Identification and characterization of structural variants related to meat quality in pigs using chromosome-level genome assemblies.

BACKGROUND: Many studies have been performed to identify various genomic loci and genes associated with the meat quality in pigs. However, the full genetic architecture of the trait still remains unclear in part because of the lack of accurate identification of related structural variations (SVs) which resulted from the shortage of target breeds, the limitations of sequencing data, and the incompleteness of genome assemblies. The recent generation of a new pig breed with superior meat quality, called Nanchukmacdon, and its chromosome-level genome assembly (the NCMD assembly) has provided new opportunities. RESULTS: By applying assembly-based SV calling approaches to various genome assemblies of pigs including Nanchukmacdon, the impact of SVs on meat quality was investigated. Especially, by checking the commonality of SVs with other pig breeds, a total of 13,819 Nanchukmacdon-specific SVs (NSVs) were identified, which have a potential effect on the unique meat quality of Nanchukmacdon. The regulatory potentials of NSVs for the expression of nearby genes were further examined using transcriptome- and epigenome-based analyses in different tissues. CONCLUSIONS: Whole-genome comparisons based on chromosome-level genome assemblies have led to the discovery of SVs affecting meat quality in pigs, and their regulatory potentials were analyzed. The identified NSVs will provide new insights regarding genetic architectures underlying the meat quality in pigs. Finally, this study confirms the utility of chromosome-level genome assemblies and multi-omics analysis to enhance the understanding of unique phenotypes.

A chromosome-level genome assembly of the Korean crossbred pig Nanchukmacdon (Sus scrofa).

As plentiful high-quality genome assemblies have been accumulated, reference-guided genome assembly can be a good approach to reconstruct a high-quality assembly. Here, we present a chromosome-level genome assembly of the Korean crossbred pig called Nanchukmacdon (the NCMD assembly) using the reference-guided assembly approach with short and long reads. The NCMD assembly contains 20 chromosome-level scaffolds with a total size of 2.38 Gbp (N50: 138.77 Mbp). Its BUSCO score is 93.1%, which is comparable to the pig reference assembly, and a total of 20,588 protein-coding genes, 8,651 non-coding genes, and 996.14 Mbp of repetitive elements are annotated. The NCMD assembly was also used to close many gaps in the pig reference assembly. This NCMD assembly and annotation provide foundational resources for the genomic analyses of pig and related species.

The AIRN lncRNA is imprinted and paternally expressed in pigs.

Genomic imprinting plays critical roles during the development of mammalian species and underlying epigenetic mechanisms frequently involve long non-coding RNAs (lncRNAs). The paternal transcription of the antisense Igf2r RNA noncoding (Airn) is responsible for paternal silencing of the mouse insulin-like growth factor 2 receptor (Igf2r) gene and maternal Igf2r expression. Although the corresponding maternal DNA methylation imprint is conserved in humans and pigs, the orthologous AIRN lncRNA has been identified in humans but not in pigs. Here, we aimed to examine imprinted allelic expression of the porcine AIRN lncRNA along with a corresponding differentially methylated region (DMR) and to analyze allelic expression of AIRN and IGF2R in pigs. By comparing parthenogenetic and control porcine embryos, we identified a maternally methylated DMR and a significantly higher expression of AIRN lncRNA in control embryos (P < 0.05) indicating its paternal expression. Further analyses revealed that the expression of AIRN lncRNA was enriched in the pig brain and its subregions, and it was monoallelically expressed; whereas, IGF2R was expressed biallelically suggesting an absence of allele-specific transcriptional regulation. Our findings will lead to further investigations into the role of the imprinted porcine AIRN lncRNA during pig development.

Genomic imprinting is important for the development of mammals and long non-coding RNAs are often involved in the imprinting process. In mice, Airn encodes a long non-coding RNA that is imprinted, and therefore, transcribed only from the paternal allele. This paternal transcription of Airn interferes with the adjacent Igf2r promoter, leading to maternal expression of Igf2r. In pigs, the orthologous AIRN has not been identified as well as its imprinting. In the current study, we report porcine AIRN and allelic expression of both AIRN and IGF2R using our parthenogenetic embryo models and various normal pig tissues.

