Major ß cell-specific functions of NKX2.2 are mediated via the NK2-specific domain.

The consolidation of unambiguous cell fate commitment relies on the ability of transcription factors (TFs) to exert tissue-specific regulation of complex genetic networks. However, the mechanisms by which TFs establish such precise control over gene expression have remained elusive-especially in instances in which a single TF operates in two or more discrete cellular systems. In this study, we demonstrate that ß cell-specific functions of NKX2.2 are driven by the highly conserved NK2-specific domain (SD). Mutation of the endogenous NKX2.2 SD prevents the developmental progression of ß cell precursors into mature, insulin-expressing ß cells, resulting in overt neonatal diabetes. Within the adult ß cell, the SD stimulates ß cell performance through the activation and repression of a subset of NKX2.2-regulated transcripts critical for ß cell function. These irregularities in ß cell gene expression may be mediated via SD-contingent interactions with components of chromatin remodelers and the nuclear pore complex. However, in stark contrast to these pancreatic phenotypes, the SD is entirely dispensable for the development of NKX2.2-dependent cell types within the CNS. Together, these results reveal a previously undetermined mechanism through which NKX2.2 directs disparate transcriptional programs in the pancreas versus neuroepithelium.

JMnorm: a novel joint multi-feature normalization method for integrative and comparative epigenomics.

Combinatorial patterns of epigenetic features reflect transcriptional states and functions of genomic regions. While many epigenetic features have correlated relationships, most existing data normalization approaches analyze each feature independently. Such strategies may distort relationships between functionally correlated epigenetic features and hinder biological interpretation. We present a novel approach named JMnorm that simultaneously normalizes multiple epigenetic features across cell types, species, and experimental conditions by leveraging information from partially correlated epigenetic features. We demonstrate that JMnorm-normalized data can better preserve cross-epigenetic-feature correlations across different cell types and enhance consistency between biological replicates than data normalized by other methods. Additionally, we show that JMnorm-normalized data can consistently improve the performance of various downstream analyses, which include candidate cis-regulatory element clustering, cross-cell-type gene expression prediction, detection of transcription factor binding and changes upon perturbations. These findings suggest that JMnorm effectively minimizes technical noise while preserving true biologically significant relationships between epigenetic datasets. We anticipate that JMnorm will enhance integrative and comparative epigenomics.

Facile Uniaxial Electrospinning Strategy To Embed CsPbBr3 Nanocrystals with Enhanced Water/Thermal Stabilities for Reversible Fluorescence Switches.

All-inorganic halide perovskite nanocrystals with their fascinating optical properties have drawn increasing attention as promising nanoemitters. However, due to the intrinsic poor colloidal stability against the external environment, the practical applications are greatly limited. Herein, a facile and effective strategy for the in situ encapsulation of CsPbBr3 NCs into highly dense multichannel polyacrylonitrile (PAN) nanofibers via a uniaxial electrospinning strategy is presented. Such a facile uniaxial electrospinning strategy enables the in situ formation of CsPbBr3 NCs in PAN nanofibers without the introduction of stabilizers. Significantly, the obtained CsPbBr3 nanofibers not only display intense fluorescence with a high quantum yield (≈48%) but also present high stability when exposed to water and air owing to the peripheral protecting matrix of PAN. After immersing CsPbBr3@PAN nanofiber films in water for 100 days, the quantum yield of CsPbBr3@PAN nanofibers maintained 87.5% of the original value, which was much higher than that using CsPbBr3 NCs. Furthermore, based on the spectral overlap between the electrochromic material of ruthenium purple and fluorescence of CsPbBr3@PAN nanofiber films with excellent water stability, a reversible fluorescence switch is constructed with good fatigue resistance, suggesting their promising applications.

Integration of transcriptomic and proteomic analyses reveals several levels of metabolic regulation in the excess starch and early senescent leaf mutant lses1 in rice.

