NBAtlas: A harmonized single-cell transcriptomic reference atlas of human neuroblastoma tumors.

Neuroblastoma, a rare embryonic tumor arising from neural crest development, is responsible for 15% of pediatric cancer-related deaths. Recently, several single-cell transcriptome studies were performed on neuroblastoma patient samples to investigate the cell of origin and tumor heterogeneity. However, these individual studies involved a small number of tumors and cells, limiting the conclusions that could be drawn. To overcome this limitation, we integrated seven single-cell or single-nucleus datasets into a harmonized cell atlas covering 362,991 cells across 61 patients. We use this atlas to decipher the transcriptional landscape of neuroblastoma at single-cell resolution, revealing associations between transcriptomic profiles and clinical outcomes within the tumor compartment. In addition, we characterize the complex immune-cell landscape and uncover considerable heterogeneity among tumor-associated macrophages. Finally, we showcase the utility of our atlas as a resource by expanding it with additional data and using it as a reference for data-driven cell-type annotation.

A single-cell multimodal view on gene regulatory network inference from transcriptomics and chromatin accessibility data.

Eukaryotic gene regulation is a combinatorial, dynamic, and quantitative process that plays a vital role in development and disease and can be modeled at a systems level in gene regulatory networks (GRNs). The wealth of multi-omics data measured on the same samples and even on the same cells has lifted the field of GRN inference to the next stage. Combinations of (single-cell) transcriptomics and chromatin accessibility allow the prediction of fine-grained regulatory programs that go beyond mere correlation of transcription factor and target gene expression, with enhancer GRNs (eGRNs) modeling molecular interactions between transcription factors, regulatory elements, and target genes. In this review, we highlight the key components for successful (e)GRN inference from (sc)RNA-seq and (sc)ATAC-seq data exemplified by state-of-the-art methods as well as open challenges and future developments. Moreover, we address preprocessing strategies, metacell generation and computational omics pairing, transcription factor binding site detection, and linear and three-dimensional approaches to identify chromatin interactions as well as dynamic and causal eGRN inference. We believe that the integration of transcriptomics together with epigenomics data at a single-cell level is the new standard for mechanistic network inference, and that it can be further advanced with integrating additional omics layers and spatiotemporal data, as well as with shifting the focus towards more quantitative and causal modeling strategies.

Vagus nerve stimulation enhances remyelination and decreases innate neuroinflammation in lysolecithin-induced demyelination.

BACKGROUND: Current treatments for Multiple Sclerosis (MS) poorly address chronic innate neuroinflammation nor do they offer effective remyelination. The vagus nerve has a strong regulatory role in inflammation and Vagus Nerve Stimulation (VNS) has potential to affect both neuroinflammation and remyelination in MS. OBJECTIVE: This study investigated the effects of VNS on demyelination and innate neuroinflammation in a validated MS rodent model. METHODS: Lysolecithin (LPC) was injected in the corpus callosum (CC) of 46 Lewis rats, inducing a demyelinated lesion. 33/46 rats received continuously-cycled VNS (cVNS) or one-minute per day VNS (1minVNS) or sham VNS from 2 days before LPC-injection until perfusion at 3 days post-injection (dpi) (corresponding with a demyelinated lesion with peak inflammation). 13/46 rats received cVNS or sham from 2 days before LPC-injection until perfusion at 11 dpi (corresponding with a partial remyelinated lesion). Immunohistochemistry and proteomics analyses were performed to investigate the extend of demyelination and inflammation. RESULTS: Immunohistochemistry showed that cVNS significantly reduced microglial and astrocytic activation in the lesion and lesion border, and significantly reduced the Olig2+ cell count at 3 dpi. Furthermore, cVNS significantly improved remyelination with 57.4 % versus sham at 11 dpi. Proteomic gene set enrichment analyses showed increased activation of (glutamatergic) synapse pathways in cVNS versus sham, most pronounced at 3 dpi. CONCLUSION: cVNS improved remyelination of an LPC-induced lesion. Possible mechanisms might include modulation of microglia and astrocyte activity, increased (glutamatergic) synapses and enhanced oligodendrocyte clearance after initial injury.

