HIV-1 Nef promotes infection by excluding SERINC5 from virion incorporation.

HIV-1 Nef, a protein important for the development of AIDS, has well-characterized effects on host membrane trafficking and receptor downregulation. By an unidentified mechanism, Nef increases the intrinsic infectivity of HIV-1 virions in a host-cell-dependent manner. Here we identify the host transmembrane protein SERINC5, and to a lesser extent SERINC3, as a potent inhibitor of HIV-1 particle infectivity that is counteracted by Nef. SERINC5 localizes to the plasma membrane, where it is efficiently incorporated into budding HIV-1 virions and impairs subsequent virion penetration of susceptible target cells. Nef redirects SERINC5 to a Rab7-positive endosomal compartment and thereby excludes it from HIV-1 particles. The ability to counteract SERINC5 was conserved in Nef encoded by diverse primate immunodeficiency viruses, as well as in the structurally unrelated glycosylated Gag from murine leukaemia virus. These examples of functional conservation and convergent evolution emphasize the fundamental importance of SERINC5 as a potent anti-retroviral factor.

CATCHing putative causative variants in consanguineous families.

BACKGROUND: Consanguinity is an important risk factor for autosomal recessive (AR) disorders. Extended genomic regions identical by descent (IBD) in the offspring of consanguineous parents give rise to recessive disorders with identical (homozygous) pathogenic variants in both alleles. However, many clinical phenotypes presenting in the offspring of consanguineous couples are still of unknown etiology. Nowadays advances in High Throughput Sequencing provide an excellent opportunity to achieve a molecular diagnosis or to identify novel candidate genes. RESULTS: To exploit all available information from the family structure we developed CATCH, an algorithm that combines genotyped SNPs of all family members for the optimal detection of Runs Of Homozygosity (ROH) and exome sequencing data from one affected individual to identify putative causative variants in consanguineous families. CONCLUSIONS: CATCH proved to be effective in discovering known or putative new causative variants in 43 out of 50 consanguineous families. Among them, novel variants causative of familial thrombocytopenia, sclerosis bone dysplasia and the first homozygous loss-of-function mutation in FGFR3 in human causing severe skeletal deformities, tall stature and hearing impairment were identified.

EMdeCODE: a novel algorithm capable of reading words of epigenetic code to predict enhancers and retroviral integration sites and to identify H3R2me1 as a distinctive mark of coding versus non-coding genes.

Existence of some extra-genetic (epigenetic) codes has been postulated since the discovery of the primary genetic code. Evident effects of histone post-translational modifications or DNA methylation over the efficiency and the regulation of DNA processes are supporting this postulation. EMdeCODE is an original algorithm that approximate the genomic distribution of given DNA features (e.g. promoter, enhancer, viral integration) by identifying relevant ChIPSeq profiles of post-translational histone marks or DNA binding proteins and combining them in a supermark. EMdeCODE kernel is essentially a two-step procedure: (i) an expectation-maximization process calculates the mixture of epigenetic factors that maximize the Sensitivity (recall) of the association with the feature under study; (ii) the approximated density is then recursively trimmed with respect to a control dataset to increase the precision by reducing the number of false positives. EMdeCODE densities improve significantly the prediction of enhancer loci and retroviral integration sites with respect to previous methods. Importantly, it can also be used to extract distinctive factors between two arbitrary conditions. Indeed EMdeCODE identifies unexpected epigenetic profiles specific for coding versus non-coding RNA, pointing towards a new role for H3R2me1 in coding regions.

Deciphering the code for retroviral integration target site selection.

Upon cell invasion, retroviruses generate a DNA copy of their RNA genome and integrate retroviral cDNA within host chromosomal DNA. Integration occurs throughout the host cell genome, but target site selection is not random. Each subgroup of retrovirus is distinguished from the others by attraction to particular features on chromosomes. Despite extensive efforts to identify host factors that interact with retrovirion components or chromosome features predictive of integration, little is known about how integration sites are selected. We attempted to identify markers predictive of retroviral integration by exploiting Precision-Recall methods for extracting information from highly skewed datasets to derive robust and discriminating measures of association. ChIPSeq datasets for more than 60 factors were compared with 14 retroviral integration datasets. When compared with MLV, PERV or XMRV integration sites, strong association was observed with STAT1, acetylation of H3 and H4 at several positions, and methylation of H2AZ, H3K4, and K9. By combining peaks from ChIPSeq datasets, a supermarker was identified that localized within 2 kB of 75% of MLV proviruses and detected differences in integration preferences among different cell types. The supermarker predicted the likelihood of integration within specific chromosomal regions in a cell-type specific manner, yielding probabilities for integration into proto-oncogene LMO2 identical to experimentally determined values. The supermarker thus identifies chromosomal features highly favored for retroviral integration, provides clues to the mechanism by which retrovirus integration sites are selected, and offers a tool for predicting cell-type specific proto-oncogene activation by retroviruses.