BACKGROUND: The normal metabolism of transitory starch in leaves plays an important role in ensuring photosynthesis, delaying senescence and maintaining high yield in crops. OsCKI1 (casein kinase I1) plays crucial regulatory roles in multiple important physiological processes, including root development, hormonal signaling and low temperature-treatment adaptive growth in rice; however, its potential role in regulating temporary starch metabolism or premature leaf senescence remains unclear. To reveal the molecular regulatory mechanism of OsCKI1 in rice leaves, physiological, transcriptomic and proteomic analyses of leaves of osckI1 allele mutant lses1 (leaf starch excess and senescence 1) and its wild-type varieties (WT) were performed. RESULTS: Phenotypic identification and physiological measurements showed that the lses1 mutant exhibited starch excess in the leaves and an obvious leaf tip withering phenotype as well as high ROS and MDA contents, low chlorophyll content and protective enzyme activities compared to WT. The correlation analyses between protein and mRNA abundance are weak or limited. However, the changes of several important genes related to carbohydrate metabolism and apoptosis at the mRNA and protein levels were consistent. The protein-protein interaction (PPI) network might play accessory roles in promoting premature senescence of lses1 leaves. Comprehensive transcriptomic and proteomic analysis indicated that multiple key genes/proteins related to starch and sugar metabolism, apoptosis and ABA signaling exhibited significant differential expression. Abnormal increase in temporary starch was highly correlated with the expression of starch biosynthesis-related genes, which might be the main factor that causes premature leaf senescence and changes in multiple metabolic levels in leaves of lses1. In addition, four proteins associated with ABA accumulation and signaling, and three CKI potential target proteins related to starch biosynthesis were up-regulated in the lses1 mutant, suggesting that LSES1 may affect temporary starch accumulation and premature leaf senescence through phosphorylation crosstalk ABA signaling and starch anabolic pathways. CONCLUSION: The current study established the high correlation between the changes in physiological characteristics and mRNA and protein expression profiles in lses1 leaves, and emphasized the positive effect of excessive starch on accelerating premature leaf senescence. The expression patterns of genes/proteins related to starch biosynthesis and ABA signaling were analyzed via transcriptomes and proteomes, which provided a novel direction and research basis for the subsequent exploration of the regulation mechanism of temporary starch and apoptosis via LSES1/OsCKI1 in rice.

IDR2D identifies reproducible genomic interactions.

Chromatin interaction data from protocols such as ChIA-PET, HiChIP and Hi-C provide valuable insights into genome organization and gene regulation, but can include spurious interactions that do not reflect underlying genome biology. We introduce an extension of the Irreproducible Discovery Rate (IDR) method called IDR2D that identifies replicable interactions shared by chromatin interaction experiments. IDR2D provides a principled set of interactions and eliminates artifacts from single experiments. The method is available as a Bioconductor package for the R community, as well as an online service at https://idr2d.mit.edu.

A novel k-mer set memory (KSM) motif representation improves regulatory variant prediction.

The representation and discovery of transcription factor (TF) sequence binding specificities is critical for understanding gene regulatory networks and interpreting the impact of disease-associated noncoding genetic variants. We present a novel TF binding motif representation, the k-mer set memory (KSM), which consists of a set of aligned k-mers that are overrepresented at TF binding sites, and a new method called KMAC for de novo discovery of KSMs. We find that KSMs more accurately predict in vivo binding sites than position weight matrix (PWM) models and other more complex motif models across a large set of ChIP-seq experiments. Furthermore, KSMs outperform PWMs and more complex motif models in predicting in vitro binding sites. KMAC also identifies correct motifs in more experiments than five state-of-the-art motif discovery methods. In addition, KSM-derived features outperform both PWM and deep learning model derived sequence features in predicting differential regulatory activities of expression quantitative trait loci (eQTL) alleles. Finally, we have applied KMAC to 1600 ENCODE TF ChIP-seq data sets and created a public resource of KSM and PWM motifs. We expect that the KSM representation and KMAC method will be valuable in characterizing TF binding specificities and in interpreting the effects of noncoding genetic variations.