Evaluation of single-sample network inference methods for precision oncology.

A major challenge in precision oncology is to detect targetable cancer vulnerabilities in individual patients. Modeling high-throughput omics data in biological networks allows identifying key molecules and processes of tumorigenesis. Traditionally, network inference methods rely on many samples to contain sufficient information for learning, resulting in aggregate networks. However, to implement patient-tailored approaches in precision oncology, we need to interpret omics data at the level of individual patients. Several single-sample network inference methods have been developed that infer biological networks for an individual sample from bulk RNA-seq data. However, only a limited comparison of these methods has been made and many methods rely on 'normal tissue' samples as reference, which are not always available. Here, we conducted an evaluation of the single-sample network inference methods SSN, LIONESS, SWEET, iENA, CSN and SSPGI using transcriptomic profiles of lung and brain cancer cell lines from the CCLE database. The methods constructed functional gene networks with distinct network characteristics. Hub gene analyses revealed different degrees of subtype-specificity across methods. Single-sample networks were able to distinguish between tumor subtypes, as exemplified by node strength clustering, enrichment of known subtype-specific driver genes among hubs and differential node strength. We also showed that single-sample networks correlated better to other omics data from the same cell line as compared to aggregate networks. We conclude that single-sample network inference methods can reflect sample-specific biology when 'normal tissue' samples are absent and we point out peculiarities of each method.

Human iPSC modeling recapitulates in vivo sympathoadrenal development and reveals an aberrant developmental subpopulation in familial neuroblastoma.

Studies defining normal and disrupted human neural crest cell development have been challenging given its early timing and intricacy of development. Consequently, insight into the early disruptive events causing a neural crest related disease such as pediatric cancer neuroblastoma is limited. To overcome this problem, we developed an in vitro differentiation model to recapitulate the normal in vivo developmental process of the sympathoadrenal lineage which gives rise to neuroblastoma. We used human in vitro pluripotent stem cells and single-cell RNA sequencing to recapitulate the molecular events during sympathoadrenal development. We provide a detailed map of dynamically regulated transcriptomes during sympathoblast formation and illustrate the power of this model to study early events of the development of human neuroblastoma, identifying a distinct subpopulation of cell marked by SOX2 expression in developing sympathoblast obtained from patient derived iPSC cells harboring a germline activating mutation in the anaplastic lymphoma kinase (ALK) gene.

HTSplotter: An end-to-end data processing, analysis and visualisation tool for chemical and genetic in vitro perturbation screening.

In biomedical research, high-throughput screening is often applied as it comes with automatization, higher-efficiency, and more and faster results. High-throughput screening experiments encompass drug, drug combination, genetic perturbagen or a combination of genetic and chemical perturbagen screens. These experiments are conducted in real-time assays over time or in an endpoint assay. The data analysis consists of data cleaning and structuring, as well as further data processing and visualisation, which, due to the amount of data, can easily become laborious, time-consuming and error-prone. Therefore, several tools have been developed to aid researchers in this process, but these typically focus on specific experimental set-ups and are unable to process data of several time points and genetic-chemical perturbagen screens. To meet these needs, we developed HTSplotter, a web tool and Python module that performs automatic data analysis and visualization of visualization of eitherendpoint or real-time assays from different high-throughput screening experiments: drug, drug combination, genetic perturbagen and genetic-chemical perturbagen screens. HTSplotter implements an algorithm based on conditional statements to identify experiment types and controls. After appropriate data normalization, including growth rate normalization, HTSplotter executes downstream analyses such as dose-response relationship and drug synergism assessment by the Bliss independence (BI), Zero Interaction Potency (ZIP) and Highest Single Agent (HSA) methods. All results are exported as a text file and plots are saved in a PDF file. The main advantage of HTSplotter over other available tools is the automatic analysis of genetic-chemical perturbagen screens and real-time assays where growth rate and perturbagen effect results are plotted over time. In conclusion, HTSplotter allows for the automatic end-to-end data processing, analysis and visualisation of various high-throughput in vitro cell culture screens, offering major improvements in terms of versatility, efficiency and time over existing tools.

