SUMMARY The genetics of complex disease produce alterations in the molecular interactions of cellular pathways whose collective effect may become clear through the organized structure of molecular networks. To characterize molecular systems associated with late-onset Alzheimer’s disease (LOAD), we constructed gene regulatory networks in 1647 post-mortem brain tissues from LOAD patients and non-demented subjects, and demonstrate that LOAD reconfigures specific portions of the molecular interaction structure. Through an integrative network-based approach, we rank-ordered these network structures for relevance to LOAD pathology, highlighting an immune and microglia-specific module dominated by genes involved in pathogen phagocytosis, containing TYROBP as a key regulator and up-regulated in LOAD. Mouse microglia cells over-expressing intact or truncated TYROBP revealed expression changes that significantly overlapped the human brain TYROBP network. Thus the causal network structure is a useful predictor of response to gene perturbations and presents a novel framework to test models of disease mechanisms underlying LOAD.

High-dimensional data sets generated by high-throughput technologies, such as DNA microarray, are often the outputs of complex networked systems driven by hidden regulatory signals. Traditional statistical methods for computing low-dimensional or hidden representations of these data sets, such as principal component analysis and independent component analysis, ignore the underlying network structures and provide decompositions based purely on a priori statistical constraints on the computed component signals. The resulting decomposition thus provides a phenomenological model for the observed data and does not necessarily contain physically or biologically meaningful signals. Here, we develop a method, called network component analysis, for uncovering hidden regulatory signals from outputs of networked systems, when only a partial knowledge of the underlying network topology is available. The a priori network structure information is first tested for compliance with a set of identifiability criteria. For networks that satisfy the criteria, the signals from the regulatory nodes and their strengths of influence on each output node can be faithfully reconstructed. This method is first validated experimentally by using the absorbance spectra of a network of various hemoglobin species. The method is then applied to microarray data generated from yeast Saccharamyces cerevisiae and the activities of various transcription factors during cell cycle are reconstructed by using recently discovered connectivity information for the underlying transcriptional regulatory networks.H igh-throughput techniques in biology, such as DNA microarray (1), have generated a large amount of data that can potentially provide systems-level information regarding the underlying dynamics and mechanisms. These high-dimensional output data are typically the end products of low-dimensional regulatory signals driven through an interacting network. As illustrated in Fig. 1, the relationship between the lower dimensional regulatory signals (or states) and output data can be modeled by a bipartite networked system, where the output signals (e.g., gene expression levels) are generated by weighted functions of the intracellular states (e.g., the activity of the transcription factors). A major challenge in systems biology is to derive methodologies for simultaneous reconstructions of the hidden dynamics of the regulatory signals.In recent years, statistical techniques for determining lowdimensional representations of high-dimensional data sets, e.g., principal component analysis (PCA) (2) or singular value decomposition (3-5) and independent component analysis (ICA) (6), have been applied successfully to deduce biologically significant information from high-throughput data sets. It is important to recognize that such dimensionality reduction techniques are not designed to address the hidden dynamics reconstruction problem addressed in this article. For example, PCA and ICA both would generate linear networks for interpreting the observed data set, where t...

SUMMARY Alteration of the PTEN/PI3K pathway is associated with late stage and castrate resistant prostate cancer (CRPC). However, how PTEN loss involves in CRPC development is not clear. Here we show that castration-resistant growth is an intrinsic property of Pten-null prostate cancer (CaP) cells, independent of cancer development stage. PTEN loss suppresses androgen-responsive gene expressions by modulating androgen receptor (AR) transcription factor activity. Conditional deletion of Ar in the epithelium promotes the proliferation of Pten-null cancer cells, at least in part, by down-regulating androgen-responsive gene Fkbp5 and preventing PHLPP-mediated AKT inhibition. Our findings identify PI3K and AR pathway crosstalk as a mechanism of CRPC development, with potentially important implications for CaP etiology and therapy.

PTEN loss or PI3K/AKT signaling pathway activation correlates with human prostate cancer progression and metastasis. However, in preclinical murine models, deletion of Pten alone fails to mimic the significant metastatic burden that frequently accompanies the end stage of human disease. To identify additional pathway alterations that cooperate with PTEN loss in prostate cancer progression, we surveyed human prostate cancer tissue microarrays and found that the RAS/MAPK pathway is significantly elevated both in primary and metastatic lesions. In an attempt to model this event, we crossed conditional activatable K-rasG12D/WT mice with the prostate conditional Pten deletion model. Although RAS activation alone cannot initiate prostate cancer development, it significantly accelerated progression caused by PTEN loss, accompanied by epithelial-to-mesenchymal transition (EMT) and macrometastasis with 100% penetrance. A novel stem/progenitor subpopulation with mesenchymal characteristics was isolated from the compound mutant prostates, which was highly metastatic upon orthotopic transplantation. Importantly, inhibition of RAS/MAPK signaling by PD325901, a MEK inhibitor, significantly reduced the metastatic progression initiated from transplanted stem/progenitor cells. Collectively, our findings indicate that activation of RAS/MAPK signaling serves as a potentiating second hit to alteration of the PTEN/PI3K/AKT axis and co-targeting both pathways is highly effective gin preventing the development of metastatic prostate cancers.

