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Neuroinflammation and immune dysregulation play a key role in Alzheimer’s disease (AD) and are also associated with severe Covid-19 and neurological symptoms. Also, genome-wide association studies found many risk single nucleotide polymorphisms (SNPs) for AD and Covid-19. However, our understanding of underlying gene regulatory mechanisms from risk SNPs to AD, Covid-19 and phenotypes is still limited. To this end, we performed an integrative multi-omics analysis to predict gene regulatory networks for major brain regions from population data in AD. Our networks linked transcription factors (TFs) to TF binding sites (TFBSs) on regulatory elements to target genes. Comparative network analyses revealed cross-region-conserved and region-specific regulatory networks, in which many immunological genes are present. Furthermore, we identified a list of AD–Covid genes using our networks involving known and Covid-19 genes. Our machine learning analysis prioritized 36 AD–Covid candidate genes for predicting Covid severity. Our independent validation analyses found that these genes outperform known genes for classifying Covid-19 severity and AD. Finally, we mapped genome-wide association study SNPs of AD and severe Covid that interrupt TFBSs on our regulatory networks, revealing potential mechanistic insights of those disease risk variants. Our analyses and results are open-source available, providing an AD–Covid functional genomic resource at the brain region level.

Single-cell multimodal datasets have measured various characteristics of individual cells, enabling a deep understanding of cellular and molecular mechanisms. However, multimodal data generation remains costly and challenging, and missing modalities happen frequently. Recently, machine learning approaches have been developed for data imputation but typically require fully matched multimodalities to learn common latent embeddings that potentially lack modality specificity. To address these issues, we developed an open-source machine learning model, Joint Variational Autoencoders for multimodal Imputation and Embedding (JAMIE). JAMIE takes single-cell multimodal data that can have partially matched samples across modalities. Variational autoencoders learn the latent embeddings of each modality. Then, embeddings from matched samples across modalities are aggregated to identify joint cross-modal latent embeddings before reconstruction. To perform cross-modal imputation, the latent embeddings of one modality can be used with the decoder of the other modality. For interpretability, Shapley values are used to prioritize input features for cross-modal imputation and known sample labels. We applied JAMIE to both simulation data and emerging single-cell multimodal data including gene expression, chromatin accessibility, and electrophysiology in human and mouse brains. JAMIE significantly outperforms existing state-of-the-art methods in general and prioritized multimodal features for imputation, providing potentially novel mechanistic insights at cellular resolution.

Injury to adult mammalian central nervous system (CNS) axons results in limited regeneration. Rodent studies have revealed a developmental switch in CNS axon regenerative ability, yet whether this is conserved in humans is unknown. Using human fibroblasts from 8 gestational-weeks to 72 years-old, we performed direct reprogramming to transdifferentiate fibroblasts into induced neurons (Fib-iNs), avoiding pluripotency which restores cells to an embryonic state. We found that early gestational Fib-iNs grew longer neurites than all other ages, mirroring the developmental switch in regenerative ability in rodents. RNA-sequencing and screening revealed ARID1A as a developmentally-regulated modifier of neurite growth in human neurons. These data suggest that age-specific epigenetic changes may drive the intrinsic loss of neurite growth ability in human CNS neurons during development.One-Sentence Summary:Directly-reprogrammed human neurons demonstrate a developmental decrease in neurite growth ability.