Opportunities and challenges in interpretable deep learning for drug sensitivity prediction of cancer cells.

In precision oncology, therapy stratification is done based on the patients' tumor molecular profile. Modeling and prediction of the drug response for a given tumor molecular type will further improve therapeutic decision-making for cancer patients. Indeed, deep learning methods hold great potential for drug sensitivity prediction, but a major problem is that these models are black box algorithms and do not clarify the mechanisms of action. This puts a limitation on their clinical implementation. To address this concern, many recent studies attempt to overcome these issues by developing interpretable deep learning methods that facilitate the understanding of the logic behind the drug response prediction. In this review, we discuss strengths and limitations of recent approaches, and suggest future directions that could guide further improvement of interpretable deep learning in drug sensitivity prediction in cancer research.

Whole transcriptome profiling of liquid biopsies from tumour xenografted mouse models enables specific monitoring of tumour-derived extracellular RNA.

While cell-free DNA (cfDNA) is widely being investigated, free circulating RNA (extracellular RNA, exRNA) has the potential to improve cancer therapy response monitoring and detection due to its dynamic nature. However, it remains unclear in which blood subcompartment tumour-derived exRNAs primarily reside. We developed a host-xenograft deconvolution framework, exRNAxeno, with mapping strategies to either a combined human-mouse reference genome or both species genomes in parallel, applicable to exRNA sequencing data from liquid biopsies of human xenograft mouse models. The tool enables to distinguish (human) tumoural RNA from (murine) host RNA, to specifically analyse tumour-derived exRNA. We applied the combined pipeline to total exRNA sequencing data from 95 blood-derived liquid biopsy samples from 30 mice, xenografted with 11 different tumours. Tumoural exRNA concentrations are not determined by plasma platelet levels, while host exRNA concentrations increase with platelet content. Furthermore, a large variability in exRNA abundance and transcript content across individual mice is observed. The tumoural gene detectability in plasma is largely correlated with the RNA expression levels in the tumour tissue or cell line. These findings unravel new aspects of tumour-derived exRNA biology in xenograft models and open new avenues to further investigate the role of exRNA in cancer.

SUBATOMIC: a SUbgraph BAsed mulTi-OMIcs clustering framework to analyze integrated multi-edge networks.

BACKGROUND: Representing the complex interplay between different types of biomolecules across different omics layers in multi-omics networks bears great potential to gain a deep mechanistic understanding of gene regulation and disease. However, multi-omics networks easily grow into giant hairball structures that hamper biological interpretation. Module detection methods can decompose these networks into smaller interpretable modules. However, these methods are not adapted to deal with multi-omics data nor consider topological features. When deriving very large modules or ignoring the broader network context, interpretability remains limited. To address these issues, we developed a SUbgraph BAsed mulTi-OMIcs Clustering framework (SUBATOMIC), which infers small and interpretable modules with a specific topology while keeping track of connections to other modules and regulators. RESULTS: SUBATOMIC groups specific molecular interactions in composite network subgraphs of two and three nodes and clusters them into topological modules. These are functionally annotated, visualized and overlaid with expression profiles to go from static to dynamic modules. To preserve the larger network context, SUBATOMIC investigates statistically the connections in between modules as well as between modules and regulators such as miRNAs and transcription factors. We applied SUBATOMIC to analyze a composite Homo sapiens network containing transcription factor-target gene, miRNA-target gene, protein-protein, homologous and co-functional interactions from different databases. We derived and annotated 5586 modules with diverse topological, functional and regulatory properties. We created novel functional hypotheses for unannotated genes. Furthermore, we integrated modules with condition specific expression data to study the influence of hypoxia in three cancer cell lines. We developed two prioritization strategies to identify the most relevant modules in specific biological contexts: one considering GO term enrichments and one calculating an activity score reflecting the degree of differential expression. Both strategies yielded modules specifically reacting to low oxygen levels. CONCLUSIONS: We developed the SUBATOMIC framework that generates interpretable modules from integrated multi-omics networks and applied it to hypoxia in cancer. SUBATOMIC can infer and contextualize modules, explore condition or disease specific modules, identify regulators and functionally related modules, and derive novel gene functions for uncharacterized genes. The software is available at https://github.com/CBIGR/SUBATOMIC .

