The regulation of energy homeostasis integrates diverse biological processes ranging from behavior to metabolism and is linked fundamentally to numerous disease states. To identify new molecules that can bypass homeostatic compensatory mechanisms of energy balance in intact animals, we screened for small-molecule modulators of Caenorhabditis elegans fat content. We report on several molecules that modulate fat storage without obvious deleterious effects on feeding, growth and reproduction. A subset of these compounds also altered fat storage in mammalian and insect cell culture. We found that one of the newly identified compounds exerts its effects in C. elegans through a pathway that requires previously undescribed functions of an AMP-activated kinase catalytic subunit and a transcription factor previously unassociated with fat regulation. Thus, our strategy identifies small molecules that are effective within the context of intact animals and reveals relationships between new pathways that operate across phyla to influence energy homeostasis.

Metabolic syndrome is a cluster of risk factors, such as obesity, insulin resistance, and hyperlipidemia that increases the individual's likelihood of developing cardiovascular diseases. Patients inflicted with metabolic disorders also suffer from tissue repair defect. Mitsugumin 53 (MG53) is a protein essential to cellular membrane repair. It facilitates the nucleation of intracellular vesicles to sites of membrane disruption to create repair patches, contributing to the regenerative capacity of skeletal and cardiac muscle tissues upon injury. Since individuals suffering from metabolic syndrome possess tissue regeneration deficiency and MG53 plays a crucial role in restoring membrane integrity, we studied MG53 activity in mice models exhibiting metabolic disorders induced by a 6 month high-fat diet (HFD) feeding. Western blotting showed that MG53 expression is not altered within the skeletal and cardiac muscles of mice with metabolic syndrome. Rather, we found that MG53 levels in blood circulation were actually reduced. This data directly contradicts findings presented by Song et. al that indict MG53 as a causative factor for metabolic syndrome (Nature 494, 375-379). The diminished MG53 serum level observed may contribute to the inadequate tissue repair aptitude exhibited by diabetic patients. Furthermore, immunohistochemical analyses reveal that skeletal muscle fibers of mice with metabolic disorders experience localization of subcellular MG53 around mitochondria. This clustering may represent an adaptive response to oxidative stress resulting from HFD feeding and may implicate MG53 as a guardian to protect damaged mitochondria. Therapeutic approaches that elevate MG53 expression in serum circulation may be a novel method to treat the degenerative tissue repair function of diabetic patients.

The goal of the current study is to examine the biological effects of epithelial-specific tumor suppressor maspin on tumor host immune response. Accumulated evidence demonstrates an anti-tumor effect of maspin on tumor growth, invasion and metastasis. The molecular mechanism underlying these biological functions of maspin is thought to be through histone deacetylase inhibition, key to the maintenance of differentiated epithelial phenotype. Since tumor-driven stromal reactivities co-evolve in tumor progression and metastasis, it is not surprising that maspin expression in tumor cells inhibits extracellular matrix degradation, increases fibrosis and blocks hypoxia-induced angiogenesis. Using the athymic nude mouse model capable of supporting the growth and progression of xenogeneic human prostate cancer cells, we further demonstrate that maspin expression in tumor cells elicits neutrophil- and B cells-dependent host tumor immunogenicity. Specifically, mice bearing maspin-expressing tumors exhibited increased systemic and intratumoral neutrophil maturation, activation and antibody-dependent cytotoxicity, and decreased peritumoral lymphangiogenesis. These results reveal a novel biological function of maspin in directing host immunity towards tumor elimination that helps explain the significant reduction of xenograft tumor incidence in vivo and the clinical correlation of maspin with better prognosis of several types of cancer. Taken together, our data raised the possibility for novel maspin-based cancer immunotherapies.