BackgroundGenome-wide association studies have found many genetic risk variants associated with Alzheimer’s disease (AD). However, how these risk variants affect deeper phenotypes such as disease progression and immune response remains elusive. Also, our understanding of cellular and molecular mechanisms from disease risk variants to various phenotypes is still limited. To address these problems, we performed an integrative multi-omics analysis of genotype, transcriptomics, and epigenomics for revealing gene regulatory mechanisms from disease variants to AD phenotypes.MethodFirst, given the population gene expression data of a cohort, we construct and cluster its gene co-expression network to identify gene co-expression modules for various AD phenotypes. Next, we predict transcription factors (TFs) regulating co-expressed genes and AD risk SNPs that interrupt TF binding sites on regulatory elements. Finally, we construct a gene regulatory network (GRN) linking SNPs, interrupted TFs, and regulatory elements to target genes and gene modules for each phenotype in the cohort. This network thus provides systematic insights into gene regulatory mechanisms from risk variants to AD phenotypes.ResultsOur analysis predicted GRNs in three major AD-relevant regions: Hippocampus, Dorsolateral Prefrontal Cortex (DLPFC), Lateral Temporal Lobe (LTL). Comparative analyses revealed cross-region-conserved and region-specific GRNs, in which many immunological genes are present. For instance, SNPs rs13404184 and rs61068452 disrupt SPI1 binding and regulation of AD gene INPP5D in the Hippocampus and LTL. However, SNP rs117863556 interrupts bindings of REST to regulate GAB2 in DLPFC only. Driven by emerging neuroinflammation in AD, we used Covid-19 as a proxy to identify possible regulatory mechanisms for neuroimmunology in AD. To this end, we looked at the GRN subnetworks relating to genes from shared AD-Covid pathways. From those subnetworks, our machine learning analysis prioritized the AD-Covid genes for predicting Covid-19 severity. Decision Curve Analysis also validated our AD-Covid genes outperform known Covid-19 genes for classifying severe Covid-19 patients. This suggests AD-Covid genes along with linked SNPs can be potential novel biomarkers for neuroimmunology in AD. Finally, our results are open-source available as a comprehensive functional genomic map for AD, providing a deeper mechanistic understanding of the interplay among multi-omics, brain regions, gene functions like neuroimmunology, and phenotypes.

Parvalbumin interneurons (PVIs) are affected in many psychiatric disorders including schizophrenia (SCZ), however the mechanism remains unclear. FXR1, a high confident risk gene for SCZ, is indispensable but its role in the brain is largely unknown. We show that deleting FXR1 from PVIs of medial prefrontal cortex (mPFC) leads to reduced PVI excitability, impaired mPFC gamma oscillation, and SCZ-like behaviors. PVI-specific translational profiling reveals that FXR1 regulates the expression of Cacna1h/Cav3.2 a T-type calcium channel implicated in autism and epilepsy. Inhibition of Cav3.2 in PVIs of mPFC phenocopies whereas elevation of Cav3.2 in PVIs of mPFC rescues behavioral deficits resulted from FXR1 deficiency. Stimulation of PVIs using a gamma oscillation-enhancing light flicker rescues behavioral abnormalities cause by FXR1 deficiency in PVIs. This work unveils the function of a newly identified SCZ risk gene in SCZ-relevant neurons and identifies a therapeutic target and a potential non-invasive treatment for psychiatric disorders.

Summary Population studies such as genome-wide association study have identified a variety of genomic variants associated with human diseases. To further understand potential mechanisms of disease variants, recent statistical methods associate functional omic data (e.g. gene expression) with genotype and phenotype and link variants to individual genes. However, how to interpret molecular mechanisms from such associations, especially across omics, is still challenging. To address this problem, we developed an interpretable deep learning method, Varmole, to simultaneously reveal genomic functions and mechanisms while predicting phenotype from genotype. In particular, Varmole embeds multi-omic networks into a deep neural network architecture and prioritizes variants, genes and regulatory linkages via biological drop-connect without needing prior feature selections. Availability and implementation Varmole is available as a Python tool on GitHub at https://github.com/daifengwanglab/Varmole. Supplementary information Supplementary data are available at Bioinformatics online.

