Showing papers by "Philip E. Bourne published in 2020"

Evidence for treatment with estradiol for women with SARS-CoV-2 infection.

Abstract: Given that an individual’s age and gender are strongly predictive of coronavirus disease 2019 (COVID-19) outcomes, do such factors imply anything about preferable therapeutic options? An analysis of electronic health records for a large (68,466-case), international COVID-19 cohort, in 5-year age strata, revealed age-dependent sex differences. In particular, we surveyed the effects of systemic hormone administration in women. The primary outcome for estradiol therapy was death. Odds ratios (ORs) and Kaplan-Meier survival curves were analyzed for 37,086 COVID-19 women in two age groups: pre- (15–49 years) and peri-/post-menopausal (> 50 years). The incidence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is higher in women than men (by about + 15%) and, in contrast, the fatality rate is higher in men (about + 50%). Interestingly, the relationships between these quantities are linked to age: pre-adolescent girls and boys had the same risk of infection and fatality rate, while adult premenopausal women had a significantly higher risk of infection than men in the same 5-year age stratum (about 16,000 vs. 12,000 cases). This ratio changed again in peri- and postmenopausal women, with infection susceptibility converging with men. While fatality rates increased continuously with age for both sexes, at 50 years, there was a steeper increase for men. Thus far, these types of intricacies have been largely neglected. Because the hormone 17s-estradiol influences expression of the human angiotensin-converting enzyme 2 (ACE2) protein, which plays a role in SARS-CoV-2 cellular entry, propensity score matching was performed for the women’s sub-cohort, comparing users vs. non-users of estradiol. This retrospective study of hormone therapy in female COVID-19 patients shows that the fatality risk for women > 50 years receiving estradiol therapy (user group) is reduced by more than 50%; the OR was 0.33, 95% CI [0.18, 0.62] and the hazard ratio (HR) was 0.29, 95% CI [0.11,0.76]. For younger, pre-menopausal women (15–49 years), the risk of COVID-19 fatality is the same irrespective of estradiol treatment, probably because of higher endogenous estradiol levels. As of this writing, still no effective drug treatment is available for COVID-19; since estradiol shows such a strong improvement regarding fatality in COVID-19, we suggest prospective studies on the potentially more broadly protective roles of this naturally occurring hormone.

IL-13 Predicts the Need for Mechanical Ventilation in COVID-19 Patients

Abstract: Severe cases of COVID-19 are characterized by excessive inflammation. Here we report on an inpatient cohort where plasma cytokines were measured and tested for association with future need for mechanical ventilation. Hierarchical clustering, Kaplan-Meier curves, and odds ratios demonstrated that the cluster of IL-13 (OR: 1.57), IL-7 (OR: 1.04) and bFGF (OR: 1.04) was predictive for intubation, independent of age, gender and comorbidities.

Evidence for treatment with estradiol for women with SARS-CoV-2 infection

Abstract: BACKGROUND Given that an individual9s age and gender are strongly predictive of COVID-19 outcomes, do such factors imply anything about preferable therapeutic options? METHODS An analysis of electronic health records for a large (68,466-case), international COVID-19 cohort, in five-year age strata, revealed age-dependent sex differences. In particular, we surveyed the effects of systemic hormone administration in women. The primary outcome for estradiol therapy was death. Odds Ratios (ORs) and Kaplan-Meier survival curves were analyzed for 37,086 COVID-19 women in two age groups: pre- (15-49 years) and post-menopausal (>50 years). RESULTS The incidence of SARS-CoV-2 infection is higher in women than men (about +15%) and, in contrast, the fatality rate is higher in men (about +50%). Interestingly, the relationships between these quantities are also linked to age. Pre-adolescent girls had the same risk of infection and fatality rate as boys. Adult premenopausal women had a significantly higher risk of infection than men in the same five-year age stratum (about 16,000 vs. 12,000 cases). This ratio changed again in postmenopausal women, with infection susceptibility converging with men. While fatality rates increased continuously with age for both sexes, at 50 years there was a steeper increase for men. Thus far, these types of intricacies have been largely neglected. Because the hormone 17β-estradiol has a positive effect on expression of the human ACE2 protein--which plays an essential role for SARS-CoV-2 cellular entry--propensity score matching was performed for the women9s sub-cohort, comparing users versus non-users of estradiol. This retrospective study of hormone therapy in female COVID-19 patients shows that the fatality risk for women >50 yrs receiving estradiol therapy (user group) is reduced by more than 50%; the OR was 0.33, 95 % CI [0.18, 0.62] and the Hazard Ratio was 0.29, 95% CI [0.11,0.76]. For younger, pre-menopausal women (15-49 yrs), the risk of COVID-19 fatality is the same irrespective of estradiol treatment, probably because of higher endogenous estradiol levels. CONCLUSIONS As of this writing, still no effective drug treatment is available for COVID-19; since estradiol shows such a strong improvement regarding fatality in COVID-19, we suggest prospective studies on the potentially more broadly protective roles of this naturally occurring hormone.