RRM2 enhances MYCN-driven neuroblastoma formation and acts as a synergistic target with CHK1 inhibition.

High-risk neuroblastoma, a pediatric tumor originating from the sympathetic nervous system, has a low mutation load but highly recurrent somatic DNA copy number variants. Previously, segmental gains and/or amplifications allowed identification of drivers for neuroblastoma development. Using this approach, combined with gene dosage impact on expression and survival, we identified ribonucleotide reductase subunit M2 (RRM2) as a candidate dependency factor further supported by growth inhibition upon in vitro knockdown and accelerated tumor formation in a neuroblastoma zebrafish model coexpressing human RRM2 with MYCN. Forced RRM2 induction alleviates excessive replicative stress induced by CHK1 inhibition, while high RRM2 expression in human neuroblastomas correlates with high CHK1 activity. MYCN-driven zebrafish tumors with RRM2 co-overexpression exhibit differentially expressed DNA repair genes in keeping with enhanced ATR-CHK1 signaling activity. In vitro, RRM2 inhibition enhances intrinsic replication stress checkpoint addiction. Last, combinatorial RRM2-CHK1 inhibition acts synergistic in high-risk neuroblastoma cell lines and patient-derived xenograft models, illustrating the therapeutic potential.

Nearby transposable elements impact plant stress gene regulatory networks: a meta-analysis in A. thaliana and S. lycopersicum.

BACKGROUND: Transposable elements (TE) make up a large portion of many plant genomes and are playing innovative roles in genome evolution. Several TEs can contribute to gene regulation by influencing expression of nearby genes as stress-responsive regulatory motifs. To delineate TE-mediated plant stress regulatory networks, we took a 2-step computational approach consisting of identifying TEs in the proximity of stress-responsive genes, followed by searching for cis-regulatory motifs in these TE sequences and linking them to known regulatory factors. Through a systematic meta-analysis of RNA-seq expression profiles and genome annotations, we investigated the relation between the presence of TE superfamilies upstream, downstream or within introns of nearby genes and the differential expression of these genes in various stress conditions in the TE-poor Arabidopsis thaliana and the TE-rich Solanum lycopersicum. RESULTS: We found that stress conditions frequently expressed genes having members of various TE superfamilies in their genomic proximity, such as SINE upon proteotoxic stress and Copia and Gypsy upon heat stress in A. thaliana, and EPRV and hAT upon infection, and Harbinger, LINE and Retrotransposon upon light stress in S. lycopersicum. These stress-specific gene-proximal TEs were mostly located within introns and more detected near upregulated than downregulated genes. Similar stress conditions were often related to the same TE superfamily. Additionally, we detected both novel and known motifs in the sequences of those TEs pointing to regulatory cooption of these TEs upon stress. Next, we constructed the regulatory network of TFs that act through binding these TEs to their target genes upon stress and discovered TE-mediated regulons targeted by TFs such as BRB/BPC, HD, HSF, GATA, NAC, DREB/CBF and MYB factors in Arabidopsis and AP2/ERF/B3, NAC, NF-Y, MYB, CXC and HD factors in tomato. CONCLUSIONS: Overall, we map TE-mediated plant stress regulatory networks using numerous stress expression profile studies for two contrasting plant species to study the regulatory role TEs play in the response to stress. As TE-mediated gene regulation allows plants to adapt more rapidly to new environmental conditions, this study contributes to the future development of climate-resilient plants.

