Interindividual clinical variability in the course of SARS-CoV-2 infection is immense. We report that at least 101 of 987 patients with life-threatening COVID-19 pneumonia had neutralizing IgG auto-Abs against IFN-ω (13 patients), the 13 types of IFN-α (36), or both (52), at the onset of critical disease; a few also had auto-Abs against the other three type I IFNs. The auto-Abs neutralize the ability of the corresponding type I IFNs to block SARS-CoV-2 infection in vitro. These auto-Abs were not found in 663 individuals with asymptomatic or mild SARS-CoV-2 infection and were present in only 4 of 1,227 healthy individuals. Patients with auto-Abs were aged 25 to 87 years and 95 were men. A B cell auto-immune phenocopy of inborn errors of type I IFN immunity underlies life-threatening COVID-19 pneumonia in at least 2.6% of women and 12.5% of men.

Immunoresponsive gene 1 (Irg1) is highly expressed in mammalian macrophages during inflammation, but its biological function has not yet been elucidated. Here, we identify Irg1 as the gene coding for an enzyme producing itaconic acid (also known as methylenesuccinic acid) through the decarboxylation of cis-aconitate, a tricarboxylic acid cycle intermediate. Using a gain-and-loss-of-function approach in both mouse and human immune cells, we found Irg1 expression levels correlating with the amounts of itaconic acid, a metabolite previously proposed to have an antimicrobial effect. We purified IRG1 protein and identified its cis-aconitate decarboxylating activity in an enzymatic assay. Itaconic acid is an organic compound that inhibits isocitrate lyase, the key enzyme of the glyoxylate shunt, a pathway essential for bacterial growth under specific conditions. Here we show that itaconic acid inhibits the growth of bacteria expressing isocitrate lyase, such as Salmonella enterica and Mycobacterium tuberculosis. Furthermore, Irg1 gene silencing in macrophages resulted in significantly decreased intracellular itaconic acid levels as well as significantly reduced antimicrobial activity during bacterial infections. Taken together, our results demonstrate that IRG1 links cellular metabolism with immune defense by catalyzing itaconic acid production.

The Ras superfamily is a fascinating example of functional diversification in the context of a preserved structural framework and a prototypic GTP binding site. Thanks to the availability of complete genome sequences of species representing important evolutionary branch points, we have analyzed the composition and organization of this superfamily at a greater level than was previously possible. Phylogenetic analysis of gene families at the organism and sequence level revealed complex relationships between the evolution of this protein superfamily sequence and the acquisition of distinct cellular functions. Together with advances in computational methods and structural studies, the sequence information has helped to identify features important for the recognition of molecular partners and the functional specialization of different members of the Ras superfamily.

The divergence accumulated during the evolution of protein families translates into their internal organization as subfamilies, and it is directly reflected in the characteristic patterns of differentially conserved residues. These specifically conserved positions in protein subfamilies are known as "specificity determining positions" (SDPs). Previous studies have limited their analysis to the study of the relationship between these positions and ligandbinding specificity, demonstrating significant yet limited predictive capacity. We have systematically extended this observation to include the role of differential protein interactions in the segregation of protein subfamilies and explored in detail the structural distribution of SDPs at protein interfaces. Our results show the extensive influence of protein interactions in the evolution of protein families and the widespread association of SDPs with protein interfaces. The combined analysis of SDPs in interfaces and ligandbinding sites provides a more complete picture of the organization of protein families, constituting the necessary framework for a large scale analysis of the evolution of protein function.functional residues | protein family evolution | protein function | protein-protein interfaces | specificity determining positions T he structure of protein families is shaped by the sequence divergence accumulated as a consequence of speciation, gene duplication, and deletion events, as well as by the evolutionary selective pressure exerted on each protein in accordance with the corresponding 3D structure and the specific function performed (1, 2). The balance between genomic rearrangements and selective pressure to increase the functional repertoire available to organisms leads to the appearance of new subfamilies in evolutionary time (3).There are many aspects of protein function that contribute to the evolution of the family organization. These may include the global conservation of catalytic mechanisms (in the case of enzymes), specific binding to substrates and cofactors, as well as the interaction with other proteins in processes such as cell signaling, the regulation of reactions and the formation of macromolecular complexes. Interestingly, even though specific protein interactions certainly are an important part of protein function, the organization of protein families in relation to the specific interactions of different subfamilies remains a poorly studied aspect of functional specificity.Multiple sequences alignments (MSAs) provide essential information on the evolution of protein families. The positions in MSAs can be interpreted in terms of the amino acid changes allowed or disallowed during evolution, and therefore useful information at the residue level can be inferred from them (4). The most obvious example is the study of fully conserved positions that pinpoint important residues for the structure and function of the family members (5).A subtler pattern of conservation is represented by the positions differentially conserved within subfamili...

The exhaustive exploration of human cell heterogeneity requires the unbiased identification of molecular signatures that can serve as unique cell identity cards for every cell in the body. However, the stochasticity associated with high-throughput single-cell sequencing has made it necessary to use clustering-based computational approaches in which the characterization of cell-type heterogeneity is performed at cellsubpopulation level rather than at full single-cell resolution. We present here Cell-ID, a clustering-free multivariate statistical method for the robust extraction of per-cell gene signatures from single-cell sequencing data. Cell-ID signatures allow unbiased cell identity recognition across different donors, tissues-of-origin, model organisms and single-cell omics technologies. Cell-ID is distributed as an open-source R software package: https://github.com/RausellLab/CelliD.