Regulating Catalytic Activity of DNA-Templated Silver Nanoclusters Based on their Differential Interactions with DNA Structures and Stimuli-Responsive Structural Transition.

This work reports exquisite engineering of catalytic activity of DNA-templated silver nanoclusters (DNA-AgNCs) based on unique adsorption phenomena of DNAs on DNA-AgNCs and reversible transition between double and triple-stranded DNAs. Four DNA homopolymers exhibit different inhibition effects on the catalytic activity of DNA-AgNCs, poly adenine (polyA) > poly guanine (polyG) > poly cytosine (polyC) > poly thymine (polyT), demonstrating that polyA strands have the strongest adsorption affinity on DNA-AgNCs. Through the formation of T-A•T triplex DNAs, catalytic activity of DNA-AgNCs is restored from the deactivated state by double or single-stranded DNAs, indicating the participation of N7 groups of adenine bases in binding to DNA-AgNCs and blocking active sites. Accordingly, reversibly regulating catalytic activity of DNA-AgNCs can be realized based on DNA input-stimulated transition between duplex and triplex structures. In the end, two low-cost and facile biosensing methods are presented, which are derived from the activity-switchable platform. It is worthy to anticipate that the DNA-AgNCs with controlled catalytic activity will inspire researchers to devise more functionalized nanocatalysts and contribute to the exploration of intelligent biomedicine in the future.

Exploration of intramolecular split G-quadruplex and its analytical applications.

Distinct from intermolecular split G-quadruplex (Inter-SG), intramolecular split G-quadruplex (Intra-SG) which could be generated in a DNA spacer-inserted G-quadruplex strand has not been systematically explored. Not only is it essential for the purpose of simplicity of DNA-based bioanalytical applications, but also it will give us hints how to design split G-quadruplex-based system. Herein, comprehensive information is provided about influences of spacer length and split mode on the formation of Intra-SG, how to adjust its thermodynamic stability, and selection of optimal Intra-SG for bioanalysis. For instances, non-classical Intra-SG (e.g. 2:10, 4:8 and 5:7) displays lower stability than classical split strands (3:9, 6:6 and 9:3), which is closely related to integrity of consecutive guanine tract; as compared to regular Intra-SG structures, single-thymine capped ones have reduced melting temperature, providing an effective approach to adjustment of stability. It is believed that the disclosed rules in this study will contribute to the effective application of split G-quadruplex in the field of DNA technology in the future.

High resolution discovery of chromatin interactions.

Chromatin interaction analysis by paired-end tag sequencing (ChIA-PET) is a method for the genome-wide de novo discovery of chromatin interactions. Existing computational methods typically fail to detect weak or dynamic interactions because they use a peak-calling step that ignores paired-end linkage information. We have developed a novel computational method called Chromatin Interaction Discovery (CID) to overcome this limitation with an unbiased clustering approach for interaction discovery. CID outperforms existing chromatin interaction detection methods with improved sensitivity, replicate consistency, and concordance with other chromatin interaction datasets. In addition, CID also outperforms other methods in discovering chromatin interactions from HiChIP data. We expect that the CID method will be valuable in characterizing 3D chromatin interactions and in understanding the functional consequences of disease-associated distal genetic variations.

Identification of new branch points and unconventional introns in Saccharomyces cerevisiae.

Spliced messages constitute one-fourth of expressed mRNAs in the yeast Saccharomyces cerevisiae, and most mRNAs in metazoans. Splicing requires 5' splice site (5'SS), branch point (BP), and 3' splice site (3'SS) elements, but the role of the BP in splicing control is poorly understood because BP identification remains difficult. We developed a high-throughput method, Branch-seq, to map BPs and 5'SSs of isolated RNA lariats. Applied to S. cerevisiae, Branch-seq detected 76% of expressed, annotated BPs and identified a comparable number of novel BPs. We performed RNA-seq to confirm associated 3'SS locations, identifying some 200 novel splice junctions, including an AT-AC intron. We show that several yeast introns use two or even three different BPs, with effects on 3'SS choice, protein coding potential, or RNA stability, and identify novel introns whose splicing changes during meiosis or in response to stress. Together, these findings show unanticipated complexity of splicing in yeast.