Association of functional sequence variants of the myosin heavy chain 3 gene with muscle collagen content in pigs.

This study examined the association between functional sequence variants (FSVs) of myosin heavy chain 3 (MYH3) genotypes and collagen content in a Landrace and Jeju native pig (JNP) crossbred population. Four muscles (Musculus longissimus dorsi, Musculus semimembranosus, Musculus triceps brachii, and Musculus biceps femoris) were used for the analysis of meat collagen content, and the same animals were genotyped for the FSVs of the MYH3 gene by using PCR-RFLP (polymerase chain reaction-restriction fragment length polymorphism). Three FSVs of MYH3 genotypes were identified and had genotype frequencies of 0.358, 0.551, and 0.091 for QQ, Qq, and qq, respectively. QQ animals for the FSVs of the MYH3 genotypes showed higher collagen content in their M. longissimus dorsi (p < 0.001), M. semimembranosus (p < 0.001), M. triceps brachii (p < 0.001), and M. biceps femoris (p < 0.001) than qq homozygous animals. After the validation of this result in other independent populations, the FSVs of MYH3 genotypes can be a valuable genetic marker for improving collagen content in porcine muscles and can also be applied to increase the amount of collagen for biomedical purposes.

Deep-learning-based gas identification by time-variant illumination of a single micro-LED-embedded gas sensor.

Electronic nose (e-nose) technology for selectively identifying a target gas through chemoresistive sensors has gained much attention for various applications, such as smart factory and personal health monitoring. To overcome the cross-reactivity problem of chemoresistive sensors to various gas species, herein, we propose a novel sensing strategy based on a single micro-LED (µLED)-embedded photoactivated (µLP) gas sensor, utilizing the time-variant illumination for identifying the species and concentrations of various target gases. A fast-changing pseudorandom voltage input is applied to the µLED to generate forced transient sensor responses. A deep neural network is employed to analyze the obtained complex transient signals for gas detection and concentration estimation. The proposed sensor system achieves high classification (~96.99%) and quantification (mean absolute percentage error ~ 31.99%) accuracies for various toxic gases (methanol, ethanol, acetone, and nitrogen dioxide) with a single gas sensor consuming 0.53 mW. The proposed method may significantly improve the efficiency of e-nose technology in terms of cost, space, and power consumption.

Nanowatt-Level Photoactivated Gas Sensor Based on Fully-Integrated Visible MicroLED and Plasmonic Nanomaterials.

Photoactivated gas sensors that are fully integrated with micro light-emitting diodes (µLED) have shown great potential to substitute conventional micro/nano-electromechanical (M/NEMS) gas sensors owing to their low power consumption, high mechanical stability, and mass-producibility. Previous photoactivated gas sensors mostly have utilized ultra-violet (UV) light (250-400 nm) for activating high-bandgap metal oxides, although energy conversion efficiencies of gallium nitride (GaN) LEDs are maximized in the blue range (430-470 nm). This study presents a more advanced monolithic photoactivated gas sensor based on a nanowatt-level, ultra-low-power blue (λpeak  = 435 nm) µLED platform (µLP). To promote the blue light absorbance of the sensing material, plasmonic silver (Ag) nanoparticles (NPs) are uniformly coated on porous indium oxide (In2 O3 ) thin films. By the plasmonic effect, Ag NPs absorb the blue light and spontaneously transfer excited hot electrons to the surface of In2 O3 . Consequently, high external quantum efficiency (EQE, ≈17.3%) and sensor response (ΔR/R0 (%) = 1319%) to 1 ppm NO2 gas can be achieved with a small power consumption of 63 nW. Therefore, it is highly expected to realize various practical applications of mobile gas sensors such as personal environmental monitoring devices, smart factories, farms, and home appliances.

