The failure to clear apoptotic cells is linked to defects in development and autoimmunity. Complement component C1q is required for efficient engulfment of apoptotic cells (efferocytosis), and C1q deficiency leads to the development of lupus. We recently identified a novel molecular mechanism for C1q-dependent efferocytosis in murine macrophages. C1q elicited the expression of Mer tyrosine kinase (Mer), a receptor that regulates efficient efferocytosis and prevention of autoimmunity. To characterize the C1q-dependent signal transduction mechanism, pathway analysis of the transcriptome from C1q-activated macrophages was performed, and it identified the adiponectin signaling pathway as significantly upregulated with C1q. Adiponectin is structurally homologous to C1q and regulates cellular metabolism via downstream activation of 5adenosine monophosphate-activated protein kinase (AMPK). Macrophage stimulation with C1q resulted in the activation of AMPK, and silencing of AMPK expression using siRNA-inhibited C1q-dependent efferocytosis. Adiponectin signaling also stimulates activation of nuclear receptors, and inhibition of the nuclear receptor retinoid X receptor abrogated C1q-dependent Mer expression and efferocytosis. Furthermore, adiponectin elicited Mer expression and Mer-dependent efferocytosis in macrophages similar to cells stimulated with C1q. Collectively, our results suggest that C1q and adiponectin share a common signal transduction cascade to promote clearance of apoptotic cells, and identify a novel molecular pathway required for efficient efferocytosis.

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.