Accurate eQTL prioritization with an ensemble-based framework.

We present a novel ensemble-based computational framework, EnsembleExpr, that achieved the best performance in the Fourth Critical Assessment of Genome Interpretation expression quantitative trait locus "(eQTL)-causal SNPs" challenge for identifying eQTLs and prioritizing their gene expression effects. eQTLs are genome sequence variants that result in gene expression changes and are thus prime suspects in the search for contributions to the causality of complex traits. When EnsembleExpr is trained on data from massively parallel reporter assays, it accurately predicts reporter expression levels from unseen regulatory sequences and identifies sequence variants that exhibit significant changes in reporter expression. Compared with other state-of-the-art methods, EnsembleExpr achieved competitive performance when applied on eQTL datasets determined by other protocols. We envision EnsembleExpr to be a resource to help interpret noncoding regulatory variants and prioritize disease-associated mutations for downstream validation.

Predicting gene expression in massively parallel reporter assays: A comparative study.

In many human diseases, associated genetic changes tend to occur within noncoding regions, whose effect might be related to transcriptional control. A central goal in human genetics is to understand the function of such noncoding regions: given a region that is statistically associated with changes in gene expression (expression quantitative trait locus [eQTL]), does it in fact play a regulatory role? And if so, how is this role "coded" in its sequence? These questions were the subject of the Critical Assessment of Genome Interpretation eQTL challenge. Participants were given a set of sequences that flank eQTLs in humans and were asked to predict whether these are capable of regulating transcription (as evaluated by massively parallel reporter assays), and whether this capability changes between alternative alleles. Here, we report lessons learned from this community effort. By inspecting predictive properties in isolation, and conducting meta-analysis over the competing methods, we find that using chromatin accessibility and transcription factor binding as features in an ensemble of classifiers or regression models leads to the most accurate results. We then characterize the loci that are harder to predict, putting the spotlight on areas of weakness, which we expect to be the subject of future studies.

Modular combinatorial binding among human trans-acting factors reveals direct and indirect factor binding.

BACKGROUND: The combinatorial binding of trans-acting factors (TFs) to the DNA is critical to the spatial and temporal specificity of gene regulation. For certain regulatory regions, more than one regulatory module (set of TFs that bind together) are combined to achieve context-specific gene regulation. However, previous approaches are limited to either pairwise TF co-association analysis or assuming that only one module is used in each regulatory region. RESULTS: We present a new computational approach that models the modular organization of TF combinatorial binding. Our method learns compact and coherent regulatory modules from in vivo binding data using a topic model. We found that the binding of 115 TFs in K562 cells can be organized into 49 interpretable modules. Furthermore, we found that tens of thousands of regulatory regions use multiple modules, a structure that cannot be observed with previous hard clustering based methods. The modules discovered recapitulate many published protein-protein physical interactions, have consistent functional annotations of chromatin states, and uncover context specific co-binding such as gene proximal binding of NFY + FOS + SP and distal binding of NFY + FOS + USF. For certain TFs, the co-binding partners of direct binding (motif present) differs from those of indirect binding (motif absent); the distinct set of co-binding partners can predict whether the TF binds directly or indirectly with up to 95% accuracy. Joint analysis across two cell types reveals both cell-type-specific and shared regulatory modules. CONCLUSIONS: Our results provide comprehensive cell-type-specific combinatorial binding maps and suggest a modular organization of combinatorial binding.

High resolution genome wide binding event finding and motif discovery reveals transcription factor spatial binding constraints.

