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Biological sequence families contain many sequences that are very similar to each other because they are related by evolution, so the strategy for splitting data into separate training and test sets is a nontrivial choice in benchmarking sequence analysis methods. A random split is insufficient because it will yield test sequences that are closely related or even identical to training sequences. Adapting ideas from independent set graph algorithms, we describe two new methods for splitting sequence data into dissimilar training and test sets. These algorithms input a sequence family and produce a split in which each test sequence is less than p % identical to any individual training sequence. These algorithms successfully split more families than a previous approach, enabling construction of more diverse benchmark datasets. 

Genes for which homologs can be detected only in a limited group of evolutionarily related species, called “lineage-specific genes,” are pervasive: Essentially every lineage has them, and they often comprise a sizable fraction of the group’s total genes. Lineage-specific genes are often interpreted as “novel” genes, representing genetic novelty born anew within that lineage. Here, we develop a simple method to test an alternative null hypothesis: that lineage-specific genes do have homologs outside of the lineage that, even while evolving at a constant rate in a novelty-free manner, have merely become undetectable by search algorithms used to infer homology. We show that this null hypothesis is sufficient to explain the lack of detected homologs of a large number of lineage-specific genes in fungi and insects. However, we also find that a minority of lineage-specific genes in both clades are not well explained by this novelty-free model. The method provides a simple way of identifying which lineage-specific genes call for special explanations beyond homology detection failure, highlighting them as interesting candidates for further study. 

Cutaneous lupus erythematosus (CLE) is a chronic inflammatory skin disease characterized by a diverse cadre of clinical presentations. CLE commonly occurs in patients with systemic lupus erythematosus (SLE), and CLE can also develop in the absence of systemic disease. Although CLE is a complex and heterogeneous disease, several studies have identified common signaling pathways, including those of type I interferons (IFNs), that play a key role in driving cutaneous inflammation across all CLE subsets. However, discriminating factors that drive different phenotypes of skin lesions remain to be determined. Thus, we sought to understand the skin-associated cellular and transcriptional differences in CLE subsets and how the different types of cutaneous inflammation relate to the presence of systemic lupus disease. In this study, we utilized two distinct cohorts comprising a total of 150 CLE lesional biopsies to compare discoid lupus erythematosus (DLE), subacute cutaneous lupus erythematosus (SCLE), and acute cutaneous lupus erythematosus (ACLE) in patients with and without associated SLE. Using an unbiased approach, we demonstrated a CLE subtype-dependent gradient of B cell enrichment in the skin, with DLE lesions harboring a more dominant skin B cell transcriptional signature and enrichment of B cells on immunostaining compared to ACLE and SCLE. Additionally, we observed a significant increase in B cell signatures in the lesional skin from patients with isolated CLE compared with similar lesions from patients with systemic lupus. This trend was driven primarily by differences in the DLE subgroup. Our work thus shows that skin-associated B cell responses distinguish CLE subtypes in patients with and without associated SLE, suggesting that B cell function in skin may be an important link between cutaneous lupus and systemic disease activity.