Expression cloning of a receptor for C5a anaphylatoxin on differentiated HL-60 cells

A cDNA clone encoding the human C5a anaphylatoxin receptor has been isolated by expression cloning from a CDM8 expression library prepared from mRNA of human myeloid HL-60 cells differentiated to the granulocyte phenotype with dibutyryladenosine cyclic monophosphate. The cDNA clone was able to transfer to COS-7 cells the capacity to specifically bind iodinated human recombinant C5a. The cDNA was 2.3 kb long, with an open reading frame encoding a 350-residue polypeptide. Cross-linking of iodinated C5a to the plasma membrane of transfected COS cells revealed a complex with an apparent molecular mass of 52-55 kDa, similar to that observed for the constitutively expressed receptor in differentiated HL-60 cells or human neutrophils. Although differentiated HL-60 cells display a single class of binding sites, with a dissociation constant of approximately 800-900 pM, the C5a-R cDNA, expressed in COS cells, generates both high-affinity (1.7 nM) and low-affinity (20-25 nM) receptors. Sequence comparison established that the degree of sequence identity between the C5a receptor and the N-formylpeptide receptor is 34%.     

Creating artificial human genomes using generative neural networks

Generative models have shown breakthroughs in a wide spectrum of domains due to recent advancements in machine learning algorithms and increased computational power. Despite these impressive achievements, the ability of generative models to create realistic synthetic data is still under-exploited in genetics and absent from population genetics. Yet a known limitation in the field is the reduced access to many genetic databases due to concerns about violations of individual privacy, although they would provide a rich resource for data mining and integration towards advancing genetic studies. In this study, we demonstrated that deep generative adversarial networks (GANs) and restricted Boltzmann machines (RBMs) can be trained to learn the complex distributions of real genomic datasets and generate novel high-quality artificial genomes (AGs) with none to little privacy loss. We show that our generated AGs replicate characteristics of the source dataset such as allele frequencies, linkage disequilibrium, pairwise haplotype distances and population structure. Moreover, they can also inherit complex features such as signals of selection. To illustrate the promising outcomes of our method, we showed that imputation quality for low frequency alleles can be improved by data augmentation to reference panels with AGs and that the RBM latent space provides a relevant encoding of the data, hence allowing further exploration of the reference dataset and features for solving supervised tasks. Generative models and AGs have the potential to become valuable assets in genetic studies by providing a rich yet compact representation of existing genomes and high-quality, easy-access and anonymous alternatives for private databases.