The phenotypes of complex biological systems are fundamentally driven by various multi-scale mechanisms. Multi-modal data, such as single cell multi-omics data, enables a deeper understanding of underlying complex mechanisms across scales for phenotypes. We developed an interpretable regularized learning model, deepManReg, to predict phenotypes from multi-modal data. First, deepManReg employs deep neural networks to learn cross-modal manifolds and then to align multi-modal features onto a common latent space. Second, deepManReg uses cross-modal manifolds as a feature graph to regularize the classifiers for improving phenotype predictions and also for prioritizing the multi-modal features and cross-modal interactions for the phenotypes. We applied deepManReg to (1) an image dataset of handwritten digits with multi-features and (2) single cell multi-modal data (Patch-seq data) including transcriptomics and electrophysiology for neuronal cells in the mouse brain. We show that deepManReg improved phenotype prediction in both datasets, and also prioritized genes and electrophysiological features for the phenotypes of neuronal cells.

Alzheimer’s disease (AD) has a strong genetic predisposition. However, its risk genes remain incompletely identified. We developed an Alzheimer’s brain gene network-based approach to predict AD-associated genes by leveraging the functional pattern of known AD-associated genes. Our constructed network outperformed existing networks in predicting AD genes. We then systematically validated the predictions using independent genetic, transcriptomic, proteomic data, neuropathological and clinical data. First, top-ranked genes were enriched in AD-associated pathways. Second, using external gene expression data from the Mount Sinai Brain Bank study, we found that the top-ranked genes were significantly associated with neuropathological and clinical traits, including the Consortium to Establish a Registry for Alzheimer’s Disease score, Braak stage score and clinical dementia rating. The analysis of Alzheimer’s brain single-cell RNA-seq data revealed cell-type-specific association of predicted genes with early pathology of AD. Third, by interrogating proteomic data in the Religious Orders Study and Memory and Aging Project and Baltimore Longitudinal Study of Aging studies, we observed a significant association of protein expression level with cognitive function and AD clinical severity. The network, method and predictions could become a valuable resource to advance the identification of risk genes for AD.

Modeling age‐related neurodegenerative disorders with human stem cells are difficult due to the embryonic nature of stem cell‐derived neurons. We developed a chemical cocktail to induce senescence of iPSC‐derived neurons to address this challenge. We first screened small molecules that induce embryonic fibroblasts to exhibit features characteristic of aged fibroblasts. We then optimized a cocktail of small molecules that induced senescence in fibroblasts and cortical neurons without causing DNA damage. The utility of the “senescence cocktail” was validated in motor neurons derived from ALS patient iPSCs which exhibited protein aggregation and axonal degeneration substantially earlier than those without cocktail treatment. Our “senescence cocktail” will likely enhance the manifestation of disease‐related phenotypes in neurons derived from iPSCs, enabling the generation of reliable drug discovery platforms., Fathi et al developed a “senescence cocktail” by screening small molecules on fibroblasts and hPSC‐derived cortical neurons for reduced expression of H3K9Me3, HP1γ, LAP2β, decreased mitochondrial activity and increased protein aggregation and beta galactosidase activity. This senescence cocktail induced senescence in hPSC‐derived motor neurons and degenerative changes in ALS (TDP43 G298S) iPSC‐derived motor neurons.

BackgroundNeuropsychiatric disorders afflict a large portion of the global population and constitute a significant source of disability worldwide. Although Genome-wide Association Studies (GWAS) have identified many disorder-associated variants, the underlying regulatory mechanisms linking them to disorders remain elusive, especially those involving distant genomic elements. Expression quantitative trait loci (eQTLs) constitute a powerful means of providing this missing link. However, most eQTL studies in human brains have focused exclusively on cis-eQTLs, which link variants to nearby genes (i.e., those within 1 Mb of a variant). A complete understanding of disease etiology requires a clearer understanding of trans-regulatory mechanisms, which, in turn, entails a detailed analysis of the relationships between variants and expression changes in distant genes.MethodsBy leveraging large datasets from the PsychENCODE consortium, we conducted a genome-wide survey of trans-eQTLs in the human dorsolateral prefrontal cortex. We also performed colocalization and mediation analyses to identify mediators in trans-regulation and use trans-eQTLs to link GWAS loci to schizophrenia risk genes.ResultsWe identified ~80,000 candidate trans-eQTLs (at FDR60% of these variants (by colocalization), the cis-eQTL’s target gene acts as a mediator for the trans-eQTL SNP's effect on the trans-eGene, highlighting examples of cis-mediation as essential for trans-regulation. Furthermore, many of these colocalized variants fall into a discernable pattern wherein cis-eQTL’s target is a transcription factor or RNA-binding protein, which, in turn, targets the gene associated with the candidate trans-eQTL. Finally, we show that trans-regulatory mechanisms provide valuable insights into psychiatric disorders: beyond what had been possible using only cis-eQTLs, we link an additional 23 GWAS loci and 90 risk genes (using colocalization between candidate trans-eQTLs and schizophrenia GWAS loci).ConclusionsWe demonstrate that the transcriptional architecture of the human brain is orchestrated by both cis- and trans-regulatory variants and found that trans-eQTLs provide insights into brain-disease biology.

