 bacterial and mycobacterial species, the dengue fever mosquito vector, and the NCI60 human cancer cell line panel of 60 human tumor cell lines. Toxicological, pharmacokinetic and metabolic properties were collected on all the compounds, assisting in the selection of the most promising candidates for murine proof-of-concept experiments and medicinal chemistry programs. The data for all of these assays are presented and analyzed to show how outstanding leads for many indications can be selected. These results reveal the immense potential for translating the dispersed expertise in biological assays involving human pathogens into drug discovery starting points, by providing open access to new families of molecules, and emphasize how a small additional investment made to help acquire and distribute compounds, and sharing the data, can catalyze drug discovery for dozens of different indications. Another lesson is that when multiple screens from different groups are run on the same library, results can be integrated quickly to select the most valuable starting points for subsequent medicinal chemistry efforts.

The spiroindolones, a new class of antimalarial medicines discovered in a cellular screen, are rendered less active by mutations in a parasite P-type ATPase, PfATP4. We show here that S. cerevisiae also acquires mutations in a gene encoding a P-type ATPase (ScPMA1) after exposure to spiroindolones and that these mutations are sufficient for resistance. KAE609 resistance mutations in ScPMA1 do not confer resistance to unrelated antimicrobials, but do confer cross sensitivity to the alkyl-lysophospholipid edelfosine, which is known to displace ScPma1p from the plasma membrane. Using an in vitro cell-free assay, we demonstrate that KAE609 directly inhibits ScPma1p ATPase activity. KAE609 also increases cytoplasmic hydrogen ion concentrations in yeast cells. Computer docking into a ScPma1p homology model identifies a binding mode that supports genetic resistance determinants and in vitro experimental structure-activity relationships in both P. falciparum and S. cerevisiae. This model also suggests a shared binding site with the dihydroisoquinolones antimalarials. Our data support a model in which KAE609 exerts its antimalarial activity by directly interfering with P-type ATPase activity.

Bioassay-guided fractionation of two marine cyanobacterial extracts using the H-460 human lung cancer cell line and the OVC-5 human ovarian cancer cell line led to the isolation of three related α-methoxy-β, β'-dimethyl-γ-pyrones each containing a modified alkyl chain, one of which was identified as the previously reported kalkipyrone and designated kalkipyrone A. The second compound was an analog designated kalkipyrone B. The third was identified as the recently reported yoshinone A, also isolated from a marine cyanobacterium. Kalkipyrone A and B were obtained from a field-collection of the cyanobacterium Leptolyngbya sp. from Fagasa Bay, American Samoa, while yoshinone A was isolated from a field-collection of cyanobacteria (cf. Schizothrix sp.) from Panama. One-dimensional and two-dimensional NMR experiments were used to determine the overall structures and relative configurations of the kalkipyrones, and the absolute configuration of kalkipyrone B was determined by (1)H NMR analysis of diastereomeric Mosher's esters. Kalkipyrone A showed good cytotoxicity to H-460 human lung cancer cells (EC50=0.9μM), while kalkipyrone B and yoshinone A were less active (EC50=9.0μM and >10μM, respectively). Both kalkipyrone A and B showed moderate toxicity to Saccharomyces cerevisiae ABC16-Monster strain (IC50=14.6 and 13.4μM, respectively), whereas yoshinone A was of low toxicity to this yeast strain (IC50=63.8μM)., (Copyright © 2015 Elsevier Ltd. All rights reserved.)

The availability of genetically tractable organisms with simple genomes is critical for the rapid, systems-level understanding of basic biological processes. Mycoplasma bacteria, with the smallest known genomes among free-living cellular organisms, are ideal models for this purpose, but the natural versions of these cells have genome complexities still too great to offer a comprehensive view of a fundamental life form. Here we describe an efficient method for reducing genomes from these organisms by identifying individually deletable regions using transposon mutagenesis and progressively clustering deleted genomic segments using meiotic recombination between the bacterial genomes harbored in yeast. Mycoplasmal genomes subjected to this process and transplanted into recipient cells yielded two mycoplasma strains. The first simultaneously lacked eight singly deletable regions of the genome, representing a total of 91 genes and ∼ 10% of the original genome. The second strain lacked seven of the eight regions, representing 84 genes. Growth assay data revealed an absence of genetic interactions among the 91 genes under tested conditions. Despite predicted effects of the deletions on sugar metabolism and the proteome, growth rates were unaffected by the gene deletions in the seven-deletion strain. These results support the feasibility of using single-gene disruption data to design and construct viable genomes lacking multiple genes, paving the way toward genome minimization. The progressive clustering method is expected to be effective for the reorganization of any mega-sized DNA molecules cloned in yeast, facilitating the construction of designer genomes in microbes as well as genomic fragments for genetic engineering of higher eukaryotes., (© 2015 Suzuki et al.; Published by Cold Spring Harbor Laboratory Press.)

Transfer of genomes into yeast facilitates genome engineering for genetically intractable organisms, but this process has been hampered by the need for cumbersome isolation of intact genomes before transfer. Here we demonstrate direct cell-to-cell transfer of bacterial genomes as large as 1.8 megabases (Mb) into yeast under conditions that promote cell fusion. Moreover, we discovered that removal of restriction endonucleases from donor bacteria resulted in the enhancement of genome transfer.