There has been much effort to prioritize genomic variants with respect to their impact on "function". However, function is often not precisely defined: sometimes it is the disease association of a variant; on other occasions, it reflects a molecular effect on transcription or epigenetics. Here, we coupled multiple genomic predictors to build GRAM, a GeneRAlized Model, to predict a well-defined experimental target: the expression-modulating effect of a non-coding variant on its associated gene, in a transferable, cell-specific manner. Firstly, we performed feature engineering: using LASSO, a regularized linear model, we found transcription factor (TF) binding most predictive, especially for TFs that are hubs in the regulatory network; in contrast, evolutionary conservation, a popular feature in many other variant-impact predictors, has almost no contribution. Moreover, TF binding inferred from in vitro SELEX is as effective as that from in vivo ChIP-Seq. Second, we implemented GRAM integrating only SELEX features and expression profiles; thus, the program combines a universal regulatory score with an easily obtainable modifier reflecting the particular cell type. We benchmarked GRAM on large-scale MPRA datasets, achieving AUROC scores of 0.72 in GM12878 and 0.66 in a multi-cell line dataset. We then evaluated the performance of GRAM on targeted regions using luciferase assays in the MCF7 and K562 cell lines. We noted that changing the insertion position of the construct relative to the reporter gene gave very different results, highlighting the importance of carefully defining the exact prediction target of the model. Finally, we illustrated the utility of GRAM in fine-mapping causal variants and developed a practical software pipeline to carry this out. In particular, we demonstrated in specific examples how the pipeline could pinpoint variants that directly modulate gene expression within a larger linkage-disequilibrium block associated with a phenotype of interest (e.g., for an eQTL).

Management of breast cancer in limited-resource settings is hindered by a lack of low-cost, logistically sustainable approaches toward molecular and cellular diagnostic pathology services that are needed to guide therapy. To address these limitations, we have developed a multimodal cellphone-based platform-the EpiView-D4-that can evaluate both cellular morphology and molecular expression of clinically relevant biomarkers directly from fine-needle aspiration (FNA) of breast tissue specimens within 1 h. The EpiView-D4 is comprised of two components: (1) an immunodiagnostic chip built upon a "non-fouling" polymer brush-coating (the "D4") which quantifies expression of protein biomarkers directly from crude cell lysates, and (2) a custom cellphone-based optical microscope ("EpiView") designed for imaging cytology preparations and D4 assay readout. As a proof-of-concept, we used the EpiView-D4 for assessment of human epidermal growth factor receptor-2 (HER2) expression and validated the performance using cancer cell lines, animal models, and human tissue specimens. We found that FNA cytology specimens (prepared in less than 5 min with rapid staining kits) imaged by the EpiView-D4 were adequate for assessment of lesional cellularity and tumor content. We also found our device could reliably distinguish between HER2 expression levels across multiple different cell lines and animal xenografts. In a pilot study with human tissue (n = 19), we were able to accurately categorize HER2-negative and HER2-positve tumors from FNA specimens. Taken together, the EpiView-D4 offers a promising alternative to invasive-and often unavailable-pathology services and may enable the democratization of effective breast cancer management in limited-resource settings.

Highly sensitive, specific, and point-of-care (POC) serological assays are an essential tool to manage coronavirus disease 2019 (COVID-19). Here, we report on a microfluidic POC test that can profile the antibody response against multiple severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antigens-spike S1 (S1), nucleocapsid (N), and the receptor binding domain (RBD)-simultaneously from 60 μl of blood, plasma, or serum. We assessed the levels of antibodies in plasma samples from 31 individuals (with longitudinal sampling) with severe COVID-19, 41 healthy individuals, and 18 individuals with seasonal coronavirus infections. This POC assay achieved high sensitivity and specificity, tracked seroconversion, and showed good concordance with a live virus microneutralization assay. We can also detect a prognostic biomarker of severity, IP-10 (interferon-γ-induced protein 10), on the same chip. Because our test requires minimal user intervention and is read by a handheld detector, it can be globally deployed to combat COVID-19.

The earliest measurable changes to postinjury platelet biology may be in the platelet transcriptome, as platelets are known to carry messenger ribonucleic acids (RNAs), and there is evidence in other inflammatory and infectious disease states of differential and alternative platelet RNA splicing in response to changing physiology. Thus, the aim of this exploratory pilot study was to examine the platelet transcriptome and platelet RNA splicing signatures in trauma patients compared with healthy donors.