The mechanism of Alzheimer’s disease (AD) remains elusive, partly due to the incomplete identification of risk genes. We developed an approach to predict AD-associated genes by learning the functional pattern of curated AD-associated genes from brain gene networks. We created a pipeline to evaluate disease-gene association by interrogating heterogeneous biological networks at different molecular levels. Our analysis showed that top-ranked genes were functionally related to AD. We identified gene modules associated with AD pathways, and found that top-ranked genes were correlated with both neuropathological and clinical phenotypes of AD on independent datasets. We also identified potential causal variants for genes such as FYN and PRKAR1A by integrating brain eQTL and ATAC-seq data. Lastly, we created the ALZLINK web interface, enabling users to exploit the functional relevance of predicted genes to AD. The predictions and pipeline could become a valuable resource to advance the identification of therapeutic targets for AD.

The biological processes from genotype to phenotype are complex involving multi-scale mechanisms. Increasing multi-modal data enables deeper understanding of underlying complex mechanisms in various phenotypes. However, integrating and interpreting such large-scale multi-modal data remains challenging, especially given highly heterogeneous, nonlinear relationships across modalities. To address this, we developed an interpretable regularized learning model, deepManReg to predict phenotypes from multi-modal data. First, deepManReg employs deep neural networks to learn cross-modal manifolds and then align multi-modal features onto a common latent space. This space aims to preserve both global consistency and local smoothness across modalities and reveal higher-order nonlinear cross-modal relationships. Second, deepManReg uses cross-modal manifolds as a feature graph to regularize the classifiers for improving phenotype predictions and also prioritizing the multi-modal features and cross-modal interactions for the phenotypes. We applied deepManReg to recent single cell multi-modal data such as Patch-seq data including transcriptomics and electrophysiology for neuronal cells in the mouse brain. We show that deepManReg significantly improves predicting cellular phenotypes and also prioritizing genes and electrophysiological features for the phenotypes. Finally, deepManReg is open-source and general for phenotype prediction from multi-modal data. deepManReg is open-source available at https://github.com/daifengwanglab/deepManReg.

Classically, psychiatric disorders have been considered to lack defining pathology, but recent work has demonstrated consistent disruption at the molecular level, characterized by transcriptomic and epigenetic alterations.1–3 In ASD, upregulation of microglial, astrocyte, and immune signaling genes, downregulation of specific synaptic genes, and attenuation of regional gene expression differences are observed.1,2,4–6 However, whether these changes are limited to the cortical association areas profiled is unknown. Here, we perform RNA-sequencing (RNA-seq) on 725 brain samples spanning 11 distinct cortical areas in 112 ASD cases and neurotypical controls. We identify substantially more genes and isoforms that differentiate ASD from controls than previously observed. These alterations are pervasive and cortex-wide, but vary in magnitude across regions, roughly showing an anterior to posterior gradient, with the strongest signal in visual cortex, followed by parietal cortex and the temporal lobe. We find a notable enrichment of ASD genetic risk variants among cortex-wide downregulated synaptic plasticity genes and upregulated protein folding gene isoforms. Finally, using snRNA-seq, we determine that regional variation in the magnitude of transcriptomic dysregulation reflects changes in cellular proportion and cell-type-specific gene expression, particularly impacting L3/4 excitatory neurons. These results highlight widespread, genetically-driven neuronal dysfunction as a major component of ASD pathology in the cerebral cortex, extending beyond association cortices to involve primary sensory regions.