From DNA Copy Number Gains and Tumor Dependencies to Novel Therapeutic Targets for High-Risk Neuroblastoma.

Neuroblastoma is a pediatric tumor arising from the sympatho-adrenal lineage and a worldwide leading cause of childhood cancer-related deaths. About half of high-risk patients die from the disease while survivors suffer from multiple therapy-related side-effects. While neuroblastomas present with a low mutational burden, focal and large segmental DNA copy number aberrations are highly recurrent and associated with poor survival. It can be assumed that the affected chromosomal regions contain critical genes implicated in neuroblastoma biology and behavior. More specifically, evidence has emerged that several of these genes are implicated in tumor dependencies thus potentially providing novel therapeutic entry points. In this review, we briefly review the current status of recurrent DNA copy number aberrations in neuroblastoma and provide an overview of the genes affected by these genomic variants for which a direct role in neuroblastoma has been established. Several of these genes are implicated in networks that positively regulate MYCN expression or stability as well as cell cycle control and apoptosis. Finally, we summarize alternative approaches to identify and prioritize candidate copy-number driven dependency genes for neuroblastoma offering novel therapeutic opportunities.

Function, dynamics and evolution of network motif modules in integrated gene regulatory networks of worm and plant.

Gene regulatory networks (GRNs) consist of different molecular interactions that closely work together to establish proper gene expression in time and space. Especially in higher eukaryotes, many questions remain on how these interactions collectively coordinate gene regulation. We study high quality GRNs consisting of undirected protein-protein, genetic and homologous interactions, and directed protein-DNA, regulatory and miRNA-mRNA interactions in the worm Caenorhabditis elegans and the plant Arabidopsis thaliana. Our data-integration framework integrates interactions in composite network motifs, clusters these in biologically relevant, higher-order topological network motif modules, overlays these with gene expression profiles and discovers novel connections between modules and regulators. Similar modules exist in the integrated GRNs of worm and plant. We show how experimental or computational methodologies underlying a certain data type impact network topology. Through phylogenetic decomposition, we found that proteins of worm and plant tend to functionally interact with proteins of a similar age, while at the regulatory level TFs favor same age, but also older target genes. Despite some influence of the duplication mode difference, we also observe at the motif and module level for both species a preference for age homogeneity for undirected and age heterogeneity for directed interactions. This leads to a model where novel genes are added together to the GRNs in a specific biological functional context, regulated by one or more TFs that also target older genes in the GRNs. Overall, we detected topological, functional and evolutionary properties of GRNs that are potentially universal in all species.

Reciprocal responses in the interaction between Arabidopsis and the cell-content-feeding chelicerate herbivore spider mite.

Most molecular-genetic studies of plant defense responses to arthropod herbivores have focused on insects. However, plant-feeding mites are also pests of diverse plants, and mites induce different patterns of damage to plant tissues than do well-studied insects (e.g. lepidopteran larvae or aphids). The two-spotted spider mite (Tetranychus urticae) is among the most significant mite pests in agriculture, feeding on a staggering number of plant hosts. To understand the interactions between spider mite and a plant at the molecular level, we examined reciprocal genome-wide responses of mites and its host Arabidopsis (Arabidopsis thaliana). Despite differences in feeding guilds, we found that transcriptional responses of Arabidopsis to mite herbivory resembled those observed for lepidopteran herbivores. Mutant analysis of induced plant defense pathways showed functionally that only a subset of induced programs, including jasmonic acid signaling and biosynthesis of indole glucosinolates, are central to Arabidopsis's defense to mite herbivory. On the herbivore side, indole glucosinolates dramatically increased mite mortality and development times. We identified an indole glucosinolate dose-dependent increase in the number of differentially expressed mite genes belonging to pathways associated with detoxification of xenobiotics. This demonstrates that spider mite is sensitive to Arabidopsis defenses that have also been associated with the deterrence of insect herbivores that are very distantly related to chelicerates. Our findings provide molecular insights into the nature of, and response to, herbivory for a representative of a major class of arthropod herbivores.