An essential component of genome function is the syntax of genomic regulatory elements that determine how diverse transcription factors interact to orchestrate a program of regulatory control. A precise characterization of in vivo spacing constraints between key transcription factors would reveal key aspects of this genomic regulatory language. To discover novel transcription factor spatial binding constraints in vivo, we developed a new integrative computational method, genome wide event finding and motif discovery (GEM). GEM resolves ChIP data into explanatory motifs and binding events at high spatial resolution by linking binding event discovery and motif discovery with positional priors in the context of a generative probabilistic model of ChIP data and genome sequence. GEM analysis of 63 transcription factors in 214 ENCODE human ChIP-Seq experiments recovers more known factor motifs than other contemporary methods, and discovers six new motifs for factors with unknown binding specificity. GEM's adaptive learning of binding-event read distributions allows it to further improve upon previous methods for processing ChIP-Seq and ChIP-exo data to yield unsurpassed spatial resolution and discovery of closely spaced binding events of the same factor. In a systematic analysis of in vivo sequence-specific transcription factor binding using GEM, we have found hundreds of spatial binding constraints between factors. GEM found 37 examples of factor binding constraints in mouse ES cells, including strong distance-specific constraints between Klf4 and other key regulatory factors. In human ENCODE data, GEM found 390 examples of spatially constrained pair-wise binding, including such novel pairs as c-Fos:c-Jun/USF1, CTCF/Egr1, and HNF4A/FOXA1. The discovery of new factor-factor spatial constraints in ChIP data is significant because it proposes testable models for regulatory factor interactions that will help elucidate genome function and the implementation of combinatorial control.

Rice OsCASP1 orchestrates Casparian strip formation and suberin deposition in small lateral roots to maintain nutrient homeostasis.

Arabidopsis Casparian strip membrane domain proteins (CASPs) form a transmembrane scaffold to recruit lignin biosynthetic enzymes for Casparian strip (CS) formation. Rice is a semi-aquatic plant with a more complex root structure than Arabidopsis to adapt its growing conditions, where the different deposition of lignin and suberin is crucial for adaptive responses. Here, we observed the structure of rice primary and small lateral roots (SLRs), particularly the deposition patterns of lignin and suberin in wild type and Oscasp1 mutants. We found that the appearance time and structure of CS in the roots of rice are different from those of Arabidopsis and observed suberin deposition in the sclerenchyma in wild type roots. Rice CASP1 is highly similar to AtCASPs, but its expression is concentrated in SLR tips and can be induced by salt stress especially in the steles. The loss of OsCASP1 function alters the expression of the genes involved in suberin biosynthesis and the deposition of suberin in the endodermis and sclerenchyma and leads to delayed CS formation and uneven lignin deposition in SLRs. These different depositions may alter nutrient uptake, resulting in ion imbalance in plant, withered leaves, fewer tillers, and reduced tolerance to salt stress. Our findings suggest that OsCASP1 could play an important role in nutrient homeostasis and adaptation to the growth environment.

An expansion of the non-coding genome and its regulatory potential underlies vertebrate neuronal diversity.

Proper assembly and function of the nervous system requires the generation of a uniquely diverse population of neurons expressing a cell-type-specific combination of effector genes that collectively define neuronal morphology, connectivity, and function. How countless partially overlapping but cell-type-specific patterns of gene expression are controlled at the genomic level remains poorly understood. Here we show that neuronal genes are associated with highly complex gene regulatory systems composed of independent cell-type- and cell-stage-specific regulatory elements that reside in expanded non-coding genomic domains. Mapping enhancer-promoter interactions revealed that motor neuron enhancers are broadly distributed across the large chromatin domains. This distributed regulatory architecture is not a unique property of motor neurons but is employed throughout the nervous system. The number of regulatory elements increased dramatically during the transition from invertebrates to vertebrates, suggesting that acquisition of new enhancers might be a fundamental process underlying the evolutionary increase in cellular complexity.

Discovering homotypic binding events at high spatial resolution.