Recent single-cell multi-modal data reveal multi-scale characteristics of single cells, such as transcriptomics, morphology, and electrophysiology. However, our understanding of functional genomics and gene regulation leading to various cellular characteristics remains elusive. To address this, we applied multiple machine learning methods to align gene expression and electrophysiological data of single neuronal cells in the mouse brain. We found that nonlinear manifold learning outperforms other methods. After manifold alignment, the cell clusters highly correspond to transcriptomic and morphological cell-types, suggesting a strong nonlinear relationship between gene expression and electrophysiology at the cell-type level. The aligned cells form developmental trajectories and show continuous changes of electrophysiological features, implying the underlying developmental process. We also found that the manifold-aligned cell clusters’ differentially expressed genes can predict many electrophysiological features. Functional enrichment and gene regulatory network analyses for those cell clusters revealed potential genome functions and molecular mechanisms from gene expression to neuronal electrophysiology.

Summary: Voluntary running enhances adult hippocampal neurogenesis, with consequences for hippocampal-dependent learning ability and mood regulation. However, the underlying mechanism remains unclear. Here, we show that voluntary running induces unique and dynamic gene expression changes specifically within the adult-born hippocampal neurons, with significant impact on genes involved in neuronal maturation and human diseases. We identify the regulator of G protein signaling 6 (RGS6) as a key factor that mediates running impact on adult-born neurons. RGS6 overexpression mimics the positive effects of voluntary running on morphological and physiological maturation of adult new neurons and reduced sensitivity of adult-born neurons to the inhibitory effect of GABAB (γ-Aminobutyric acid B) receptor activation. Knocking down RGS6 abolishes running-enhanced neuronal maturation and hippocampal neurogenesis-dependent learning and anxiolytic effect. Our study provides a data resource showing genome-wide intrinsic molecular changes in adult-born hippocampal neurons that contribute to voluntary running-induced neurogenesis.

Understanding cell-type-specific gene regulatory mechanisms from genetic variants to diseases remains challenging. To address this, we developed a computational pipeline, scGRNom (single-cell Gene Regulatory Network prediction from multi-omics), to predict cell-type disease genes and regulatory networks including transcription factors and regulatory elements. With applications to schizophrenia and Alzheimer’s disease, we predicted disease genes and regulatory networks for excitatory and inhibitory neurons, microglia, and oligodendrocytes. Further enrichment analyses revealed cross-disease and disease-specific functions and pathways at the cell-type level. Our machine learning analysis also found that cell-type disease genes improved clinical phenotype predictions. scGRNom is a general-purpose tool available at https://github.com/daifengwanglab/scGRNom.

Population studies such as GWAS have identified a variety of genomic variants associated with human diseases. To further understand potential mechanisms of disease variants, recent statistical methods associate functional omic data (e.g., gene expression) with genotype and phenotype and link variants to individual genes. However, how to interpret molecular mechanisms from such associations, especially across omics is still challenging. To address this, we develop an interpretable deep learning method, Varmole to simultaneously reveal genomic functions and mechanisms while predicting phenotype from genotype. In particular, Varmole embeds multi-omic networks into a deep neural network architecture and prioritizes variants, genes and regulatory linkages via drop-connect without needing prior feature selections. Varmole is available as a Python package on GitHub at https://github.com/daifengwanglab/Varmole.