Computational Prediction of Potential Inhibitors of the Main Protease of SARS-CoV-2

Abstract: The rapidly developing pandemic, known as coronavirus disease 2019 (COVID-19) and caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has recently spread across 213 countries and territories. This pandemic is a dire public health threat-particularly for those suffering from hypertension, cardiovascular diseases, pulmonary diseases, or diabetes; without approved treatments, it is likely to persist or recur. To facilitate the rapid discovery of inhibitors with clinical potential, we have applied ligand- and structure-based computational approaches to develop a virtual screening methodology that allows us to predict potential inhibitors. In this work, virtual screening was performed against two natural products databases, Super Natural II and Traditional Chinese Medicine. Additionally, we have used an integrated drug repurposing approach to computationally identify potential inhibitors of the main protease of SARS-CoV-2 in databases of drugs (both approved and withdrawn). Roughly 360,000 compounds were screened using various molecular fingerprints and molecular docking methods; of these, 80 docked compounds were evaluated in detail, and the 12 best hits from four datasets were further inspected via molecular dynamics simulations. Finally, toxicity and cytochrome inhibition profiles were computationally analyzed for the selected candidate compounds.

Structural Insights into the Binding Modes of Viral RNA-Dependent RNA Polymerases Using a Function-Site Interaction Fingerprint Method for RNA Virus Drug Discovery.

Abstract: The coronavirus disease of 2019 (COVID-19) pandemic speaks to the need for drugs that not only are effective but also remain effective given the mutation rate of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). To this end, we describe structural binding-site insights for facilitating COVID-19 drug design when targeting RNA-dependent RNA polymerase (RDRP), a common conserved component of RNA viruses. We combined an RDRP structure data set, including 384 RDRP PDB structures and all corresponding RDRP-ligand interaction fingerprints, thereby revealing the structural characteristics of the active sites for application to RDRP-targeted drug discovery. Specifically, we revealed the intrinsic ligand-binding modes and associated RDRP structural characteristics. Four types of binding modes with corresponding binding pockets were determined, suggesting two major subpockets available for drug discovery. We screened a drug data set of 7894 compounds against these binding pockets and presented the top-10 small molecules as a starting point in further exploring the prevention of virus replication. In summary, the binding characteristics determined here help rationalize RDRP-targeted drug discovery and provide insights into the specific binding mechanisms important for containing the SARS-CoV-2 virus.

Overview of Current Type I/II Kinase Inhibitors

Abstract: Research on kinase-targeting drugs has made great strides over the last 30 years and is attracting greater attention for the treatment of yet more kinase-related diseases. Currently, 42 kinase drugs have been approved by the FDA, most of which (Wilson et al., Cancer Research 78(1):15–29, 2018) are Type I/II inhibitors. Notwithstanding these advances, it is desirable to target additional kinases for drug development as more than 200 diseases, particularly cancers, are directly associated with aberrant kinase regulation and signaling. Here, we review the extant Type I/II drugs systematically to obtain insights into the binding pocket characteristics, the associated features of Type I/II drugs, and the mechanism of action to facilitate future kinase drug design and discovery. We conclude by summarizing the main successes and limitations of targeting kinases for the development of drugs.

Revealing Acquired Resistance Mechanisms of Kinase-Targeted Drugs Using an on-the-Fly, Function-Site Interaction Fingerprint Approach.