Transcriptional activation domains (TADs) are difficult to predict and identify, since they are not conserved and have little consensus. Here, we describe a yeast-based screening method that is able to identify individual amino acid residues involved in transcriptional activation in a high throughput manner. A plant transcriptional activator, PIF3 (phytochrome interacting factor 3), was fused to the yeast GAL4-DNA-binding Domain (BD), driving expression of the URA3 (Orotidine 5'-phosphate decarboxylase) reporter, and used for negative selection on 5-fluroorotic acid (5FOA). Randomly mutagenized variants of PIF3 were then selected for a loss or reduction in transcriptional activation activity by survival on FOA. In the process, we developed a strategy to eliminate false positives from negative selection that can be used for both reverse-1- and 2-hybrid screens. With this method we were able to identify two distinct regions in PIF3 with transcriptional activation activity, both of which are functionally conserved in PIF1, PIF4, and PIF5. Both are collectively necessary for full PIF3 transcriptional activity, but neither is sufficient to induce transcription autonomously. We also found that the TAD appear to overlap physically with other PIF3 functions, such as phyB binding activity and consequent phosphorylation. Our protocol should provide a valuable tool for identifying, analyzing and characterizing novel TADs in eukaryotic transcription factors, and thus potentially contribute to the unraveling of the mechanism underlying transcriptional activation.

Measuring expression profiles of inflammatory biomarkers is important in monitoring the polarization of immune responses; therefore, results should be independent of quantitation methods if they are to be accepted as validated clinical pathology biomarkers. To evaluate effects of differing quantitation methods, the seven major circulating Th1/Th2/Th17 cytokines interleukin 2 (IL-2), interferon γ (IFN-γ), tumor necrosis factor α (TNF-α), IL-4, IL-6, IL-10 and IL-17A were quantified in plasma of lipopolysaccharide (LPS)-treated mice with two different multiplex platforms.

Number of involved lymph nodes (LNs) is a crucial stratification factor in staging of numerous disease sites, but has not been incorporated for endometrial cancer. We evaluated whether number of involved LNs provide improved prognostic value.

Highly sensitive, specific, and point-of-care (POC) serological assays are an essential tool to manage the COVID-19 pandemic. Here, we report on a microfluidic, multiplexed POC test that can profile the antibody response against multiple SARS-CoV-2 antigens - Spike S1 (S1), Nucleocapsid (N), and the receptor binding domain (RBD) - simultaneously from a 60 microliter drop of blood, plasma, or serum. We assessed the levels of anti-SARS-CoV-2 antibodies in plasma samples from 19 individuals (at multiple time points) with COVID-19 that required admission to the intensive care unit and from 10 healthy individuals. This POC assay shows good concordance with a live virus microneutralization assay, achieved high sensitivity (100%) and specificity (100%), and successfully tracked the longitudinal evolution of the antibody response in infected individuals. We also demonstrated that we can detect a chemokine, IP-10, on the same chip, which may provide prognostic insight into patient outcomes. Because our test requires minimal user intervention and is read by a handheld detector, it can be globally deployed in the fight against COVID-19 by democratizing access to laboratory quality tests.

