Expression of Distinct Self-Incompatibility Specificities in Arabidopsis thaliana

The interplay of balancing selection within a species and rapid gene evolution between species can confound our ability to determine the functional equivalence of interspecific and intergeneric pairs of alleles underlying reproduction. In crucifer plants, mating specificity in the barrier to self-fertilization called self-incompatibility (SI) is controlled by allele-specific interactions between two highly polymorphic and co-evolving proteins, the S-locus receptor kinase (SRK) and its S-locus cysteine rich (SCR) ligand. These proteins have diversified both within and between species such that it is often difficult to determine from sequence information alone if they encode the same or different SI specificity. The self-fertile Arabidopsis thaliana was derived from an obligate outbreeding ancestor by loss of self-incompatibility, often in conjunction with inactivation of SRK or SCR. Nevertheless, some accessions of A. thaliana can express self-incompatibility upon transformation with an SRK–SCR gene pair isolated from its self-incompatible close relative A. lyrata. Here we show that several additional and highly diverged SRK/SCR genes from A. lyrata and another crucifer plant, Capsella grandiflora, confer self-incompatibility in A. thaliana, either as intact genes isolated from genomic libraries or after manipulation to generate chimeric fusions. We describe how the use of this newly developed chimeric protein strategy has allowed us to test the functional equivalence of SRK/SCR gene pairs from different taxa and to assay the functionality of endogenous A. thaliana SRK and SCR sequences

MACSima imaging cyclic staining (MICS) technology reveals combinatorial target pairs for CAR T cell treatment of solid tumors

Many critical advances in research utilize techniques that combine high-resolution with high-content characterization at the single cell level. We introduce the MICS (MACSima Imaging Cyclic Staining) technology, which enables the immunofluorescent imaging of hundreds of protein targets across a single specimen at subcellular resolution. MICS is based on cycles of staining, imaging, and erasure, using photobleaching of fluorescent labels of recombinant antibodies (REAfinity Antibodies), or release of antibodies (REAlease Antibodies) or their labels (REAdye_lease Antibodies). Multimarker analysis can identify potential targets for immune therapy against solid tumors. With MICS we analysed human glioblastoma, ovarian and pancreatic carcinoma, and 16 healthy tissues, identifying the pair EPCAM/THY1 as a potential target for chimeric antigen receptor (CAR) T cell therapy for ovarian carcinoma. Using an Adapter CAR T cell approach, we show selective killing of cells only if both markers are expressed. MICS represents a new high-content microscopy methodology widely applicable for personalized medicine

The Caenorhabditis elegans snf-11 Gene Encodes a Sodium-dependent GABA Transporter Required for Clearance of Synaptic GABA

Sodium-dependent neurotransmitter transporters participate in the clearance and/or recycling of neurotransmitters from synaptic clefts. The snf-11 gene in Caenorhabditis elegans encodes a protein of high similarity to mammalian GABA transporters (GATs). We show here that snf-11 encodes a functional GABA transporter; SNF-11–mediated GABA transport is Na(+) and Cl(−) dependent, has an EC(50) value of 168 μM, and is blocked by the GAT1 inhibitor SKF89976A. The SNF-11 protein is expressed in seven GABAergic neurons, several additional neurons in the head and retrovesicular ganglion, and three groups of muscle cells. Therefore, all GABAergic synapses are associated with either presynaptic or postsynaptic (or both) expression of SNF-11. Although a snf-11 null mutation has no obvious effects on GABAergic behaviors, it leads to resistance to inhibitors of acetylcholinesterase. In vivo, a snf-11 null mutation blocks GABA uptake in at least a subset of GABAergic cells; in a cell culture system, all GABA uptake is abolished by the snf-11 mutation. We conclude that GABA transport activity is not essential for normal GABAergic function in C. elegans and that the localization of SNF-11 is consistent with a GABA clearance function rather than recycling

A Community Challenge for Inferring Genetic Predictors of Gene Essentialities through Analysis of a Functional Screen of Cancer Cell Lines

We report the results of a DREAM challenge designed to predict relative genetic essentialities based on a novel dataset testing 98,000 shRNAs against 149 molecularly characterized cancer cell lines. We analyzed the results of over 3,000 submissions over a period of 4 months. We found that algorithms combining essentiality data across multiple genes demonstrated increased accuracy; gene expression was the most informative molecular data type; the identity of the gene being predicted was far more important than the modeling strategy; well-predicted genes and selected molecular features showed enrichment in functional categories; and frequently selected expression features correlated with survival in primary tumors. This study establishes benchmarks for gene essentiality prediction, presents a community resource for future comparison with this benchmark, and provides insights into factors influencing the ability to predict gene essentiality from functional genetic screens. This study also demonstrates the value of releasing pre-publication data publicly to engage the community in an open research collaboration. Gönen et al. report the results of an open-participation DREAM challenge to critically assess the ability to predict gene essentiality on a novel functional screening dataset of 149 cancer cell lines. This study establishes benchmarks for gene essentiality prediction, presents a community resource for future comparison with this benchmark, and provides insights into factors influencing the ability to predict gene essentiality from functional genetic screens

Crowdsourced estimation of cognitive decline and resilience in Alzheimer's disease.

Identifying accurate biomarkers of cognitive decline is essential for advancing early diagnosis and prevention therapies in Alzheimer's disease. The Alzheimer's disease DREAM Challenge was designed as a computational crowdsourced project to benchmark the current state-of-the-art in predicting cognitive outcomes in Alzheimer's disease based on high dimensional, publicly available genetic and structural imaging data. This meta-analysis failed to identify a meaningful predictor developed from either data modality, suggesting that alternate approaches should be considered for prediction of cognitive performance