New target to tackle coronaviruses
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Pathogens and planetary change
Emerging infectious diseases, biodiversity loss, and anthropogenic environmental change are interconnected crises with massive social and ecological costs. In this Review, we discuss how pathogens and parasites are responding to global change, and the implications for pandemic prevention and biodiversity conservation. Ecological and evolutionary principles help to explain why both pandemics and wildlife die-offs are becoming more common; why land-use change and biodiversity loss are often followed by an increase in zoonotic and vector-borne diseases; and why some species, such as bats, host so many emerging pathogens. To prevent the next pandemic, scientists should focus on monitoring and limiting the spread of a handful of high-risk viruses, especially at key interfaces such as farms and live-animal markets. But to address the much broader set of infectious disease risks associated with the Anthropocene, decision-makers will need to develop comprehensive strategies that include pathogen surveillance across species and ecosystems; conservation-based interventions to reduce human–animal contact and protect wildlife health; health system strengthening; and global improvements in epidemic preparedness and response. Scientists can contribute to these efforts by filling global gaps in disease data, and by expanding the evidence base for disease–driver relationships and ecological interventions.
Role of the humoral immune response during COVID-19: guilty or not guilty?
Systemic and mucosal humoral immune responses are crucial to fight respiratory viral infections in the current pandemic of COVID-19 caused by the SARS-CoV-2 virus. During SARS-CoV-2 infection, the dynamics of systemic and mucosal antibody infections are affected by patient characteristics, such as age, sex, disease severity, or prior immunity to other human coronaviruses. Patients suffering from severe disease develop higher levels of anti-SARS-CoV-2 antibodies in serum and mucosal tissues than those with mild disease, and these antibodies are detectable for up to a year after symptom onset. In hospitalized patients, the aberrant glycosylation of anti-SARS-CoV-2 antibodies enhances inflammation-associated antibody Fc-dependent effector functions, thereby contributing to COVID-19 pathophysiology. Current vaccines elicit robust humoral immune responses, principally in the blood. However, they are less effective against new viral variants, such as Delta and Omicron. This review provides an overview of current knowledge about the humoral immune response to SARS-CoV-2, with a particular focus on the protective and pathological role of humoral immunity in COVID-19 severity. We also discuss the humoral immune response elicited by COVID-19 vaccination and protection against emerging viral variants.
The comprehensive SARS-CoV-2 ‘hijackome’ knowledge base
The continuous evolution of SARS-CoV-2 has led to the emergence of several variants of concern (VOCs) that significantly affect global health. This study aims to investigate how these VOCs affect host cells at proteome level to better understand the mechanisms of disease. To achieve this, we first analyzed the (phospho)proteome changes of host cells infected with Alpha, Beta, Delta, and Omicron BA.1 and BA.5 variants over time frames extending from 1 to 36 h post infection. Our results revealed distinct temporal patterns of protein expression across the VOCs, with notable differences in the (phospho)proteome dynamics that suggest variant-specific adaptations. Specifically, we observed enhanced expression and activation of key components within crucial cellular pathways such as the RHO GTPase cycle, RNA splicing, and endoplasmic reticulum-associated degradation (ERAD)-related processes. We further utilized proximity biotinylation mass spectrometry (BioID-MS) to investigate how specific mutation of these VOCs influence viral–host protein interactions. Our comprehensive interactomics dataset uncovers distinct interaction profiles for each variant, illustrating how specific mutations can change viral protein functionality. Overall, our extensive analysis provides a detailed proteomic profile of host cells for each variant, offering valuable insights into how specific mutations may influence viral protein functionality and impact therapeutic target identification. These insights are crucial for the potential use and design of new antiviral substances, aiming to enhance the efficacy of treatments against evolving SARS-CoV-2 variants.
Breaking barriers: we need a multidisciplinary approach to tackle cancer drug resistance
Most cancer-related deaths result from drug-resistant disease(1,2). However, cancer drug resistance is not a primary focus in drug development. Effectively mitigating and treating drug-resistant cancer will require advancements in multiple fields, including early detection, drug discovery, and our fundamental understanding of cancer biology. Therefore, successfully tackling drug resistance requires an increasingly multidisciplinary approach. A recent workshop on cancer drug resistance, jointly organised by Cancer Research UK, the Rosetrees Trust, and the UKRI-funded Physics of Life Network, brought together experts in cell biology, physical sciences, computational biology, drug discovery, and clinicians to focus on these key challenges and devise interdisciplinary approaches to address them. In this perspective, we review the outcomes of the workshop and highlight unanswered research questions. We outline the emerging hallmarks of drug resistance and discuss lessons from the COVID-19 pandemic and antimicrobial resistance that could help accelerate information sharing and timely adoption of research discoveries into the clinic. We envisage that initiatives that drive greater interdisciplinarity will yield rich dividends in developing new ways to better detect, monitor, and treat drug resistance, thereby improving treatment outcomes for cancer patients.
Modeling the impact of structure and coverage on the reactivity of realistic heterogeneous catalysts
Adsorbates often cover the surfaces of catalysts densely as they carry out reactions, dynamically altering their structure and reactivity. Understanding adsorbate-induced phenomena and harnessing them in our broader quest for improved catalysts is a substantial challenge that is only beginning to be addressed. Here we chart a path toward a deeper understanding of such phenomena by focusing on emerging in silico modeling methodologies, which will increasingly incorporate machine learning techniques. We first examine how adsorption on catalyst surfaces can lead to local and even global structural changes spanning entire nanoparticles, and how this affects their reactivity. We then evaluate current efforts and the remaining challenges in developing robust and predictive simulations for modeling such behavior. Last, we provide our perspectives in four critical areas—integration of artificial intelligence, building robust catalysis informatics infrastructure, synergism with experimental characterization, and adaptive modeling frameworks—that we believe can help surmount the remaining challenges in rationally designing catalysts in light of these complex phenomena.
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