Transposable elements contribute to tissue-specific gene regulation in humans.

BACKGROUND: Transposable elements (TEs) contribute to approximately half of the human genome, and along with many other functions, they have been known to play a role in gene regulation in the genome. With TEs' active/repressed states varying across tissue and cell types, they have the potential to regulate gene expression in a tissue-specific manner. OBJECTIVE AND METHODS: To provide a systematic analysis of TEs' contribution in tissue-specific gene regulation, we examined the regulatory elements and genes in association with TE-derived regulatory sequences in 14 human cell lines belonging to 10 different tissue types using the functional genomics data from the ENCODE project. Specifically, we separately analyzed regulatory regions identified by three different approaches (DNase hypersensitive sites (DHS), histone active sites (HA), and histone repressive sites (HR)). RESULTS: These regulatory regions showed to be distinct from each other by sharing less than 2.5% among all three types and more than 95% showed to be cell line-specific. Despite a lower total TE content overall than the genome average, each regulatory sequence type showed enrichment for one or two specific TE type(s): DHS for long terminal repeats (LTRs) and DNA transposons, HA for short interspersed nucleotide elements (SINEs), and HR for LTRs. In contrast, SINE was shown to be overrepresented in all three types of regulatory sequences located in gene-neighboring regions. TE-regulated genes were mostly shown to have cell line specific pattern, and tissue-specific genes (TSGs) showed higher usage of TE regulatory sequences in the tissue of their expression. While TEs in the regulatory sequences showed to be older than their genome-wide counterparts, younger TEs were shown to be more likely used in cell line specific regulatory sequences. CONCLUSIONS: Collectively, our study provided further evidence enforcing an important contribution of TEs to tissue-specific gene regulation in humans.

Cardiac Transcription Factors and Regulatory Networks.

Cardiac development is a fine-tuned process governed by complex transcriptional networks, in which transcription factors (TFs) interact with other regulatory layers. In this chapter, we introduce the core cardiac TFs including Gata, Hand, Nkx2, Mef2, Srf, and Tbx. These factors regulate each other's expression and can also act in a combinatorial manner on their downstream targets. Their disruption leads to various cardiac phenotypes in mice, and mutations in humans have been associated with congenital heart defects. In the second part of the chapter, we discuss different levels of regulation including cis-regulatory elements, chromatin structure, and microRNAs, which can interact with transcription factors, modulate their function, or are downstream targets. Finally, examples of disturbances of the cardiac regulatory network leading to congenital heart diseases in human are provided.

Studying relative RNA localization From nucleus to the cytosol.

The precise coordination of important biological processes, such as differentiation and development, is highly dependent on the regulation of expression of the genetic information. The flow of the genetic information is tightly regulated on multiple levels. Among them, RNA export to cytosol is an essential step for the production of proteins in eukaryotic cells. Hence, estimating the relative concentration of RNA molecules of a given transcript species in the nucleus and in the cytosol is of major significance as it contributes to the understanding of the dynamics of RNA trafficking between the nucleus and the cytosol. The most efficient way to estimate the levels of RNA species genome-wide is through RNA sequencing (RNAseq). While RNAseq can be performed separately in the nucleus and in the cytosol, because measured transcript levels are relative to the total volume of RNA in these compartments, and because this volume is usually unknown, the transcript levels in the nucleus and in the cytosol cannot be directly compared. Here we show theoretically that if, in addition to nuclear and cytosolic RNA-seq, whole cell RNA-seq is also performed, then accurate estimations of the localization of transcripts can be obtained. Based on this, we designed a method that estimates, first the fraction of the total RNA volume in the cytosol (nucleus), and then, this fraction for every transcript. We evaluate our methodology on simulated data and nuclear and cytosolic single cell data available. Finally, we use our method to investigate the cellular localization of transcripts using bulk RNAseq data from the ENCODE project.

AliasNet: Alias artefact suppression network for accelerated phase-encode MRI.