Total and regional body composition are strongly correlated with metabolic syndrome and have been estimated non-invasively from 3D optical scans using linear parameterizations of body shape and linear regression models. Prior works produced accurate and precise predictions on many, but not all, body composition targets relative to the reference dual X-Ray absorptiometry (DXA) measurement. Here, we report the effects of replacing linear models with nonlinear parameterization and regression models on the precision and accuracy of body composition estimation in a novel application of deep 3D convolutional graph networks to human body composition modeling. We assembled an ensemble dataset of 4286 topologically standardized 3D optical scans from four different human body shape databases, DFAUST, CAESAR, Shape Up! Adults, and Shape Up! Kids and trained a parameterized shape model using a graph convolutional 3D autoencoder (3DAE) in lieu of linear PCA. We trained a nonlinear Gaussian process regression (GPR) on the 3DAE parameter space to predict body composition via correlations to paired DXA reference measurements from the Shape Up! scan subset. We tested our model on a set of 424 randomly withheld test meshes and compared the effects of nonlinear computation against prior linear models. Nonlinear GPR produced up to 20% reduction in prediction error and up to 30% increase in precision over linear regression for both sexes in 10 tested body composition variables. Deep shape features produced 6-8% reduction in prediction error over linear PCA features for males only and a 4-14% reduction in precision error for both sexes. Our best performing nonlinear model predicting body composition from deep features outperformed prior work using linear methods on all tested body composition prediction metrics in both precision and accuracy. All coefficients of determination (R2) for all predicted variables were above 0.86. We show that GPR is a more precise and accurate method for modeling body composition mappings from body shape features than linear regression. Deep 3D features learned by a graph convolutional autoencoder only improved male body composition accuracy but improved precision in both sexes. Our work achieved lower estimation RMSEs than all previous work on 10 metrics of body composition.

Dopamine is a mediator of the stimulant properties of drugs of abuse, including ethanol, in mammals and in the fruit fly Drosophila. The neural substrates for the stimulant actions of ethanol in flies are not known. We show that a subset of dopamine neurons and their targets, through the action of the D1-like dopamine receptor DopR, promote locomotor activation in response to acute ethanol exposure. A bilateral pair of dopaminergic neurons in the fly brain mediates the enhanced locomotor activity induced by ethanol exposure, and promotes locomotion when directly activated. These neurons project to the central complex ellipsoid body, a structure implicated in regulating motor behaviors. Ellipsoid body neurons are required for ethanol-induced locomotor activity and they express DopR. Elimination of DopR blunts the locomotor activating effects of ethanol, and this behavior can be restored by selective expression of DopR in the ellipsoid body. These data tie the activity of defined dopamine neurons to D1-like DopR-expressing neurons to form a neural circuit that governs acute responding to ethanol.

Mapping distal regulatory elements, such as enhancers, is a cornerstone for elucidating how genetic variations may influence diseases. Previous enhancer-prediction methods have used either unsupervised approaches or supervised methods with limited training data. Moreover, past approaches have implemented enhancer discovery as a binary classification problem without accurate boundary detection, producing low-resolution annotations with superfluous regions and reducing the statistical power for downstream analyses (e.g. causal variant mapping and functional validations). Here, we addressed these challenges via a two-step model called Deep-learning framework for Condensing enhancers and refining boundaries with large-scale functional assays (DECODE). First, we employed direct enhancer-activity readouts from novel functional characterization assays, such as STARR-seq, to train a deep neural network for accurate cell-type-specific enhancer prediction. Second, to improve the annotation resolution, we implemented a weakly supervised object detection framework for enhancer localization with precise boundary detection (to a 10 bp resolution) using Gradient-weighted Class Activation Mapping.

ENCODE comprises thousands of functional genomics datasets, and the encyclopedia covers hundreds of cell types, providing a universal annotation for genome interpretation. However, for particular applications, it may be advantageous to use a customized annotation. Here, we develop such a custom annotation by leveraging advanced assays, such as eCLIP, Hi-C, and whole-genome STARR-seq on a number of data-rich ENCODE cell types. A key aspect of this annotation is comprehensive and experimentally derived networks of both transcription factors and RNA-binding proteins (TFs and RBPs). Cancer, a disease of system-wide dysregulation, is an ideal application for such a network-based annotation. Specifically, for cancer-associated cell types, we put regulators into hierarchies and measure their network change (rewiring) during oncogenesis. We also extensively survey TF-RBP crosstalk, highlighting how SUB1, a previously uncharacterized RBP, drives aberrant tumor expression and amplifies the effect of MYC, a well-known oncogenic TF. Furthermore, we show how our annotation allows us to place oncogenic transformations in the context of a broad cell space; here, many normal-to-tumor transitions move towards a stem-like state, while oncogene knockdowns show an opposing trend. Finally, we organize the resource into a coherent workflow to prioritize key elements and variants, in addition to regulators. We showcase the application of this prioritization to somatic burdening, cancer differential expression and GWAS. Targeted validations of the prioritized regulators, elements and variants using siRNA knockdowns, CRISPR-based editing, and luciferase assays demonstrate the value of the ENCODE resource.

