BeWith: A Between-Within Method to Discover Relationships between Cancer Modules via Integrated Analysis of Mutual Exclusivity, Co-occurrence and Functional Interactions

The analysis of the mutational landscape of cancer, including mutual exclusivity and co-occurrence of mutations, has been instrumental in studying the disease. We hypothesized that exploring the interplay between co-occurrence, mutual exclusivity, and functional interactions between genes will further improve our understanding of the disease and help to uncover new relations between cancer driving genes and pathways. To this end, we designed a general framework, BeWith, for identifying modules with different combinations of mutation and interaction patterns. We focused on three different settings of the BeWith schema: (i) BeME-WithFun in which the relations between modules are enriched with mutual exclusivity while genes within each module are functionally related; (ii) BeME-WithCo which combines mutual exclusivity between modules with co-occurrence within modules; and (iii) BeCo-WithMEFun which ensures co-occurrence between modules while the within module relations combine mutual exclusivity and functional interactions. We formulated the BeWith framework using Integer Linear Programming (ILP), enabling us to find optimally scoring sets of modules. Our results demonstrate the utility of BeWith in providing novel information about mutational patterns, driver genes, and pathways. In particular, BeME-WithFun helped identify functionally coherent modules that might be relevant for cancer progression. In addition to finding previously well-known drivers, the identified modules pointed to the importance of the interaction between NCOR and NCOA3 in breast cancer. Additionally, an application of the BeME-WithCo setting revealed that gene groups differ with respect to their vulnerability to different mutagenic processes, and helped us to uncover pairs of genes with potentially synergetic effects, including a potential synergy between mutations in TP53 and metastasis related DCC gene

A Highly Stable Blood Meal Alternative for Rearing \u3cem\u3eAedes\u3c/em\u3e and \u3cem\u3eAnopheles\u3c/em\u3e Mosquitoes

We investigated alternatives to whole blood for blood feeding of mosquitoes with a focus on improved stability and compatibility with mass rearing programs. In contrast to whole blood, an artificial blood diet of ATP-supplemented plasma was effective in maintaining mosquito populations and was compatible with storage for extended periods refrigerated, frozen, and as a lyophilized powder. The plasma ATP diet supported rearing of both Anopheles and Aedes mosquitoes. It was also effective in rearing Wolbachia-infected Aedes mosquitoes, suggesting compatibility with vector control efforts

Estimating female malaria mosquito age by quantifying Y-linked genes in stored male spermatozoa

Vector control strategies are among the most effective measures to combat mosquito-borne diseases, such as malaria. These strategies work by altering the mosquito age structure through increased mortality of the older female mosquitoes that transmit pathogens. However, methods to monitor changes to mosquito age structure are currently inadequate for programmatic implementation. Female mosquitoes generally mate a single time soon after emergence and draw down spermatozoa reserves with each oviposition cycle. Here, we demonstrate that measuring spermatozoa quantity in female Anopheles mosquitoes is an effective approach to assess mosquito age. Using multiplexed qPCR targeted at male spermatozoa, we show that Y-linked genes in female mosquitoes are exclusively found in the spermatheca, the organ that houses spermatozoa, and the quantity of these gene sequences significantly declines with age. The method can accurately identify mosquitoes more than 10 days old and thus old enough to potentially transmit pathogens harbored in the salivary glands during blood feeding. Furthermore, mosquito populations that differ by 10% in daily survivorship have a high likelihood of being distinguished using modest sample sizes, making this approach scalable for assessing the efficacy of vector intervention control programs

Targeting melanoma growth and viability reveals dualistic functionality of the phosphonothionate analogue of carba cyclic phosphatidic acid

<p>Abstract</p> <p>Background</p> <p>Although the incidence of melanoma in the U.S. is rising faster than any other cancer, the FDA-approved chemotherapies lack efficacy for advanced disease, which results in poor overall survival. Lysophosphatidic acid (LPA), autotaxin (ATX), the enzyme that produces LPA, and the LPA receptors represent an emerging group of therapeutic targets in cancer, although it is not known which of these is most effective.</p> <p>Results</p> <p>Herein we demonstrate that thio-ccPA 18:1, a stabilized phosphonothionate analogue of carba cyclic phosphatidic acid, ATX inhibitor and LPA1/3 receptor antagonist, induced a marked reduction in the viability of B16F10 metastatic melanoma cells compared with PBS-treated control by 80-100%. Exogenous LPA 18:1 or D-sn-1-O-oleoyl-2-O-methylglyceryl-3-phosphothioate did not reverse the effect of thio-ccPA 18:1. The reduction in viability mediated by thio-ccPA 18:1 was also observed in A375 and MeWo melanoma cell lines, suggesting that the effects are generalizable. Interestingly, siRNA to LPA3 (siLPA3) but not other LPA receptors recapitulated the effects of thio-ccPA 18:1 on viability, suggesting that inhibition of the LPA3 receptor is an important dualistic function of the compound. In addition, siLPA3 reduced proliferation, plasma membrane integrity and altered morphology of A375 cells. Another experimental compound designed to antagonize the LPA1/3 receptors significantly reduced viability in MeWo cells, which predominantly express the LPA3 receptor.</p> <p>Conclusions</p> <p>Thus the ability of thio-ccPA 18:1 to inhibit the LPA3 receptor and ATX are key to its molecular mechanism, particularly in melanoma cells that predominantly express the LPA3 receptor. These observations necessitate further exploration and exploitation of these targets in melanoma.</p

The Monarch Initiative in 2024: an analytic platform integrating phenotypes, genes and diseases across species.

Bridging the gap between genetic variations, environmental determinants, and phenotypic outcomes is critical for supporting clinical diagnosis and understanding mechanisms of diseases. It requires integrating open data at a global scale. The Monarch Initiative advances these goals by developing open ontologies, semantic data models, and knowledge graphs for translational research. The Monarch App is an integrated platform combining data about genes, phenotypes, and diseases across species. Monarch\u27s APIs enable access to carefully curated datasets and advanced analysis tools that support the understanding and diagnosis of disease for diverse applications such as variant prioritization, deep phenotyping, and patient profile-matching. We have migrated our system into a scalable, cloud-based infrastructure; simplified Monarch\u27s data ingestion and knowledge graph integration systems; enhanced data mapping and integration standards; and developed a new user interface with novel search and graph navigation features. Furthermore, we advanced Monarch\u27s analytic tools by developing a customized plugin for OpenAI\u27s ChatGPT to increase the reliability of its responses about phenotypic data, allowing us to interrogate the knowledge in the Monarch graph using state-of-the-art Large Language Models. The resources of the Monarch Initiative can be found at monarchinitiative.org and its corresponding code repository at github.com/monarch-initiative/monarch-app