Nitric Oxide Induces Cell Death by Regulating Anti-Apoptotic BCL-2 Family Members

Nitric oxide (NO) activates the intrinsic apoptotic pathway to induce cell death. However, the mechanism by which this pathway is activated in cells exposed to NO is not known. Here we report that BAX and BAK are activated by NO and that cytochrome c is released from the mitochondria. Cells deficient in Bax and Bak or Caspase-9 are completely protected from NO-induced cell death. The individual loss of the BH3-only proteins, Bim, Bid, Puma, Bad or Noxa, or Bid knockdown in Bim−/−/Puma−/− MEFs, does not prevent NO-induced cell death. Our data show that the anti-apoptotic protein MCL-1 undergoes ASK1-JNK1 mediated degradation upon exposure to NO, and that cells deficient in either Ask1 or Jnk1 are protected against NO-induced cell death. NO can inhibit the mitochondrial electron transport chain resulting in an increase in superoxide generation and peroxynitrite formation. However, scavengers of ROS or peroxynitrite do not prevent NO-induced cell death. Collectively, these data indicate that NO degrades MCL-1 through the ASK1-JNK1 axis to induce BAX/BAK-dependent cell death

Adaptation and cryptic pseudogenization in penguin Toll-like receptors

Penguins (Sphenisciformes) are an iconic order of flightless, diving seabirds distributed across a large latitudinal range in the Southern Hemisphere. The extensive area over which penguins are endemic is likely to have fostered variation in pathogen pressure, which in turn will have imposed differential selective pressures on the penguin immune system. At the front line of pathogen detection and response, the Toll-like receptors (TLRs) provide insight into host evolution in the face of microbial challenge. TLRs respond to conserved pathogen-associated molecular patterns and are frequently found to be under positive selection, despite retaining specificity for defined agonist classes. We undertook a comparative immunogenetics analysis of TLRs for all penguin species, and found evidence of adaptive evolution that was largely restricted to the cell surface expressed TLRs, with evidence of positive selection at, or near, key agonist-binding sites in TLR1B, TLR4 and TLR5. Intriguingly, TLR15, which is activated by fungal products, appeared to have been pseudogenized multiple times in the Eudyptes spp., but a full-length form was present as a rare haplotype at the population level. However, in vitro analysis revealed that even the full-length form of Eudyptes TLR15 was non-functional, indicating an ancestral cryptic pseudogenization prior to its eventual disruption multiple times in the Eudyptes lineage. This unusual pseudogenization event could provide an insight into immune adaptation to fungal pathogens such as Aspergillus, which is responsible for significant mortality in wild and captive bird populations

Aligning Protein-Coding Nucleotide Sequences with MACSE

International audienceMost genomic and evolutionary comparative analyses rely on accurate multiple sequence alignments. With their underlying codon structure, protein-coding nucleotide sequences pose a specific challenge for multiple sequence alignment. Multiple Alignment of Coding Sequences (MACSE) is a multiple sequence alignment program that provided the first automatic solution for aligning protein-coding gene datasets containing both functional and nonfunctional sequences (pseudogenes). Through its unique features, reliable codon alignments can be built in the presence of frameshifts and stop codons suitable for subsequent analysis of selection based on the ratio of nonsynonymous to synonymous substitutions. Here we offer a practical overview and guidelines on the use of MACSE v2. This major update of the initial algorithm now comes with a graphical interface providing user-friendly access to different subprograms to handle multiple alignments of protein-coding sequences. We also present new pipelines based on MACSE v2 subprograms to handle large datasets and distributed as Singularity containers. MACSE and associated pipelines are available at: https://bioweb.supagro.inra.fr/macse/