Sparse reconstruction is an important aspect of MRI, helping to reduce acquisition time and improve spatial-temporal resolution. Popular methods are based mostly on compressed sensing (CS), which relies on the random sampling of k-space to produce incoherent (noise-like) artefacts. Due to hardware constraints, 1D Cartesian phase-encode under-sampling schemes are popular for 2D CS-MRI. However, 1D under-sampling limits 2D incoherence between measurements, yielding structured aliasing artefacts (ghosts) that may be difficult to remove assuming a 2D sparsity model. Reconstruction algorithms typically deploy direction-insensitive 2D regularisation for these direction-associated artefacts. Recognising that phase-encode artefacts can be separated into contiguous 1D signals, we develop two decoupling techniques that enable explicit 1D regularisation and leverage the excellent 1D incoherence characteristics. We also derive a combined 1D + 2D reconstruction technique that takes advantage of spatial relationships within the image. Experiments conducted on retrospectively under-sampled brain and knee data demonstrate that combination of the proposed 1D AliasNet modules with existing 2D deep learned (DL) recovery techniques leads to an improvement in image quality. We also find AliasNet enables a superior scaling of performance compared to increasing the size of the original 2D network layers. AliasNet therefore improves the regularisation of aliasing artefacts arising from phase-encode under-sampling, by tailoring the network architecture to account for their expected appearance. The proposed 1D + 2D approach is compatible with any existing 2D DL recovery technique deployed for this application.

Encoded Structural Color Microneedle Patches for Multiple Screening of Wound Small Molecules.

Detection of biomarkers associated with wound conditions provides in-depth healthcare information and benefits wound healing treatment. The current aim of wound detection is to achieve in situ multiple detections. Novel encoded structural color microneedle patches (EMNs) combining photonic crystals (PhCs) and microneedle arrays (MNs) for multiple wound biomarker detection in situ are described here. Using a partitioned and layered casting strategy, the EMNs can be divided into different modules and each serves for the detection of small molecules , including pH, glucose, and histamine. pH sensing is based on the interaction between hydrogen ions and carboxyl groups from hydrolyzed polyacrylamide (PAM); glucose sensing is achieved with the help of glucose-responsive fluorophenylboronic acid (FPBA); while histamine sensing relies on specific recognition of aptamers and target molecules. Owing to the responsive volume change of these three modules in the presence of target molecules, the EMNs can create structural color change and characteristic peak shift of the PhCs, thus realizing the qualitative measurement of target molecules with a spectrum analyzer. It is further demonstrated that the EMNs behave well in the multivariate detection of rat wound molecules. These features indicate that the EMNs can be valuable smart detection systems for wound status screening.

The EN-TEx resource of multi-tissue personal epigenomes & variant-impact models.

Understanding how genetic variants impact molecular phenotypes is a key goal of functional genomics, currently hindered by reliance on a single haploid reference genome. Here, we present the EN-TEx resource of 1,635 open-access datasets from four donors (∼30 tissues × âˆ¼15 assays). The datasets are mapped to matched, diploid genomes with long-read phasing and structural variants, instantiating a catalog of >1 million allele-specific loci. These loci exhibit coordinated activity along haplotypes and are less conserved than corresponding, non-allele-specific ones. Surprisingly, a deep-learning transformer model can predict the allele-specific activity based only on local nucleotide-sequence context, highlighting the importance of transcription-factor-binding motifs particularly sensitive to variants. Furthermore, combining EN-TEx with existing genome annotations reveals strong associations between allele-specific and GWAS loci. It also enables models for transferring known eQTLs to difficult-to-profile tissues (e.g., from skin to heart). Overall, EN-TEx provides rich data and generalizable models for more accurate personal functional genomics.

The Use of a Coded Healing Abutment in the Restoration of a Single, Immediately Placed Implant in the Esthetic Zone: A Clinical Case Report.

For several years, the implant-level impression procedure started by removal of the healing abutment, followed by connection of the impression coping to the implant. Encode Complete (Encode, Biomet 3i, Biomet 3i, Palm Beach Gardens, FL) was introduced to eliminate implant-level impressions by offering a healing abutment-level impression protocol. This report illustrated the treatment of a single tooth in the anterior esthetic zone using Encode Complete. A 23-year-old female patient reported to the prosthodontics clinic complaining of a fractured maxillary anterior tooth that was deemed nonrestorable. After immediate implant placement, soft tissue preservation and temporization of the implant, healing abutment level impression was made. The codes embedded on the occlusal surface communicated the implant depth, hex-orientation, platform diameter, and interface. The definitive Encode gold-plated titanium abutment was anatomically designed virtually with customized margin, contour, taper, and emergence profile. The milling process was initiated, and the virtual design data were sent to the Robocast Center for analog placement in the original Encode master cast. The definitive abutment was placed on the master cast using Robocast technology, followed by the fabrication of the final porcelain fused to zirconia cement-retained all ceramic crown. The abutment was secured to the implant with a Gold-Tite abutment screw, followed by the final cement-retained implant crown placement. Recall visits were obtained at 1 week, 1 month, 3 months, and 1 year after final prosthesis insertion.

Modulating Brain Activity with Invasive Brain-Computer Interface: A Narrative Review.

