Transformer (TRA) promotes female development in several dipteran species including the Australian sheep blowfly Lucilia cuprina, the Mediterranean fruit fly, housefly and Drosophila melanogaster. tra transcripts are sex-specifically spliced such that only the female form encodes full length functional protein. The presence of six predicted TRA/TRA2 binding sites in the sex-specific female intron of the L. cuprina gene suggested that tra splicing is auto-regulated as in medfly and housefly. With the aim of identifying conserved motifs that may play a role in tra sex-specific splicing, here we have isolated and characterized the tra gene from three additional blowfly species, L. sericata, Cochliomyia hominivorax and C. macellaria. The blowfly adult male and female transcripts differ in the choice of splice donor site in the first intron, with males using a site downstream of the site used in females. The tra genes all contain a single TRA/TRA2 site in the male exon and a cluster of four to five sites in the male intron. However, overall the sex-specific intron sequences are poorly conserved in closely related blowflies. The most conserved regions are around the exon/intron junctions, the 3′ end of the intron and near the cluster of TRA/TRA2 sites. We propose a model for sex specific regulation of tra splicing that incorporates the conserved features identified in this study. In L. sericata embryos, the male tra transcript was first detected at around the time of cellular blastoderm formation. RNAi experiments showed that tra is required for female development in L. sericata and C. macellaria. The isolation of the tra gene from the New World screwworm fly C. hominivorax, a major livestock pest, will facilitate the development of a “male-only” strain for genetic control programs.

Background Early in the pandemic, we designed a SARS-CoV-2 peptide vaccine containing epitope regions optimized for concurrent B cell, CD4+ T cell, and CD8+ T cell stimulation. The rationale for this design was to drive both humoral and cellular immunity with high specificity while avoiding undesired effects such as antibody-dependent enhancement (ADE). Methods We explored the set of computationally predicted SARS-CoV-2 HLA-I and HLA-II ligands, examining protein source, concurrent human/murine coverage, and population coverage. Beyond MHC affinity, T cell vaccine candidates were further refined by predicted immunogenicity, sequence conservation, source protein abundance, and coverage of high frequency HLA alleles. B cell epitope regions were chosen from linear epitope mapping studies of convalescent patient serum, followed by filtering for surface accessibility, sequence conservation, spatial localization near functional domains of the spike glycoprotein, and avoidance of glycosylation sites. Results From 58 initial candidates, three B cell epitope regions were identified. From 3730 (MHC-I) and 5045 (MHC-II) candidate ligands, 292 CD8+ and 284 CD4+ T cell epitopes were identified. By combining these B cell and T cell analyses, as well as a manufacturability heuristic, we proposed a set of 22 SARS-CoV-2 vaccine peptides for use in subsequent murine studies. We curated a dataset of ~ 1000 observed T cell epitopes from convalescent COVID-19 patients across eight studies, showing 8/15 recurrent epitope regions to overlap with at least one of our candidate peptides. Of the 22 candidate vaccine peptides, 16 (n = 10 T cell epitope optimized; n = 6 B cell epitope optimized) were manually selected to decrease their degree of sequence overlap and then synthesized. The immunogenicity of the synthesized vaccine peptides was validated using ELISpot and ELISA following murine vaccination. Strong T cell responses were observed in 7/10 T cell epitope optimized peptides following vaccination. Humoral responses were deficient, likely due to the unrestricted conformational space inhabited by linear vaccine peptides. Conclusions Overall, we find our selection process and vaccine formulation to be appropriate for identifying T cell epitopes and eliciting T cell responses against those epitopes. Further studies are needed to optimize prediction and induction of B cell responses, as well as study the protective capacity of predicted T and B cell epitopes.