Organoids derived from human pluripotent stem cells are a potentially powerful tool for high-throughput screening (HTS), but the complexity of organoid cultures poses a significant challenge for miniaturization and automation. Here, we present a fully automated, HTS-compatible platform for enhanced differentiation and phenotyping of human kidney organoids. The entire 21-day protocol, from plating to differentiation to analysis, can be performed automatically by liquid-handling robots, or alternatively by manual pipetting. High-content imaging analysis reveals both dose-dependent and threshold effects during organoid differentiation. Immunofluorescence and single-cell RNA sequencing identify previously undetected parietal, interstitial, and partially differentiated compartments within organoids and define conditions that greatly expand the vascular endothelium. Chemical modulation of toxicity and disease phenotypes can be quantified for safety and efficacy prediction. Screening in gene-edited organoids in this system reveals an unexpected role for myosin in polycystic kidney disease. Organoids in HTS formats thus establish an attractive platform for multidimensional phenotypic screening.

Crucial transitions in cancer-including tumor initiation, local expansion, metastasis, and therapeutic resistance-involve complex interactions between cells within the dynamic tumor ecosystem. Transformative single-cell genomics technologies and spatial multiplex in situ methods now provide an opportunity to interrogate this complexity at unprecedented resolution. The Human Tumor Atlas Network (HTAN), part of the National Cancer Institute (NCI) Cancer Moonshot Initiative, will establish a clinical, experimental, computational, and organizational framework to generate informative and accessible three-dimensional atlases of cancer transitions for a diverse set of tumor types. This effort complements both ongoing efforts to map healthy organs and previous largescale cancer genomics approaches focused on bulk sequencing at a single point in time. Generating single-cell, multiparametric, longitudinal atlases and integrating them with clinical outcomes should help identify novel predictive biomarkers and features as well as therapeutically relevant cell types, cell states, and cellular interactions across transitions. The resulting tumor atlases should have a profound impact on our understanding of cancer biology and have the potential to improve cancer detection, prevention, and therapeutic discovery for better precision-medicine treatments of cancer patients and those at risk for cancer.Cancer forms and progresses through a series of critical transitions-from pre-malignant to malignant states, from locally contained to metastatic disease, and from treatment-responsive to treatment-resistant tumors (Figure 1). Although specifics differ across tumor types and patients, all transitions involve complex dynamic interactions between diverse pre-malignant, malignant, and non-malignant cells (e.g., stroma cells and immune cells), often organized in specific patterns within the tumor

Polycystic kidney disease (PKD) is a life-threatening disorder, commonly caused by defects in polycystin-1 (PC1) or polycystin-2 (PC2), in which tubular epithelia form fluid-filled cysts 1, 2. A major barrier to understanding PKD is the absence of human cellular models that accurately and efficiently recapitulate cystogenesis 3, 4. Previously, we have generated a genetic model of PKD using human pluripotent stem cells and derived kidney organoids 5, 6. Here we show that systematic substitution of physical components can dramatically increase or decrease cyst formation, unveiling a critical role for microenvironment in PKD. Removal of adherent cues increases cystogenesis 10-fold, producing cysts phenotypically resembling PKD that expand massively to 1-centimeter diameters. Removal of stroma enables outgrowth of PKD cell lines, which exhibit defects in PC1 expression and collagen compaction. Cyclic AMP, when added, induces cysts in both PKD organoids and controls. These biomaterials establish a highly efficient model of PKD cystogenesis that directly implicates the microenvironment at the earliest stages of the disease.

Complete modeling of metabolic networks is desirable, but it is difficult to accomplish because of the lack of kinetics. As a step toward this goal, we have developed an approach to build an ensemble of dynamic models that reach the same steady state. The models in the ensemble are based on the same mechanistic framework at the elementary reaction level, including known regulations, and span the space of all kinetics allowable by thermodynamics. This ensemble allows for the examination of possible phenotypes of the network upon perturbations, such as changes in enzyme expression levels. The size of the ensemble is reduced by acquiring data for such perturbation phenotypes. If the mechanistic framework is approximately accurate, the ensemble converges to a smaller set of models and becomes more predictive. This approach bypasses the need for detailed characterization of kinetic parameters and arrives at a set of models that describes relevant phenotypes upon enzyme perturbations.