Brain-computer interface (BCI) can be used as a real-time bidirectional information gateway between the brain and machines. In particular, rapid progress in invasive BCI, propelled by recent developments in electrode materials, miniature and power-efficient electronics, and neural signal decoding technologies has attracted wide attention. In this review, we first introduce the concepts of neuronal signal decoding and encoding that are fundamental for information exchanges in BCI. Then, we review the history and recent advances in invasive BCI, particularly through studies using neural signals for controlling external devices on one hand, and modulating brain activity on the other hand. Specifically, regarding modulating brain activity, we focus on two types of techniques, applying electrical stimulation to cortical and deep brain tissues, respectively. Finally, we discuss the related ethical issues concerning the clinical application of this emerging technology.

Comprehensive 100-bp resolution genome-wide epigenomic profiling data for the hg38 human reference genome.

This manuscript presents a comprehensive collection of diverse epigenomic profiling data for the human genome in 100-bp resolution with full genome-wide coverage. The datasets are processed from raw read count data collected from five types of sequencing-based assays collected by the Encyclopedia of DNA Elements consortium (ENCODE, http://www.encodeproject.org). Data from high-throughput sequencing assays were processed and crystallized into a total of 6,305 genome-wide profiles. To ensure the quality of the features, we filtered out assays with low read depth, inconsistent read counts, and poor data quality. The types of sequencing-based experiment assays include DNase-seq, histone and TF ChIP-seq, ATAC-seq, and Poly(A) RNA-seq. Merging of processed data was done by averaging read counts across technical replicates to obtain signals in about 30 million predefined 100-bp bins that tile the entire genome. We provide an example of fetching read counts using disease-related risk variants from the GWAS Catalog. Additionally, we have created a tabix index enabling fast user retrieval of read counts given coordinates in the human genome. The data processing pipeline is replicable for users' own purposes and for other experimental assays. The processed data can be found on Zenodo at https://zenodo.org/record/7015783. These data can be used as features for statistical and machine learning models to predict or infer a wide range of variables of biological interest. They can also be applied to generate novel insights into gene expression, chromatin accessibility, and epigenetic modifications across the human genome. Finally, the processing pipeline can be easily applied to data from any other genome-wide profiling assays, expanding the amount of available data.

End-to-End Deep Convolutional Recurrent Models for Noise Robust Waveform Speech Enhancement.

Because of their simple design structure, end-to-end deep learning (E2E-DL) models have gained a lot of attention for speech enhancement. A number of DL models have achieved excellent results in eliminating the background noise and enhancing the quality as well as the intelligibility of noisy speech. Designing resource-efficient and compact models during real-time processing is still a key challenge. In order to enhance the accomplishment of E2E models, the sequential and local characteristics of speech signal should be efficiently taken into consideration while modeling. In this paper, we present resource-efficient and compact neural models for end-to-end noise-robust waveform-based speech enhancement. Combining the Convolutional Encode-Decoder (CED) and Recurrent Neural Networks (RNNs) in the Convolutional Recurrent Network (CRN) framework, we have aimed at different speech enhancement systems. Different noise types and speakers are used to train and test the proposed models. With LibriSpeech and the DEMAND dataset, the experiments show that the proposed models lead to improved quality and intelligibility with fewer trainable parameters, notably reduced model complexity, and inference time than existing recurrent and convolutional models. The quality and intelligibility are improved by 31.61% and 17.18% over the noisy speech. We further performed cross corpus analysis to demonstrate the generalization of the proposed E2E SE models across different speech datasets.

Video captioning based on vision transformer and reinforcement learning.

Global encoding of visual features in video captioning is important for improving the description accuracy. In this paper, we propose a video captioning method that combines Vision Transformer (ViT) and reinforcement learning. Firstly, Resnet-152 and ResNeXt-101 are used to extract features from videos. Secondly, the encoding block of the ViT network is applied to encode video features. Thirdly, the encoded features are fed into a Long Short-Term Memory (LSTM) network to generate a video content description. Finally, the accuracy of video content description is further improved by fine-tuning reinforcement learning. We conducted experiments on the benchmark dataset MSR-VTT used for video captioning. The results show that compared with the current mainstream methods, the model in this paper has improved by 2.9%, 1.4%, 0.9% and 4.8% under the four evaluation indicators of LEU-4, METEOR, ROUGE-L and CIDEr-D, respectively.

Optimization of the Omni-ATAC protocol to chromatin accessibility profiling in snap-frozen rat adipose and muscle tissues.