There is an urgent need for a vaccine with efficacy against SARS-CoV-2. We hypothesize that peptide vaccines containing epitope regions optimized for concurrent B cell, CD4 + T cell, and CD8 + T cell stimulation would drive both humoral and cellular immunity with high specificity, potentially avoiding undesired effects such as antibody-dependent enhancement (ADE). Additionally, such vaccines can be rapidly manufactured in a distributed manner. In this study, we combine computational prediction of T cell epitopes, recently published B cell epitope mapping studies, and epitope accessibility to select candidate peptide vaccines for SARS-CoV-2. We begin with an exploration of the space of possible T cell epitopes in SARS-CoV-2 with interrogation of predicted HLA-I and HLA-II ligands, overlap between predicted ligands, protein source, as well as concurrent human/murine coverage. Beyond MHC affinity, T cell vaccine candidates were further refined by predicted immunogenicity, viral source protein abundance, sequence conservation, coverage of high frequency HLA alleles and co-localization of CD4 + and CD8 + T cell epitopes. B cell epitope regions were chosen from linear epitope mapping studies of convalescent patient serum, followed by filtering to select regions with surface accessibility, high sequence conservation, spatial localization near functional domains of the spike glycoprotein, and avoidance of glycosylation sites. From 58 initial candidates, three B cell epitope regions were identified. By combining these B cell and T cell analyses, as well as a manufacturability heuristic, we propose a set of SARS-CoV-2 vaccine peptides for use in subsequent murine studies and clinical trials. Figure 2: Landscape of SARS-CoV-2 MHC ligands. (A&B) Selection criteria for (A) HLA-I and (B) shows predicted (x-axis) versus IEDB (y-axis) binding affinity, with horizontal line representing 500nM IEDB binding affinity and vertical line representing corresponding predicted binding affinity for 90% specificity in binding prediction. Histogram (top) shows all predicted SARS-CoV-2 HLA ligand candidates. (C) Landscape of predicted HLA ligands, showing nested HLA ligands comprising HLA-I and -II ligands with complete overlap (top), and LOESS fitted curve (span = 0.1) for HLA-I/II ligands by location along the . Red track represents SARS epitopes identified in literature review with sequence identity in SARS-CoV-2. Predicted HLA ligands with conserved sequences to this literature set are represented in the lollipop plot with a red stick. (D) Summary of total number of predicted HLA-I/II ligands and nested HLA ligands. (E) Summary of nested HLA ligand coverage by protein, with raw counts (left) or counts normalized by protein length (right). (F) Summary of murine/human MHC ligand overlap. (G) Distribution of population frequencies among predicted HLA-I, -II, and nested HLA ligands.

Background With the increased volume of genomics data from studies involving treatment with immune checkpoint inhibition (ICI) and other immunotherapies, researchers remain unable to to make full use of results due to lack of comprehensive access to data or th ability to compare outcomes across datasets.The Cancer Research Institute (CRI) iAtlas 1 (www.cri-iatlas.org) is a comprehensive web platform for interactive data exploration and discovery in immuno-oncology, originating in a study by The Cancer Genome Atlas (TCGA). 1-3 iAtlas provides topic-oriented analysis modules, each generating visualizations and statistics for studying interactions between tumors and the immune microenvironment (figure 1). Methods Immunogenomic features from 15 ICI trials encompassing 1,142 samples were processed with a standardized bioinformatics workflow 4 and incorporated into iAtlas, augmenting the 11,535 patient samples from TCGA 1-3 and the Pan-Cancer Analysis of Whole Genomes 5 consortia. A compendium of in-development immunotherapy drug targets 6 and results of a study of germline genetic contribution to immune response in cancer 7 were included. For efficient access, all data were incorporated into a relational database, and programmatic access was made available through an application programming interface (API) (figure 2). The set of available iAtlas modules was vastly extended, and numerous improvements were made to the codebase. Users can now define sample cohorts and sample groups based on any available categorical or numerical variable.Results iAtlas provides 17 interactive analysis modules (table 1) to explore immune-cancer interactions, immunotherapy treatment, and outcomes in 12,677 patient samples. Six modules are dedicated to ICI studies: dataset overview, immune readouts, immunomodulators, clinical outcome, regression analysis, and a machine learning module to enable identification of factors associated with response to therapy (figure 3). We added modules to explore how germline variation and copy number alterations relate to immune response, and how receptor-ligand interactions mediate interactions among tumor and immune cells (figure 4). Docker images using Common Workflow Language descriptors are provided so that researchers can run iAtlas workflows on their own data. Computational notebooks are provided to illustrate and explain iAtlas code, plots, and functionality and to facilitate integration of iAtlas data with data sourced from a researcher's own study.Conclusions iAtlas serves as a repository and resource for harmonized data on immune response in cancer and response to immunotherapy. iAtlas enables researchers to readily test hypotheses and access data through multiple modalities: an interactive web portal, data download, tools, 8 and computational workflows and notebooks. Abstract 927 Figure 1 CRI iAtlas Explorer Entry into exploration of immune response in cancer with iAtlas. Researchers start by defining cohorts and sample groups, and can then explore and visualize results using any of 17 analysis m...

In Drosophila melanogaster males, the expression of X-linked genes is regulated by mechanisms that operate on a chromosomal scale. One such mechanism, male-specific lethal complex-dependent X-linked dosage compensation, is thought to broadly enhance the expression of male X-linked genes through two-fold transcriptional upregulation. The evolutionary consequences of this form of dosage compensation are not well understood, particularly with regard to genes more highly expressed in males. It has been observed the X chromosome arrangement of these male-biased genes is non-random, consistent with what one might expect if there is a selective advantage for male-biased genes to avoid dosage compensation. Separately, it has been noted that the male-specific lethal complex and its dosage compensation mechanism appear absent in some male tissues, thus providing a control for the selection hypothesis. Here we utilized publicly available datasets to reassess the arrangement of X-linked male-biased expressed genes after accounting for expression in tissues not dosage compensated by the male-specific lethal complex. Our results do not corroborate previous observations supporting organismal-wide detrimental effects by dosage compensation on X-linked male-biased expressed genes. We instead find no evidence that dosage compensation has played a role in the arrangement of dosage compensated male-biased genes on the X chromosome.