Multiple developmental processes go awry in Down syndrome (DS, trisomy 21, Ts21), the most common genetic cause of intellectual disability and a complex multigene disorder. DS individuals have smaller brains with reduced volume of frontal and temporal lobes, including hippocampus. This smaller brain volume corresponds to fewer neurons in the DS cortex measured at pre-and post-natal stages, implicating impaired neurogenesis during development. We employ Ts21 human induced pluripotent stem cells (iPSCs) and isogenic controls to identify disrupted interneuron developmental processes in DS. Interneuron progenitor specification, proliferation and migration is largely controlled by integrating known morphogen gradients, sonic hedgehog (SHH) and Wingless (WNT), and we find that Ts21 progenitors have altered patterning and generate different progenitor populations. Specifically, we find expression of WNT signaling genes is reduced and expression of GLI genes that regulate WNT and SHH signaling in this population is increased in Ts21 interneuron progenitors, suggesting Ts21 progenitors are patterned more ventrally and less caudally. Altered patterning affects lineage decisions and, in fact, fewer caudal interneuron progenitors expressing the transcription factor COUP-TFII differentiate from Ts21 iPSCs. In mouse, COUP-TFII+ progenitors are found in more caudal regions of the neurogenic regions, thus linking the patterning changes to altered generation of interneuron subpopulations. These data identify affected progenitor subpopulations in Ts21 and suggest that abnormal patterning of human Ts21 interneuron progenitors alters the generation of interneurons in DS and contributes to the reduced neurogenesis in DS.

Background The coordination of genomic functions is a critical and complex process across biological systems such as phenotypes or states (e.g., time, disease, organism, environmental perturbation). Understanding how the complexity of genomic function relates to these states remains a challenge. To address this, we have developed a novel computational method, ManiNetCluster, which simultaneously aligns and clusters gene networks (e.g., co-expression) to systematically reveal the links of genomic function between different conditions. Specifically, ManiNetCluster employs manifold learning to uncover and match local and non-linear structures among networks, and identifies cross-network functional links. Results We demonstrated that ManiNetCluster better aligns the orthologous genes from their developmental expression profiles across model organisms than state-of-the-art methods (p-value −16). This indicates the potential non-linear interactions of evolutionarily conserved genes across species in development. Furthermore, we applied ManiNetCluster to time series transcriptome data measured in the green alga Chlamydomonas reinhardtii to discover the genomic functions linking various metabolic processes between the light and dark periods of a diurnally cycling culture. We identified a number of genes putatively regulating processes across each lighting regime. Conclusions ManiNetCluster provides a novel computational tool to uncover the genes linking various functions from different networks, providing new insight on how gene functions coordinate across different conditions. ManiNetCluster is publicly available as an R package at https://github.com/daifengwanglab/ManiNetCluster.

Author(s): Arkin, Adam P; Cottingham, Robert W; Henry, Christopher S; Harris, Nomi L; Stevens, Rick L; Maslov, Sergei; Dehal, Paramvir; Ware, Doreen; Perez, Fernando; Canon, Shane; Sneddon, Michael W; Henderson, Matthew L; Riehl, William J; Murphy-Olson, Dan; Chan, Stephen Y; Kamimura, Roy T; Kumari, Sunita; Drake, Meghan M; Brettin, Thomas S; Glass, Elizabeth M; Chivian, Dylan; Gunter, Dan; Weston, David J; Allen, Benjamin H; Baumohl, Jason; Best, Aaron A; Bowen, Ben; Brenner, Steven E; Bun, Christopher C; Chandonia, John-Marc; Chia, Jer-Ming; Colasanti, Ric; Conrad, Neal; Davis, James J; Davison, Brian H; DeJongh, Matthew; Devoid, Scott; Dietrich, Emily; Dubchak, Inna; Edirisinghe, Janaka N; Fang, Gang; Faria, Jose P; Frybarger, Paul M; Gerlach, Wolfgang; Gerstein, Mark; Greiner, Annette; Gurtowski, James; Haun, Holly L; He, Fei; Jain, Rashmi; Joachimiak, Marcin P; Keegan, Kevin P; Kondo, Shinnosuke; Kumar, Vivek; Land, Miriam L; Meyer, Folker; Mills, Marissa; Novichkov, Pavel S; Oh, Taeyun; Olsen, Gary J; Olson, Robert; Parrello, Bruce; Pasternak, Shiran; Pearson, Erik; Poon, Sarah S; Price, Gavin A; Ramakrishnan, Srividya; Ranjan, Priya; Ronald, Pamela C; Schatz, Michael C; Seaver, Samuel MD; Shukla, Maulik; Sutormin, Roman A; Syed, Mustafa H; Thomason, James; Tintle, Nathan L; Wang, Daifeng; Xia, Fangfang; Yoo, Hyunseung; Yoo, Shinjae; Yu, Dantong