During transcription, numerous transcription factors (TFs) bind to targets in a highly coordinated manner to control the gene expression. Alterations in groups of TF-binding profiles (i.e. "co-binding changes") can affect the co-regulating associations between TFs (i.e. "rewiring the co-regulator network"). This, in turn, can potentially drive downstream expression changes, phenotypic variation, and even disease. However, quantification of co-regulatory network rewiring has not been comprehensively studied.

Cas12a enzymes are quickly being adopted for use in a variety of genome-editing applications. These programmable nucleases are part of adaptive microbial immune systems, the natural diversity of which has been largely unexplored. Here, we identified novel families of Type V-A CRISPR nucleases through a large-scale analysis of metagenomes collected from a variety of complex environments, and developed representatives of these systems into gene-editing platforms. The nucleases display extensive protein variation and can be programmed by a single-guide RNA with specific motifs. The majority of these enzymes are part of systems recovered from uncultivated organisms, some of which also encode a divergent Type V effector. Biochemical analysis uncovered unexpected protospacer adjacent motif diversity, indicating that these systems will facilitate a variety of genome-engineering applications. The simplicity of guide sequences and activity in human cell lines suggest utility in gene and cell therapies.

Identifying frequently mutated regions is a key approach to discover DNA elements influencing cancer progression. However, it is challenging to identify these burdened regions due to mutation rate heterogeneity across the genome and across different individuals. Moreover, it is known that this heterogeneity partially stems from genomic confounding factors, such as replication timing and chromatin organization. The increasing availability of cancer whole genome sequences and functional genomics data from the Encyclopedia of DNA Elements (ENCODE) may help address these issues.

Alternative RNA splicing provides an important means to expand metazoan transcriptome diversity. Contrary to what was accepted previously, splicing is now thought to predominantly take place during transcription. Motivated by emerging data showing the physical proximity of the spliceosome to Pol II, we surveyed the effect of epigenetic context on co-transcriptional splicing. In particular, we observed that splicing factors were not necessarily enriched at exon junctions and that most epigenetic signatures had a distinctly asymmetric profile around known splice sites. Given this, we tried to build an interpretable model that mimics the physical layout of splicing regulation where the chromatin context progressively changes as the Pol II moves along the guide DNA. We used a recurrent-neural-network architecture to predict the inclusion of a spliced exon based on adjacent epigenetic signals, and we showed that distinct spatio-temporal features of these signals were key determinants of model outcome, in addition to the actual nucleotide sequence of the guide DNA strand. After the model had been trained and tested (with >80% precision-recall curve metric), we explored the derived weights of the latent factors, finding they highlight the importance of the asymmetric time-direction of chromatin context during transcription.

Type II Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)-Cas9 nucleases have been extensively used in biotechnology and therapeutics. However, many applications are not possible owing to the size, targetability, and potential off-target effects associated with currently known systems. In this study, we identified thousands of CRISPR type II effectors by mining an extensive, genome-resolved metagenomics database encompassing hundreds of thousands of microbial genomes. We developed a high-throughput pipeline that enabled us to predict tracrRNA sequences, to design single guide RNAs, and to demonstrate nuclease activity in vitro for 41 newly described subgroups. Active systems represent an extensive diversity of protein sequences and guide RNA structures and require diverse protospacer adjacent motifs (PAMs) that collectively expand the known targeting capability of current systems. Several nucleases showed activity levels comparable to or significantly higher than SpCas9, despite being smaller in size. In addition, top systems exhibited low levels of off-target editing in mammalian cells, and PAM-interacting domain engineered chimeras further expanded their targetability. These newly discovered nucleases are attractive enzymes for translation into many applications, including therapeutics.