MOTIVATION: Clusters of protein-DNA interaction events involving the same transcription factor are known to act as key components of invertebrate and mammalian promoters and enhancers. However, detecting closely spaced homotypic events from ChIP-Seq data is challenging because random variation in the ChIP fragmentation process obscures event locations. RESULTS: The Genome Positioning System (GPS) can predict protein-DNA interaction events at high spatial resolution from ChIP-Seq data, while retaining the ability to resolve closely spaced events that appear as a single cluster of reads. GPS models observed reads using a complexity penalized mixture model and efficiently predicts event locations with a segmented EM algorithm. An optional mode permits GPS to align common events across distinct experiments. GPS detects more joint events in synthetic and actual ChIP-Seq data and has superior spatial resolution when compared with other methods. In addition, the specificity and sensitivity of GPS are superior to or comparable with other methods. AVAILABILITY: http://cgs.csail.mit.edu/gps.

Recent advances in chitosan-based layer-by-layer biomaterials and their biomedical applications.

In recent years, chitosan-based biomaterials have been continually and extensively researched by using layer-by-layer (LBL) assembly, due to their potentials in biomedicine. Various chitosan-based LBL materials have been newly developed and applied in different areas along with the development of technologies. This work reviews the recent advances of chitosan-based biomaterials produced by LBL assembly. Driving forces of LBL, for example electrostatic interactions, hydrogen bond as well as Schiff base linkage have been discussed. Various forms of chitosan-based LBL materials such as films/coatings, capsules and fibers have been reviewed. The applications of these biomaterials in the field of antimicrobial applications, drug delivery, wound dressings and tissue engineering have been comprehensively reviewed.

Illuminating Diverse Concomitant DNA Logic Gates and Concatenated Circuits with Hairpin DNA-Templated Silver Nanoclusters as Universal Dual-Output Generators.

Exquisite administration of a new type of hairpin DNA-templated silver nanoclusters (H-AgNCs) as universal dual-output generators in DNA-based logic systems is reported. Diverse concomitant contrary logic gates (CCLGs) with opposite functions (YES^ NOT, OR^ NOR, INHIBIT^ IMPLICATION, XOR^ XNOR, and MAJORITY^ MINORITY) and extended concatenated logic circuits are presented and some of them perform specific functions, such as parity generators and checkers. The introduction of H-AgNCs as noncovalent signal reporters avoids tedious and high-cost labeling procedures. Of note, the concomitant feature of CCLGs attributed to the dual-emitter AgNCs conduces to reducing the time and cost to devise multiple logic gates. As compared to previous ones, this design eliminates numerous substances (e.g., organic dyes) and unstable components (hydrogen peroxide), which not only decreases the complexity of logic performs and improves repeatability of operation, but also makes it convenient to connect distinct DNA-based logic gates. It is worthy to anticipate that the cost-effective strategy will inspire researchers to develop much more complex logic systems and contribute to the field of molecular computing.

Recent amplification of Osr4 LTR-retrotransposon caused rice D1 gene mutation and dwarf phenotype.

A novel rice d1 mutant was identified using map-based cloning and comparative analysis of known d1 mutants. The mutant (d1-a) shows a mild dwarf trait, which differs only slightly from the wildtype in plant height at the tillering stage. The d1-a mutant is different from other d1 mutants. We found that it was interrupted by an Osr4 long terminal repeat (LTR)-retrotransposon, which resulted in the loss of exon 7 in the mutant D1 mRNA. A paralog of the D1 gene, D1-like, was revealed. D1-like is a truncated gene that might have resulted from recombination between retrotransposons. We identified 65 Osr4 LTR-retrotransposons in Nipponbare, and found more LTR variants in contrast to coding DNA sequence (CDS) in the retrotransposons. We also identified five possible regulatory motifs in LTRs which may control the expression of the retrotransposons. In addition, we predicted six putative functional Osr4 retrotransposons that contain complete CDSs and all important elements. Osr4 retrotransposons were classified into 4 groups, and this type of retrotransposon only appears to be present in monocots. Members of group I-1, which included all putative functional retrotransposons, showed a high similarity with each other. The retrotransposons were expressed in all tissues, at especially higher levels in some leaves and seeds. These findings imply that transpositions of group I-1 members might have occurred frequently and recently.