The physiological functions of natural killer (NK) cells in human immunity and reproduction depend upon diverse interactions between killer cell immunoglobulin-like receptors (KIRs) and their HLA class I ligands: HLA-A, HLA-B, and HLA-C. The genomic regions containing the KIR and HLA class I genes are unlinked, structurally complex, and highly polymorphic. They are also strongly associated with a wide spectrum of diseases, including infections, autoimmune disorders, cancers, and pregnancy disorders, as well as the efficacy of transplantation and other immunotherapies. To facilitate study of these extraordinary genes, we developed a method that captures, sequences, and analyzes the 13 KIR genes and HLA-A, HLA-B, and HLA-C from genomic DNA. We also devised a bioinformatics pipeline that attributes sequencing reads to specific KIR genes, determines copy number by read depth, and calls high-resolution genotypes for each KIR gene. We validated this method by using DNA from well-characterized cell lines, comparing it to established methods of HLA and KIR genotyping, and determining KIR genotypes from 1000 Genomes sequence data. This identified 116 previously uncharacterized KIR alleles, which were all demonstrated to be authentic by sequencing from source DNA via standard methods. Analysis of just two KIR genes showed that 22% of the 1000 Genomes individuals have a previously uncharacterized allele or a structural variant. The method we describe is suited to the large-scale analyses that are needed for characterizing human populations and defining the precise HLA and KIR factors associated with disease. The methods are applicable to other highly polymorphic genes.

The most polymorphic part of the human genome, the MHC, encodes over 160 proteins of diverse function. Half of them, including the HLA class I and II genes, are directly involved in immune responses. Consequently, the MHC region strongly associates with numerous diseases and clinical therapies. Notoriously, the MHC region has been intractable to high-throughput analysis at complete sequence resolution, and current reference haplotypes are inadequate for large-scale studies. To address these challenges, we developed a method that specifically captures and sequences the 4.8-Mbp MHC region from genomic DNA. For 95 MHC homozygous cell lines we assembled, de novo, a set of high-fidelity contigs and a sequence scaffold, representing a mean 98% of the target region. Included are six alternative MHC reference sequences of the human genome that we completed and refined. Characterization of the sequence and structural diversity of the MHC region shows the approach accurately determines the sequences of the highly polymorphic HLA class I and HLA class II genes and the complex structural diversity of complement factor C4A/C4B It has also uncovered extensive and unexpected diversity in other MHC genes; an example is MUC22, which encodes a lung mucin and exhibits more coding sequence alleles than any HLA class I or II gene studied here. More than 60% of the coding sequence alleles analyzed were previously uncharacterized. We have created a substantial database of robust reference MHC haplotype sequences that will enable future population scale studies of this complicated and clinically important region of the human genome.

The human brain has enormously complex cellular diversity and connectivities fundamental to our neural functions, yet difficulties in interrogating individual neurons has impeded understanding of the underlying transcriptional landscape. We developed a scalable approach to sequence and quantify RNA molecules in isolated neuronal nuclei from a postmortem brain, generating 3227 sets of single-neuron data from six distinct regions of the cerebral cortex. Using an iterative clustering and classification approach, we identified 16 neuronal subtypes that were further annotated on the basis of known markers and cortical cytoarchitecture. These data demonstrate a robust and scalable method for identifying and categorizing single nuclear transcriptomes, revealing shared genes sufficient to distinguish previously unknown and orthologous neuronal subtypes as well as regional identity and transcriptomic heterogeneity within the human brain.

We have used multiplexed high-throughput sequencing to characterize changes in small RNA populations that occur during viral infection in animal cells. Small RNA-based mechanisms such as RNA interference (RNAi) have been shown in plant and invertebrate systems to play a key role in host responses to viral infection. Although homologs of the key RNAi effector pathways are present in mammalian cells, and can launch an RNAi-mediated degradation of experimentally targeted mRNAs, any role for such responses in mammalian host-virus interactions remains to be characterized. Six different viruses were examined in 41 experimentally susceptible and resistant host systems. We identified virus-derived small RNAs (vsRNAs) from all six viruses, with total abundance varying from "vanishingly rare" (less than 0.1% of cellular small RNA) to highly abundant (comparable to abundant micro-RNAs "miRNAs"). In addition to the appearance of vsRNAs during infection, we saw a number of specific changes in host miRNA profiles. For several infection models investigated in more detail, the RNAi and Interferon pathways modulated the abundance of vsRNAs. We also found evidence for populations of vsRNAs that exist as duplexed siRNAs with zero to three nucleotide 3' overhangs. Using populations of cells carrying a Hepatitis C replicon, we observed strand-selective loading of siRNAs onto Argonaute complexes. These experiments define vsRNAs as one possible component of the interplay between animal viruses and their hosts.