April Kim

BACKGROUND: Integrating rare variation from trio family and case-control studies has successfully implicated specific genes contributing to risk of neurodevelopmental disorders (NDDs) including autism spectrum disorders (ASD), intellectual disability (ID), developmental disorders (DDs), and epilepsy (EPI). For schizophrenia (SCZ), however, while sets of genes have been implicated through the study of rare variation, only two risk genes have been identified. METHODS: We used hierarchical Bayesian modeling of rare-variant genetic architecture to estimate mean effect sizes and risk-gene proportions, analyzing the largest available collection of whole exome sequence data for SCZ (1,077 trios, 6,699 cases, and 13,028 controls), and data for four NDDs (ASD, ID, DD, and EPI; total 10,792 trios, and 4,058 cases and controls). RESULTS: For SCZ, we estimate there are 1,551 risk genes. There are more risk genes and they have weaker effects than for NDDs. We provide power analyses to predict the number of risk-gene discoveries as more data become available. We confirm and augment prior risk gene and gene set enrichment results for SCZ and NDDs. In particular, we detected 98 new DD risk genes at FDR < 0.05. Correlations of risk-gene posterior probabilities are high across four NDDs (ρ>0.55), but low between SCZ and the NDDs (ρ<0.3). An in-depth analysis of 288 NDD genes shows there is highly significant protein-protein interaction (PPI) network connectivity, and functionally distinct PPI subnetworks based on pathway enrichment, single-cell RNA-seq cell types, and multi-region developmental brain RNA-seq. CONCLUSIONS: We have extended a pipeline used in ASD studies and applied it to infer rare genetic parameters for SCZ and four NDDs ( https://github.com/hoangtn/extTADA ). We find many new DD risk genes, supported by gene set enrichment and PPI network connectivity analyses. We find greater similarity among NDDs than between NDDs and SCZ. NDD gene subnetworks are implicated in postnatally expressed presynaptic and postsynaptic genes, and for transcriptional and post-transcriptional gene regulation in prenatal neural progenitor and stem cells.

Functional genomics networks are widely used to identify unexpected pathway relationships in large genomic datasets. However, it is challenging to compare the signal-to-noise ratios of different networks and to identify the optimal network with which to interpret a particular genetic dataset. We present GeNets, a platform in which users can train a machine-learning model (Quack) to carry out these comparisons and execute, store, and share analyses of genetic and RNA-sequencing datasets. The GeNets web platform can identify the most informative network, as well as execute, store and share network-based analyses of RNA-seq or genomic datasets.

Combining genetic and cell-type-specific proteomic datasets can generate biological insights and therapeutic hypotheses, but a technical and statistical framework for such analyses is lacking. Here, we present an open-source computational tool called Genoppi (lagelab.org/genoppi) that enables robust, standardized, and intuitive integration of quantitative proteomic results with genetic data. We use Genoppi to analyze 16 cell-type-specific protein interaction datasets of four proteins (BCL2, TDP-43, MDM2, PTEN) involved in cancer and neurological disease. Through systematic quality control of the data and integration with published protein interactions, we show a general pattern of both cell-type-independent and cell-type-specific interactions across three cancer cell types and one human iPSC-derived neuronal cell type. Furthermore, through the integration of proteomic and genetic datasets in Genoppi, our results suggest that the neuron-specific interactions of these proteins are mediating their genetic involvement in neurodegenerative diseases. Importantly, our analyses suggest that human iPSC-derived neurons are a relevant model system for studying the involvement of BCL2 and TDP-43 in amyotrophic lateral sclerosis.

Immune-cell-derived membranes have garnered significant attention as innovative delivery modalities in cancer immunotherapy for their intrinsic immune-modulating functionalities and superior biocompatibilities. Integrating additional parental cell membranes or synthetic lipid vesicles into cellular vesicles can further potentiate their capacities to perform combinatorial pharmacological activities in activating antitumor immunity, thus providing insights into the potential of hybrid cellular vesicles as versatile delivery vehicles for cancer immunotherapy. Here, we have developed a macrophage-membrane-derived hybrid vesicle that has the dual functions of transporting immunotherapeutic drugs and shaping the polarization of tumor-associated macrophages for cancer immunotherapy. The platform combines M1 macrophage-membrane-derived vesicles with CXCR4-binding-peptide-conjugated liposomes loaded with manganese and doxorubicin. The hybrid nanovesicles exhibited remarkable macrophage-targeting capacity through the CXCR4-binding peptide, resulting in enhanced macrophage polarization to the antitumoral M1 phenotype characterized by proinflammatory cytokine release. The manganese/doxorubicin-loaded hybrid vesicles in the CXCR4-expressing tumor cells evoked potent cancer cytotoxicity, immunogenic cell death of tumor cells, and STING activation. Moreover, cotreatment with manganese and doxorubicin promoted dendritic cell maturation, enabling effective tumor growth inhibition. In murine models of CT26 colon carcinoma and 4T1 breast cancer, intravenous administration of the manganese/doxorubicin-loaded hybrid vesicles elicited robust tumor-suppressing activity at a low dosage without adverse systemic effects. Local administration of hybrid nanovesicles also induced an abscessive effect in a bilateral 4T1 tumor model. This study demonstrates a promising biomimetic manganese/doxorubicin-based hybrid nanovesicle platform for effective cancer immunotherapy tailored to the tumor microenvironment, which may offer an innovative approach to combinatorial immunotherapy.