Abstract: Although kinase-targeted drugs have achieved significant clinical success, they are frequently subject to the limitations of drug resistance, which has become a primary vulnerability to targeted drug therapy. Therefore, deciphering resistance mechanisms is an important step in designing more efficacious, antiresistant drugs. Here we studied two FDA-approved kinase drugs: Crizotinib and Ceritinib, which are first- and second-generation anaplastic lymphoma kinase (ALK) targeted inhibitors, to unravel drug-resistance mechanisms. We used an on-the-fly, function-site interaction fingerprint (on-the-fly Fs-IFP) approach, combining binding free-energy surface calculations with the Fs-IFPs. Establishing the potentials of mean force and monitoring the atomic-scale protein-ligand interactions, before and after L1196M-induced drug resistance, revealed insights into drug-resistance/antiresistant mechanisms. Crizotinib prefers to bind the wild-type ALK kinase domain, whereas Ceritinib binds more favorably to the mutated ALK kinase domain, in agreement with experimental results. We determined that ALK kinase-drug interactions in the region of the front pocket are associated with drug resistance. Additionally, we find that the L1196M mutation does not simply alter the binding modes of inhibitors but also affects the flexibility of the entire ALK kinase domain. Our work provides an understanding of the mechanisms of ALK drug resistance, confirms the usefulness of the on-the-fly Fs-IFP approach, and provides a practical paradigm to study drug-resistance mechanisms in prospective drug discovery.

Ten simple rules for starting research in your late teens

Abstract: The Ten Simple Rules (TSR) series covers topics ranging from the very broad (e.g., career paths and scientific communication) to the more specific (e.g., illustrating figures and managing software), and all the various TSRs focus on one’s scientific and professional development [1]. The present TSR shares that goal and is authored by consumers and suppliers of research opportunities. Here, the consumers are individuals in their late teenage years, i.e., late high school (LHS) or early college (EC), who are either considering or actively searching for their first opportunity in a research lab at a university, national lab, or beyond (authors AMN and JC). The suppliers are university researchers (CM and PEB), along with the views of a seasoned educator (MC). We write this TSR for 3 reasons. First, research requests have become more frequent in recent years and, while our general area has been computational biology, that’s probably secondary: The Rules articulated here may apply equally well across many disciplines. Second, in the past decade or so, there has been an astonishing increase in the intensity of HS students, on many fronts—in terms of technical skill sets (e.g., mastery of programming languages), academic preparation and scientific sophistication (e.g., courses in advanced math), and beyond (e.g., career-related ambitions, such as searching for research opportunities!). Finally, some of these HS students who spent time in our laboratories have gone on to productive and rewarding research careers, underscoring that this is a formative stage in one’s scientific career. Reflecting on these experiences, through the eyes of trainee and mentor, we hope that this TSR affords some useful tips on whether research is right for you, how to go about procuring a research position, and the broader topic of navigating the LHS/EC stage of your own scientific trajectory.

Ten simple rules for researchers while in isolation from a pandemic

Abstract: The scale and intensity of the coronavirus disease 2019 (COVID-19) worldwide pandemic is unprecedented in all our lifetimes This is written for all of us involved in scientific research - graduate student, postdoc, academic, staff scientist, in academia, government or industry Rule 3: Follow institutional guidance and provide feedback By institution, we mean everything from the government (federal, state, and local) to your workplace to your individual laboratory Rule 4: Embrace a new work habit and environment How science is conducted has changed, almost overnight [Extracted from the article] Copyright of PLoS Computational Biology is the property of Public Library of Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission However, users may print, download, or email articles for individual use This abstract may be abridged No warranty is given about the accuracy of the copy Users should refer to the original published version of the material for the full abstract (Copyright applies to all Abstracts )

Ten simple rules for writing scientific op-ed articles.

Abstract: Op-eds, or opinionated editorial essays, are opinion pieces typically written for newspapers or magazines and intended for a wide audience. There are op-ed writers who specialize in writing broadly, and there are subject matter experts that focus on specific topics. Apart from other means of online outreach [1], an op-ed is an effective way to express a widely disseminated opinion about a topic. As a scientist, you get the prestige and satisfaction of expressing your point of view in a competitive, most-read section of a major publication. In the best case, it could influence decision-making and make a difference [2]. Op-eds are not like writing a scientific article nor is the process to publication the same. We offer ten simple rules as guidance, based on our experiences as op-ed writers and columnists.

Ten simple rules for more objective decision-making.