Motivation Gene expression and regulation, a key molecular mechanism driving human disease development, remains elusive, especially at early stages. Integrating the increasing amount of population-level genomic data and understanding gene regulatory mechanisms in disease development are still challenging. Machine learning has emerged to solve this, but many machine learning methods were typically limited to building an accurate prediction model as a ‘black box’, barely providing biological and clinical interpretability from the box. Results To address these challenges, we developed an interpretable and scalable machine learning model, ECMarker, to predict gene expression biomarkers for disease phenotypes and simultaneously reveal underlying regulatory mechanisms. Particularly, ECMarker is built on the integration of semi- and discriminative-restricted Boltzmann machines, a neural network model for classification allowing lateral connections at the input gene layer. This interpretable model is scalable without needing any prior feature selection and enables directly modeling and prioritizing genes and revealing potential gene networks (from lateral connections) for the phenotypes. With application to the gene expression data of non-small-cell lung cancer patients, we found that ECMarker not only achieved a relatively high accuracy for predicting cancer stages but also identified the biomarker genes and gene networks implying the regulatory mechanisms in the lung cancer development. In addition, ECMarker demonstrates clinical interpretability as its prioritized biomarker genes can predict survival rates of early lung cancer patients (P-value < 0.005). Finally, we identified a number of drugs currently in clinical use for late stages or other cancers with effects on these early lung cancer biomarkers, suggesting potential novel candidates on early cancer medicine. Availabilityand implementation ECMarker is open source as a general-purpose tool at https://github.com/daifengwanglab/ECMarker. Supplementary information Supplementary data are available at Bioinformatics online.

In this article, we present a high-efficiency electromagnetic transducer harvesting kinetic energy from the ocean current for self-powered technique in underwater robots to tackle the electric energy replenishment problem. A circular magnet array with alternating magnet arrangement was employed for providing a sudden change in magnetic flux density. A gear train was introduced into the compact design to greatly increase the rotation speed of the rotor. The finite element method is used to simulate and compare the magnetic flux density distributions and the induced voltages in the coil under different magnet arrays (conventional Halbach magnet array and proposed alternating one) with different magnet dimensions and numbers. The finite element method analysis shows that the alternating array displays a much larger changing magnetic flux density rate resulting in high induced electromotive forces. The experimental results based on a fabricated prototype indicate that the transducer yields maximum average output power of about 0.51 W with an energy conversion efficiency of 30.91% under the water flow speed of 0.64 m/s with a load resistance of 1 kΩ. We also investigated the capability of the transducer for self-powered applications in underwater robots. The transducer charged a 20 mF capacitor from 0 V to 29 V in 50 s. Also, the prototype charged a lithium battery embedded inside an underwater robot by 75% within 400 s. This study, with a broad application prospect, can advance the future development of self-powered underwater robotic systems.