Arabidopsis ensemble reverse-engineered gene regulatory network discloses interconnected transcription factors in oxidative stress.

The abiotic stress response in plants is complex and tightly controlled by gene regulation. We present an abiotic stress gene regulatory network of 200,014 interactions for 11,938 target genes by integrating four complementary reverse-engineering solutions through average rank aggregation on an Arabidopsis thaliana microarray expression compendium. This ensemble performed the most robustly in benchmarking and greatly expands upon the availability of interactions currently reported. Besides recovering 1182 known regulatory interactions, cis-regulatory motifs and coherent functionalities of target genes corresponded with the predicted transcription factors. We provide a valuable resource of 572 abiotic stress modules of coregulated genes with functional and regulatory information, from which we deduced functional relationships for 1966 uncharacterized genes and many regulators. Using gain- and loss-of-function mutants of seven transcription factors grown under control and salt stress conditions, we experimentally validated 141 out of 271 predictions (52% precision) for 102 selected genes and mapped 148 additional transcription factor-gene regulatory interactions (49% recall). We identified an intricate core oxidative stress regulatory network where NAC13, NAC053, ERF6, WRKY6, and NAC032 transcription factors interconnect and function in detoxification. Our work shows that ensemble reverse-engineering can generate robust biological hypotheses of gene regulation in a multicellular eukaryote that can be tested by medium-throughput experimental validation.

The membrane-bound NAC transcription factor ANAC013 functions in mitochondrial retrograde regulation of the oxidative stress response in Arabidopsis.

Upon disturbance of their function by stress, mitochondria can signal to the nucleus to steer the expression of responsive genes. This mitochondria-to-nucleus communication is often referred to as mitochondrial retrograde regulation (MRR). Although reactive oxygen species and calcium are likely candidate signaling molecules for MRR, the protein signaling components in plants remain largely unknown. Through meta-analysis of transcriptome data, we detected a set of genes that are common and robust targets of MRR and used them as a bait to identify its transcriptional regulators. In the upstream regions of these mitochondrial dysfunction stimulon (MDS) genes, we found a cis-regulatory element, the mitochondrial dysfunction motif (MDM), which is necessary and sufficient for gene expression under various mitochondrial perturbation conditions. Yeast one-hybrid analysis and electrophoretic mobility shift assays revealed that the transmembrane domain-containing no apical meristem/Arabidopsis transcription activation factor/cup-shaped cotyledon transcription factors (ANAC013, ANAC016, ANAC017, ANAC053, and ANAC078) bound to the MDM cis-regulatory element. We demonstrate that ANAC013 mediates MRR-induced expression of the MDS genes by direct interaction with the MDM cis-regulatory element and triggers increased oxidative stress tolerance. In conclusion, we characterized ANAC013 as a regulator of MRR upon stress in Arabidopsis thaliana.

Identification of cis-regulatory elements specific for different types of reactive oxygen species in Arabidopsis thaliana.

The type of reactive oxygen species (ROS) is a major factor that determines the specificity of biological responses. These responses may be elicited by activation of transcription factors that recognize ROS-specific cis-regulatory elements in target genes. In search for Arabidopsis promoter motifs specific for particular types of ROS, genome-wide microarray expression profiles for 283 abiotic stress-related conditions were subjected to cluster analysis to identify gene groups induced by singlet oxygen, superoxide radicals, and H(2)O(2). Promoters of these gene groups were analyzed to identify cis-regulatory elements that are associated with specific types of ROS. Eleven ROS-specific de novo identified elements, seven known promoter motifs and several sequences enriched in ROS-responsive clusters but lacking in specificity are reported. The conservation of the identified motifs was determined in orthologous genes in C. papaya, V. vinifera and P. trichocarpa. Finally, biological functions were attributed to the motifs by calculation of GO-term enrichment for genes with conserved ROS-responsive elements.

