Lim, T. | Search bioRxiv

bioRxiv2018

Maternal immunity and antibodies to dengue promote Zika virus-induced microcephaly in fetuses

Zika virus (ZIKV), a recently emerged flaviviral pathogen, has been linked to microcephaly in neonates1. Yet, it is not understood why some fetuses develop severe microcephaly due to maternal ZIKV infection while others do not. The risk for ZIKV-induced microcephaly is greatest during the first trimester of pregnancy in humans2,3, yet this alone cannot account for the varied presentation of microcephaly observed. Given the antigenic similarity between ZIKV and closely related dengue virus (DENV)4, combined with the substantial immunity to DENV in ZIKV target populations in recent outbreaks, we hypothesized that maternal antibodies against DENV could promote ZIKV-induced microcephaly. Here, using immune-competent mice, we show that maternal to fetal transmission of ZIKV occurs, leading to fetal infection and disproportionate microcephaly. DENV-specific antibodies in pregnant female mice enhance vertical transmission of infection and result in a severe microcephaly like-syndrome during ZIKV infection. Furthermore, fetal infection was promoted by the neonatal Fc receptor (FcRN). Our results identify a novel immune-mediated mechanism of vertical transmission of viral infection and raise caution since ZIKV epidemic regions are also endemic to DENV.

immunology↗

bioRxiv2017

A Multi-Species Functional Embedding Integrating Sequence and Network Structure

A key challenge to transferring knowledge between species is that different species have fundamentally different genetic architectures. Initial computational approaches to transfer knowledge across species have relied on measures of heredity such as genetic homology, but these approaches suffer from limitations. First, only a small subset of genes have homologs, limiting the amount of knowledge that can be transferred, and second, genes change or repurpose functions, complicating the transfer of knowledge. Many approaches address this problem by expanding the notion of homology by leveraging high-throughput genomic and proteomic measurements, such as through network alignment.\n\nIn this work, we take a new approach to transferring knowledge across species by expanding the notion of homology through explicit measures of functional similarity between proteins in different species. Specifically, our kernel-based method, HO_SCPLOWANDLC_SCPLOW (Homology Assessment across Networks using Diffusion and Landmarks), integrates sequence and network structure to create a functional embedding in which proteins from different species are embedded in the same vector space. We show that inner products in this space capture functional similarity across species, and the vectors themselves are useful for a variety of cross species tasks. We perform the first whole-genome method for predicting phenologs, generating many that were previously identified, but also predicting new phenologs supported from the biological literature. We also demonstrate the HO_SCPLOWANDLC_SCPLOW-embedding captures pairwise gene function, in that gene pairs with synthetic lethal interactions are co-located in HO_SCPLOWANDLC_SCPLOW-space both within and across species. Software for the HO_SCPLOWANDLC_SCPLOW algorithm is available at http://github.com/lrgr/HANDL.

bioinformatics↗