ATAC-seq is a fast and sensitive method for the epigenomic profiling of open chromatin and for mapping of transcription factor binding sites [1]. Despite the development of the Omni-ATAC protocol for the profiling of chromatin accessibility in frozen tissues [2], studies in adipose tissue have been restricted due to technical challenges including the high lipid content of adipocytes and reproducibility issues between replicates. Here, we provide a modified Omni-ATAC protocol that achieves high data reproducibility in various tissue types from rat, including adipose and muscle tissues [3].•This protocol describes a methodology that enables chromatin accessibility profiling from snap-frozen rat adipose and muscle tissues.•The technique comprises an optimized bead-based tissue homogenization process that substitutes to Dounce homogenization, reduces variability in the experimental procedure, and is adaptable to various tissue types.•In comparison with the Omni-ATAC protocol, the method described here results in improved ATAC-seq data quality that complies with ENCODE quality standards.

Proteomics-driven identification of short open reading frame-encoded peptides.

Accumulating evidence has shown that a large number of short open reading frames (sORFs) also have the ability to encode proteins. The discovery of sORFs opens up a new research area, leading to the identification and functional study of sORF encoded peptides (SEPs) at the omics level. Besides bioinformatics prediction and ribosomal profiling, mass spectrometry (MS) has become a significant tool as it directly detects the sequence of SEPs. Though MS-based proteomics methods have proved to be effective for qualitative and quantitative analysis of SEPs, the detection of SEPs is still a great challenge due to their low abundance and short sequence. To illustrate the progress in method development, we described and discussed the main steps of large-scale proteomics identification of SEPs, including SEP extraction and enrichment, MS detection, data processing and quality control, quantification, and function prediction and validation methods.

The Complexity of the Mammalian Transcriptome.

Draft genome assemblies for multiple mammalian species combined with new technologies to map transcripts from diverse RNA samples to these genomes developed in the early 2000s revealed that the mammalian transcriptome was vastly larger and more complex than previously anticipated. Efforts to comprehensively catalog the identity and features of transcripts present in a variety of species, tissues and cell lines revealed that a large fraction of the mammalian genome is transcribed in at least some settings. A large number of these transcripts encode long non-coding RNAs (lncRNAs). Many lncRNAs overlap or are anti-sense to protein coding genes and others overlap small RNAs. However, a large number are independent of any previously known mRNA or small RNA. While the functions of a majority of these lncRNAs are unknown, many appear to play roles in gene regulation. Many lncRNAs have species-specific and cell type specific expression patterns and their evolutionary origins are varied. While technological challenges have hindered getting a full picture of the diversity and transcript structure of all of the transcripts arising from lncRNA loci, new technologies including single molecule nanopore sequencing and single cell RNA sequencing promise to generate a comprehensive picture of the mammalian transcriptome.

Computational modeling of chromatin accessibility identified important epigenomic regulators.

Chromatin accessibility is essential for transcriptional activation of genomic regions. It is well established that transcription factors (TFs) and histone modifications (HMs) play critical roles in chromatin accessibility regulation. However, there is a lack of studies that quantify these relationships. Here we constructed a two-layer model to predict chromatin accessibility by integrating DNA sequence, TF binding, and HM signals. By applying the model to two human cell lines (GM12878 and HepG2), we found that DNA sequences had limited power for accessibility prediction, while both TF binding and HM signals predicted chromatin accessibility with high accuracy. According to the HM model, HM features determined chromatin accessibility in a cell line shared manner, with the prediction power attributing to five core HM types. Results from the TF model indicated that chromatin accessibility was determined by a subset of informative TFs including both cell line-specific and generic TFs. The combined model of both TF and HM signals did not further improve the prediction accuracy, indicating that they provide redundant information in terms of chromatin accessibility prediction. The TFs and HM models can also distinguish the chromatin accessibility of proximal versus distal transcription start sites with high accuracy.

SciApps: An Automated Platform for Processing and Distribution of Plant Genomics Data.

SciApps is an open-source, web-based platform for processing, storing, visualizing, and distributing genomic data and analysis results. Built upon the Tapis (formerly Agave) platform, SciApps brings users TB-scale of data storage via CyVerse Data Store and over one million CPUs via the Extreme Science and Engineering Discovery Environment (XSEDE) resources at Texas Advanced Computing Center (TACC). SciApps provides users ways to chain individual jobs into automated and reproducible workflows in a distributed cloud and provides a management system for data, associated metadata, individual analysis jobs, and multi-step workflows. This chapter provides examples of how to (1) submitting, managing, constructing workflows, (2) using public workflows for Bulked Segregant Analysis (BSA), (3) constructing a Data Analysis Center (DAC), and Data Coordination Center (DCC) for the plant ENCODE project.