Transcription regulatory networks in Caenorhabditis elegans inferred through reverse-engineering of gene expression profiles constitute biological hypotheses for metazoan development.

Differential gene expression governs the development, function and pathology of multicellular organisms. Transcription regulatory networks study differential gene expression at a systems level by mapping the interactions between regulatory proteins and target genes. While microarray transcription profiles are the most abundant data for gene expression, it remains challenging to correctly infer the underlying transcription regulatory networks. The reverse-engineering algorithm LeMoNe (learning module networks) uses gene expression profiles to extract ensemble transcription regulatory networks of coexpression modules and their prioritized regulators. Here we apply LeMoNe to a compendium of microarray studies of the worm Caenorhabditis elegans. We obtain 248 modules with a regulation program for 5020 genes and 426 regulators and a total of 24 012 predicted transcription regulatory interactions. Through GO enrichment analysis, comparison with the gene-gene association network WormNet and integration of other biological data, we show that LeMoNe identifies functionally coherent coexpression modules and prioritizes regulators that relate to similar biological processes as the module genes. Furthermore, we can predict new functional relationships for uncharacterized genes and regulators. Based on modules involved in molting, meiosis and oogenesis, ciliated sensory neurons and mitochondrial metabolism, we illustrate the value of LeMoNe as a biological hypothesis generator for differential gene expression in greater detail. In conclusion, through reverse-engineering of C. elegans expression data, we obtained transcription regulatory networks that can provide further insight into metazoan development.

Matrix and Steiner-triple-system smart pooling assays for high-performance transcription regulatory network mapping.

Yeast one-hybrid (Y1H) assays provide a gene-centered method for the identification of interactions between gene promoters and regulatory transcription factors (TFs). To date, Y1H assays have involved library screens that are relatively expensive and laborious. We present two Y1H strategies that allow immediate prey identification: matrix assays that use an array of 755 individual Caenorhabditis elegans TFs, and smart-pool assays that use TF multiplexing. Both strategies simplify the Y1H pipeline and reduce the cost of protein-DNA interaction identification. We used a Steiner triple system (STS) to create smart pools of 4-25 TFs. Notably, we uniplexed a small number of highly connected TFs to allow efficient assay deconvolution. Both strategies outperform library screens in terms of coverage, confidence and throughput. These versatile strategies can be adapted both to TFs in other systems and, likely, to other biomolecules and assays as well.

Transcription factor modularity in a gene-centered C. elegans core neuronal protein-DNA interaction network.

Transcription regulatory networks play a pivotal role in the development, function, and pathology of metazoan organisms. Such networks are comprised of protein-DNA interactions between transcription factors (TFs) and their target genes. An important question pertains to how the architecture of such networks relates to network functionality. Here, we show that a Caenorhabditis elegans core neuronal protein-DNA interaction network is organized into two TF modules. These modules contain TFs that bind to a relatively small number of target genes and are more systems specific than the TF hubs that connect the modules. Each module relates to different functional aspects of the network. One module contains TFs involved in reproduction and target genes that are expressed in neurons as well as in other tissues. The second module is enriched for paired homeodomain TFs and connects to target genes that are often exclusively neuronal. We find that paired homeodomain TFs are specifically expressed in C. elegans and mouse neurons, indicating that the neuronal function of paired homeodomains is evolutionarily conserved. Taken together, we show that a core neuronal C. elegans protein-DNA interaction network possesses TF modules that relate to different functional aspects of the complete network.