Prostate cancer (PCa) is one of the most common cancers in men worldwide and a leading cause of cancer-related mortality. Though PCa is known to be substantially heterogeneous at the molecular and histological level, there is still little understanding of the tumor subpopulations (“clones”) generating this intratumor heterogeneity (ITH) and their underlying clonal dynamics and interactions. The impact of ITH on disease outcome remains unknown and is thus a significant hurdle in treatment of PCa. This barrier is especially evident in progression to castration resistance: after initial androgen dependence, subpopulations that are castration resistant and less dependent on androgens emerge to populate the tumor, contributing to disease progression. The introduction of second generation androgen deprivation therapies has shown some success in treating castration resistant prostate cancer (CRPC), but eventually drug resistant clones emerge and the disease recurs. Understanding the fundamental differences–both phenotypic and genomic–between clones may hold the key to improving therapies, preventing disease progression, and prolonging patient survival. Using a multi-omics approach, we are exploring the dynamic changes in these subpopulations during progression to castration and drug resistance, focusing on the differences between clones at the transcriptomic and genomic level. Thus far, we have developed a mouse model of ITH in CRPC with the multicolor Confetti reporter, allowing for spatial distinction between different clones. Using single cell sequencing and RNAseq, we have mapped the evolution of early-stage clones post-castration and identified pathways and gene regulatory networks that potentially grant specific clones their castration resistant phenotype. We have extended our system to in vitro 3D organoid cultures to study the evolution of these clones longitudinally during treatment with the antiandrogens enzalutamide and abiraterone, developing a drug resistant in vitro model of CRPC. We are currently using our organoid system to identify novel drug resistance mechanisms in order to target advanced CRPC, which until now has been considered untreatable and lethal. By characterizing clonal dynamics during disease progression, our studies provide new mechanistic insights for identifying pre-existing high-risk clones with drug resistant phenotypes in PCa and uncovering novel therapeutic targets in CRPC. Our work has implications for designing new therapies aimed to control the emergence of advanced disease, preventing CRPC and drug resistance, with the ultimate goal of reducing mortality and improving prognosis. Citation Format: Lara F Tshering, Fu Luo, Nam D. Nguyen, James G. Rail, Ting Jin, Daifeng Wang, Flaminia Talos. Intratumor heterogeneity and clonal dynamics underlying treatment resistance in prostate cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 3126.

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., ENCODE is a resource comprising thousands of functional genomic datasets. Here, the authors present custom annotation within ENCODE for cancer, highlighting a workflow that can help prioritise key elements in oncogenesis.

We compare the "patterns of mutation" in biological and technological networks. Negative selection at central nodes in biological networks has been widely reported; however, we show technological networks have an opposite trend. This suggests a potential contrast: biological evolution involves random tinkering, whereas man-made systems change according to rational planning.

Mutational activation of the epidermal growth factor receptor (EGFR) is a major player in the pathogenesis of non-small cell lung cancer (NSCLC). NSCLC patients with constitutively active EGFR mutations eventually develop drug resistance against EGFR tyrosine-kinase inhibitors; therefore, better understandings of key components of mutant EGFR (mtEGFR) signaling are required. Here, we initially observed aberrantly high expression of protein kinase Cα (PKCα) in lung adenocarcinomas, especially those with EGFR mutations, and proceeded to examine the role of PKCα in the regulation of the signaling pathways downstream of mtEGFR. The results showed that NSCLC cell lines with constitutively active EGFR mutations tend to have very or moderately high PKCα levels. Furthermore, PKCα was constitutively activated in HCC827 and H4006 cells which have an EGFR deletion mutation in exon 19. Interestingly, mtEGFR was not required for the induction of PKCα at protein and message levels suggesting that the increased levels of PKCα are due to independent selection. On the other hand, mtEGFR activity was required for robust activation of PKCα. Loss of functions studies revealed that the NSCLC cells rely heavily on PKCα for the activation of the mTORC1 signaling pathway. Unexpectedly, the results demonstrated that PKCα was required for activation of Akt upstream of mTOR but only in cells with the mtEGFR and with the increased expression of PKCα. Functionally, inhibition of PKCα in HCC827 led to caspase-3-dependent apoptosis and a significant decrease in cell survival in response to cellular stress induced by serum starvation. In summary, the results identified important roles of PKCα in regulating mTORC1 activity in lung cancer cells, whereby a primary switching occurs from PKCα-independent to PKCα-dependent signaling in the presence of EGFR mutations. The results present PKCα as a potential synergistic target of personalized treatment for NSCLC with constitutively active mutant forms of EGFR and constitutively active PKCα.