Prioritization of human well-being spectrum related GWAS-SNVs using ENCODE-based web-tools predict interplay between PSMC3, ITIH4, and SERPINC1 genes in modulating well-being.

Several traits related to positive and negative affect show a high genetic as well as phenotypic correlation with well-being in humans, and are therefore collectively termed as "Well-being spectrum". Genome-Wide Association studies (GWA studies) on "well-being measurement" have led to identification of several genomic variants (Single Nucleotide Variants - SNVs), but very little has been explained with respect to their functionality and mode of alteration of well-being. Utilizing a pool of 1258 GWA studies based SNVs on "well-being measurement", we prioritized the SNVs and tried to annotate well-being related functionality through several bioinformatic tools to predict whether a protein sequence variation affects protein function, as well as experimentally validated datasets available in ENCODE based web-tools namely rSNPBase, RegulomeDB, Haploreg, along with GTEx Portal and STRING based protein interaction networks. Prioritization yielded three key SNVs; rs3781627-A, rs13072536-T and 5877-C potentially regulating three genes, PSMC3, ITIH4 and SERPINC1, respectively. Interestingly, the genes showed well clustered protein-protein interaction (maximum combined confidence score >0.4) with other well-being candidate genes, namely TNF and CRP genes suggesting their important role in modulation of well-being. PSMC3 and ITIH4 genes are also involved in driving acute phase responses signifying a probable cross-talk between well-being and psychoneuroimmunological system. To best of our knowledge this study is the first of its kind where the well-being associated GWA studies-SNVs were prioritized and functionally annotated, majorly based on functional data available in public domain, which revealed PSMC3, ITIH4 and SERPINC1 genes as probable candidates in regulation of well-being spectrum.

The role of purifying selection in the origin and maintenance of complex function.

Fitness contribution alone should not be the criterion of 'function' in molecular biology and genomics. Disagreement over the use of 'function' in molecular biology and genomics is still with us, almost eight years after publicity surrounding the Encyclopedia of DNA Elements project claimed that 80.4% of the human genome comprises "functional elements". Recent approaches attempt to resolve or reformulate this debate by redefining genomic 'function' in terms of current fitness contribution. In its favour, this redefinition for the genomic context is in apparent conformity with predominant experimental practices, especially in biomedical research, and with ascription of function by selective maintenance. We argue against approaches of this kind, however, on the grounds that they could be seen as non-Darwinian, and fail to properly account for the diversity of non-adaptive processes involved in the origin and maintenance of genomic complexity. We examine cases of molecular and organismal complexity that arise neutrally, showing how purifying selection maintains non-adaptive genomic complexity. Rather than lumping different sorts of genomic complexity together by defining 'function' as fitness contribution, we argue that it is best to separate the heterogeneous contributions of preaptation, exaptation and adaptation to the historical processes of origin and maintenance for complex features.

Decoding brain encoded by genome.

Human brain is the most complicated living organ in nature. How the human genome encodes the structure and function of brain is a fundamental question to understand the essence of mind. Currently, it is still an unsolved scientific problem requiring the further breakthrough of comprehensive technologies. Here, we summarize the recent advances in brain development/function OMICS studies, and discuss the huge challenges and prospects in understanding how brain is encoded by genome.

Role of Non-Coding Regulatory Elements in the Control of GR-Dependent Gene Expression.

The glucocorticoid receptor (GR, also known as NR3C1) coordinates molecular responses to stress. It is a potent transcription activator and repressor that influences hundreds of genes. Enhancers are non-coding DNA regions outside of the core promoters that increase transcriptional activity via long-distance interactions. Active GR binds to pre-existing enhancer sites and recruits further factors, including EP300, a known transcriptional coactivator. However, it is not known how the timing of GR-binding-induced enhancer remodeling relates to transcriptional changes. Here we analyze data from the ENCODE project that provides ChIP-Seq and RNA-Seq data at distinct time points after dexamethasone exposure of human A549 epithelial-like cell line. This study aimed to investigate the temporal interplay between GR binding, enhancer remodeling, and gene expression. By investigating a single distal GR-binding site for each differentially upregulated gene, we show that transcriptional changes follow GR binding, and that the largest enhancer remodeling coincides in time with the highest gene expression changes. A detailed analysis of the time course showed that for upregulated genes, enhancer activation persists after gene expression changes settle. Moreover, genes with the largest change in EP300 binding showed the highest expression dynamics before the peak of EP300 recruitment. Overall, our results show that enhancer remodeling may not directly be driving gene expression dynamics but rather be a consequence of expression activation.