IL-7R signaling activates widespread VH and DH gene usage to drive antibody diversity in bone marrow B cells.

Generation of the primary antibody repertoire requires V(D)J recombination of hundreds of gene segments in the immunoglobulin heavy chain (Igh) locus. The role of interleukin-7 receptor (IL-7R) signaling in Igh recombination has been difficult to partition from its role in B cell survival and proliferation. With a detailed description of the Igh repertoire in murine IL-7Rα-/- bone marrow B cells, we demonstrate that IL-7R signaling profoundly influences VH gene selection during VH-to-DJH recombination. We find skewing toward 3' VH genes during de novo VH-to-DJH recombination more severe than the fetal liver (FL) repertoire and uncover a role for IL-7R signaling in DH-to-JH recombination. Transcriptome and accessibility analyses suggest reduced expression of B lineage transcription factors (TFs) and targets and loss of DH and VH antisense transcription in IL-7Rα-/- B cells. Thus, in addition to its roles in survival and proliferation, IL-7R signaling shapes the Igh repertoire by activating underpinning mechanisms.

Kodoja: A workflow for virus detection in plants using k-mer analysis of RNA-sequencing data.

RNA-sequencing of plant material allows for hypothesis-free detection of multiple viruses simultaneously. This methodology relies on bioinformatics workflows for virus identification. Most workflows are designed for human clinical data, and few go beyond sequence mapping for virus identification. We present a new workflow (Kodoja) for the detection of plant virus sequences in RNA-sequence data. Kodoja uses k-mer profiling at the nucleotide level and sequence mapping at the protein level by integrating two existing tools Kraken and Kaiju. Kodoja was tested on three existing RNA-seq datasets from grapevine, and two new RNA-seq datasets from raspberry. For grapevine, Kodoja was shown to be more sensitive than a method based on contig building and blast alignments (27 viruses detected compared to 19). The application of Kodoja to raspberry, showed that field-grown raspberries were infected by multiple viruses, and that RNA-seq can identify lower amounts of virus material than reverse transcriptase PCR. This work enabled the design of new PCR-primers for detection of Raspberry yellow net virus and Beet ringspot virus. Kodoja is a sensitive method for plant virus discovery in field samples and enables the design of more accurate primers for detection. Kodoja is available to install through Bioconda and as a tool within Galaxy.

Viral Diagnostics in Plants Using Next Generation Sequencing: Computational Analysis in Practice.

Viruses cause significant yield and quality losses in a wide variety of cultivated crops. Hence, the detection and identification of viruses is a crucial facet of successful crop production and of great significance in terms of world food security. Whilst the adoption of molecular techniques such as RT-PCR has increased the speed and accuracy of viral diagnostics, such techniques only allow the detection of known viruses, i.e., each test is specific to one or a small number of related viruses. Therefore, unknown viruses can be missed and testing can be slow and expensive if molecular tests are unavailable. Methods for simultaneous detection of multiple viruses have been developed, and (NGS) is now a principal focus of this area, as it enables unbiased and hypothesis-free testing of plant samples. The development of NGS protocols capable of detecting multiple known and emergent viruses present in infected material is proving to be a major advance for crops, nuclear stocks or imported plants and germplasm, in which disease symptoms are absent, unspecific or only triggered by multiple viruses. Researchers want to answer the question "how many different viruses are present in this crop plant?" without knowing what they are looking for: RNA-sequencing (RNA-seq) of plant material allows this question to be addressed. As well as needing efficient nucleic acid extraction and enrichment protocols, virus detection using RNA-seq requires fast and robust bioinformatics methods to enable host sequence removal and virus classification. In this review recent studies that use RNA-seq for virus detection in a variety of crop plants are discussed with specific emphasis on the computational methods implemented. The main features of a number of specific bioinformatics workflows developed for virus detection from NGS data are also outlined and possible reasons why these have not yet been widely adopted are discussed. The review concludes by discussing the future directions of this field, including the use of bioinformatics tools for virus detection deployed in analytical environments using cloud computing.

Two Mutually Exclusive Local Chromatin States Drive Efficient V(D)J Recombination.

Variable (V), diversity (D), and joining (J) (V(D)J) recombination is the first determinant of antigen receptor diversity. Understanding how recombination is regulated requires a comprehensive, unbiased readout of V gene usage. We have developed VDJ sequencing (VDJ-seq), a DNA-based next-generation-sequencing technique that quantitatively profiles recombination products. We reveal a 200-fold range of recombination efficiency among recombining V genes in the primary mouse Igh repertoire. We used machine learning to integrate these data with local chromatin profiles to identify combinatorial patterns of epigenetic features that associate with active VH gene recombination. These features localize downstream of VH genes and are excised by recombination, revealing a class of cis-regulatory element that governs recombination, distinct from expression. We detect two mutually exclusive chromatin signatures at these elements, characterized by CTCF/RAD21 and PAX5/IRF4, which segregate with the evolutionary history of associated VH genes. Thus, local chromatin signatures downstream of VH genes provide an essential layer of regulation that determines recombination efficiency.

Differential activity by polymorphic variants of a remote enhancer that supports galanin expression in the hypothalamus and amygdala: implications for obesity, depression and alcoholism.

The expression of the galanin gene (GAL) in the paraventricular nucleus (PVN) and in the amygdala of higher vertebrates suggests the requirement for highly conserved, but unidentified, regulatory sequences that are critical to allow the galanin gene to control alcohol and fat intake and modulate mood. We used comparative genomics to identify a highly conserved sequence that lay 42 kb 5' of the human GAL transcriptional start site that we called GAL5.1. GAL5.1 activated promoter activity in neurones of the PVN, arcuate nucleus and amygdala that also expressed the galanin peptide. Analysis in neuroblastoma cells demonstrated that GAL5.1 acted as an enhancer of promoter activity after PKC activation. GAL5.1 contained two polymorphisms; rs2513280(C/G) and rs2513281(A/G), that occurred in two allelic combinations (GG or CA) where the dominant GG alelle occurred in 70-83 % of the human population. Intriguingly, both SNPs were found to be in LD (R(2) of 0.687) with another SNP (rs2156464) previously associated with major depressive disorder (MDD). Recreation of these alleles in reporter constructs and subsequent magnetofection into primary rat hypothalamic neurones showed that the CA allele was 40 % less active than the GG allele. This is consistent with the hypothesis that the weaker allele may affect food and alcohol preference. The linkage of the SNPs analysed in this study with a SNP previously associated with MDD together with the functioning of GAL5.1 as a PVN and amygdala specific enhancer represent a significant advance in our ability to understand alcoholism, obesity and major depressive disorder.