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Candida albicans is among the most prevalent invasive fungal pathogens for immunocompromised individuals and novel therapeutic approaches that involve immune response modulation are imperative. Absent in melanoma 2 (AIM2), a pattern recognition receptor for DNA sensing, is well recognized for its involvement in inflammasome formation and its crucial role in safeguarding the host against various pathogenic infections. However, the role of AIM2 in host defense against C. albicans infection remains uncertain. This study reveals that the gene expression of AIM2 is induced in human and mouse innate immune cells or tissues after C. albicans infection. Furthermore, compared to their wild-type (WT) counterparts, Aim2 -/- mice surprisingly exhibit resistance to C. albicans infection, along with reduced inflammation in the kidneys post-infection. The resistance of Aim2 -/- mice to C. albicans infection is not reliant on inflammasome or type I interferon production. Instead, Aim2 -/- mice display lower levels of apoptosis in kidney tissues following infection than WT mice. The deficiency of AIM2 in macrophages, but not in dendritic cells, results in a phenocopy of the resistance observed in Aim2 -/- mice against C. albican infection. The treatment of Clodronate Liposome, a reagent that depletes macrophages, also shows the critical role of macrophages in host defense against C. albican infection in Aim2 -/- mice. Furthermore, the reduction in apoptosis is observed in Aim2 -/- mouse macrophages following infection or treatment of DNA from C. albicans in comparison with controls. Additionally, higher levels of AKT activation are observed in Aim2 -/- mice, and treatment with an AKT inhibitor reverses the host resistance to C. albicans infection. The findings collectively demonstrate that AIM2 exerts a negative regulatory effect on AKT activation and enhances macrophage apoptosis, ultimately compromising host defense against C. albicans infection. This suggests that AIM2 and AKT may represent promising therapeutic targets for the management of fungal infections., (© 2024. The Author(s).)

Three prevalent SARS-CoV-2 variants of concern (VOCs) emerged and caused epidemic waves. It is essential to uncover advantageous mutations that cause the high transmissibility of VOCs. However, viral mutations are tightly linked, so traditional population genetic methods, including machine learning-based methods, cannot reliably detect mutations conferring a fitness advantage. In this study, we developed an approach based on the sequential occurrence order of mutations and the accelerated furcation rate in the pandemic-scale phylogenomic tree. We analyzed 3,777,753 high-quality SARS-CoV-2 genomic sequences and the epidemiology metadata using the Coronavirus GenBrowser. We found that two noncoding mutations at the same position (g.a28271-/u) may be crucial to the high transmissibility of Alpha, Delta, and Omicron VOCs although the noncoding mutations alone cannot increase viral transmissibility. Both mutations cause an A-to-U change at the core position -3 of the Kozak sequence of the N gene and significantly reduce the protein expression ratio of ORF9b to N. Using a convergent evolutionary analysis, we found that g.a28271-/u, S:p.P681H/R, and N:p.R203K/M occur independently on three VOC lineages, suggesting that coordinated changes of S, N, and ORF9b proteins are crucial to high viral transmissibility. Our results provide new insights into high viral transmissibility co-modulated by advantageous noncoding and nonsynonymous changes., Competing Interests: Conflict of interest statement. The authors declare no competing interests., (© The Author(s) 2023. Published by Oxford University Press on behalf of Society for Molecular Biology and Evolution.)

Candida albicans is an opportunistic pathogenic fungus, which tends to infect the host with defective immune function including cancer patients. A growing number of studies have shown that C. albicans infection increases the host susceptibility to cancer such as oral, gastric, and colorectal cancer. Cancer and anti-cancer treatment may also affect the colonization of C. albicans. C. albicans may promote the development of cancer by damaging mucosal epithelium, inducing the production of carcinogens, triggering chronic inflammation including Th17 cell-mediated immune response. In this article, we aim to elaborate the interaction between C. albicans and cancers development and summarize the potential molecular mechanisms, so as to provide theoretical basis for prevention, diagnosis and treatment of cancers., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Yu and Liu.)

Genomic epidemiology is important to study the COVID-19 pandemic, and more than two million severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genomic sequences were deposited into public databases. However, the exponential increase of sequences invokes unprecedented bioinformatic challenges. Here, we present the Coronavirus GenBrowser (CGB) based on a highly efficient analysis framework and a node-picking rendering strategy. In total, 1,002,739 high-quality genomic sequences with the transmission-related metadata were analyzed and visualized. The size of the core data file is only 12.20 MB, highly efficient for clean data sharing. Quick visualization modules and rich interactive operations are provided to explore the annotated SARS-CoV-2 evolutionary tree. CGB binary nomenclature is proposed to name each internal lineage. The pre-analyzed data can be filtered out according to the user-defined criteria to explore the transmission of SARS-CoV-2. Different evolutionary analyses can also be easily performed, such as the detection of accelerated evolution and ongoing positive selection. Moreover, the 75 genomic spots conserved in SARS-CoV-2 but non-conserved in other coronaviruses were identified, which may indicate the functional elements specifically important for SARS-CoV-2. The CGB was written in Java and JavaScript. It not only enables users who have no programming skills to analyze millions of genomic sequences, but also offers a panoramic vision of the transmission and evolution of SARS-CoV-2., (© The Author(s) 2022. Published by Oxford University Press.)