Abstract: Scientists spend their lives analyzing data by the systematic study of the structure and behavior of the physical and natural world using both observation and experiment—objective analysis. But when it comes to decision-making, scientists are also humans with accompanying subjectivity. Put colloquially, we have both heart and head—and are capable of being simultaneously subjective and objective. Here we posit that bringing more objectivity (\"head\") to decisions is a good thing. It’s a key part of \"critical thinking,\" the \"Socratic questioning\" method. We are not suggesting, that like Mr. Spock, we should be driven entirely by rationality, nor are we considering the merits of various reasoning systems [1]; we are simply examining why greater objectivity helps in providing a simple way to achieve improved objectivity. So, to start, is objectivity indeed better than subjectivity? To address this question, it’s useful to look at the 2 opposite ends of the spectrum: objectivity is really the application of pure logic (something is either right or wrong, more or less, etc.), whereas subjectivity [2] is embodied in the form of what is often called Cartesian Doubt or skepticism (that the knowledge of anything outside ones direct experience has to be considered as unsure). In certain cases, increased objectivity is superior, for example, when the decision being taken leads toward a measurable or quantifiable outcome: if there is a specific goal in mind, then it’s very useful to be able to estimate how close that decision might get you to that goal before you set out on the path. In real life, most decisions are a mixture of head and heart, but with these rules, we hope to increase both the accuracy and quantity of the head part while not neglecting the heart. But enough of the epistemological concepts, what we want is to make better decisions (better here being more objective) and look at 10 ways in which we might do this, culminating in a simple tool that anyone with a spreadsheet (or even a pen and paper) can use. Each rule is accompanied by a use case, some drawn from 2 previous Ten simple rules: Ten simple rules for graduate students [3] and Ten simple rules for selecting a postdoctoral position [4]. We will culminate with a worked example that illustrates this approach. Every lab needs a good coffee machine, and we are inspired by the example of the famous Trojan Room coffee pot. Based in the old computer laboratory of the University of Cambridge, England, in 1991, it provided the inspiration for the world’s first webcam [5]. So here we show how to make sure your coffee is well up to par! PLOS COMPUTATIONAL BIOLOGY

Deep Learning of Protein Structural Classes: Any Evidence for an ‘Urfold’?

Abstract: Recent computational advances in the accurate prediction of protein three-dimensional (3D) structures from amino acid sequences now present a unique opportunity to decipher the interrelationships between proteins. This task entails—but is not equivalent to—a problem of 3D structure comparison and classification. Historically, protein domain classification has been a largely manual and subjective activity, relying upon various heuristics. Databases such as CATH represent significant steps towards a more systematic (and automatable) approach, yet there still remains much room for the development of more scalable and quantitative classification methods, grounded in machine learning. We suspect that re-examining these relationships via a Deep Learning (DL) approach may entail a large-scale restructuring of classification schemes, improved with respect to the interpretability of distant relationships between proteins. Here, we describe our training of DL models on protein domain structures (and their associated physicochemical properties) in order to evaluate classification properties at CATH’s “homologous superfamily” (SF) level. To achieve this, we have devised and applied an extension of image-classification methods and image segmentation techniques, utilizing a convolutional autoencoder model architecture. Our DL architecture allows models to learn structural features that, in a sense, ‘define’ different homologous SFs. We evaluate and quantify pairwise ‘distances’ between SFs by building one model per SF and comparing the loss functions of the models. Hierarchical clustering on these distance matrices provides a new view of protein interrelationships—a view that extends beyond simple structural/geometric similarity, and towards the realm of structure/function properties.

Deep Learning of Protein Structural Classes: Any Evidence for an 'Urfold'?

Abstract: Recent computational advances in the accurate prediction of protein three-dimensional (3D) structures from amino acid sequences now present a unique opportunity to decipher the interrelationships between proteins. This task entails--but is not equivalent to--a problem of 3D structure comparison and classification. Historically, protein domain classification has been a largely manual and subjective activity, relying upon various heuristics. Databases such as CATH represent significant steps towards a more systematic (and automatable) approach, yet there still remains much room for the development of more scalable and quantitative classification methods, grounded in machine learning. We suspect that re-examining these relationships via a Deep Learning (DL) approach may entail a large-scale restructuring of classification schemes, improved with respect to the interpretability of distant relationships between proteins. Here, we describe our training of DL models on protein domain structures (and their associated physicochemical properties) in order to evaluate classification properties at CATH's "homologous superfamily" (SF) level. To achieve this, we have devised and applied an extension of image-classification methods and image segmentation techniques, utilizing a convolutional autoencoder model architecture. Our DL architecture allows models to learn structural features that, in a sense, 'define' different homologous SFs. We evaluate and quantify pairwise 'distances' between SFs by building one model per SF and comparing the loss functions of the models. Hierarchical clustering on these distance matrices provides a new view of protein interrelationships--a view that extends beyond simple structural/geometric similarity, and towards the realm of structure/function properties.