Drastic Gas Sensing Selectivity in 2-Dimensional MoS2 Nanoflakes by Noble Metal Decoration.

Noble metal nanoparticle decoration is a representative strategy to enhance selectivity for fabricating chemical sensor arrays based on the 2-dimensional (2D) semiconductor material, represented by molybdenum disulfide (MoS2). However, the mechanism of selectivity tuning by noble metal decoration on 2D materials has not been fully elucidated. Here, we successfully decorated noble metal nanoparticles on MoS2 flakes by the solution process without using reducing agents. The MoS2 flakes showed drastic selectivity changes after surface decoration and distinguished ammonia, hydrogen, and ethanol gases clearly, which were not observed in general 3D metal oxide nanostructures. The role of noble metal nanoparticle decoration on the selectivity change is investigated by first-principles density functional theory (DFT) calculations. While the H2 sensitivity shows a similar tendency with the calculated binding energy, that of NH3 is strongly related to the binding site deactivation due to preferred noble metal particle decoration at the MoS2 edge. This finding is a specific phenomenon which originates from the distinguished structure of the 2D material, with highly active edge sites. We believe that our study will provide the fundamental comprehension for the strategy to devise the highly efficient sensor array based on 2D materials.

Low-Power, Multi-Transduction Nanosensor Array for Accurate Sensing of Flammable and Toxic Gases.

Toxic and flammable gases pose a major safety risk in industrial settings; thus, their portable sensing is desired, which requires sensors with fast response, low-power consumption, and accurate detection. Herein, a low-power, multi-transduction array is presented for the accurate sensing of flammable and toxic gases. Specifically, four different sensors are integrated on a micro-electro-mechanical-systems platform consisting of bridge-type microheaters. To produce distinct fingerprints for enhanced selectivity, the four sensors operate based on two different transduction mechanisms: chemiresistive and calorimetric sensing. Local, in situ synthesis routes are used to integrate nanostructured materials (ZnO, CuO, and Pt Black) for the sensors on the microheaters. The transient responses of the four sensors are fed to a convolutional neural network for real-time classification and regression of five different gases (H2 , NO2 , C2 H6 O, CO, and NH3 ). An overall classification accuracy of 97.95%, an average regression error of 14%, and a power consumption of 7 mW per device are obtained. The combination of a versatile low-power platform, local integration of nanomaterials, different transduction mechanisms, and a real-time machine learning strategy presented herein helps advance the constant need to simultaneously achieve fast, low-power, and selective gas sensing of flammable and toxic gases.

Ultra-Low-Power E-Nose System Based on Multi-Micro-LED-Integrated, Nanostructured Gas Sensors and Deep Learning.

As interests in air quality monitoring related to environmental pollution and industrial safety increase, demands for gas sensors are rapidly increasing. Among various gas sensor types, the semiconductor metal oxide (SMO)-type sensor has advantages of high sensitivity, low cost, mass production, and small size but suffers from poor selectivity. To solve this problem, electronic nose (e-nose) systems using a gas sensor array and pattern recognition are widely used. However, as the number of sensors in the e-nose system increases, total power consumption also increases. In this study, an ultra-low-power e-nose system was developed using ultraviolet (UV) micro-LED (µLED) gas sensors and a convolutional neural network (CNN). A monolithic photoactivated gas sensor was developed by depositing a nanocolumnar In2O3 film coated with plasmonic metal nanoparticles (NPs) directly on the µLED. The e-nose system consists of two different µLED sensors with silver and gold NP coating, and the total power consumption was measured as 0.38 mW, which is one-hundredth of the conventional heater-based e-nose system. Responses to various target gases measured by multi-µLED gas sensors were analyzed by pattern recognition and used as the training data for the CNN algorithm. As a result, a real-time, highly selective e-nose system with a gas classification accuracy of 99.32% and a gas concentration regression error (mean absolute) of 13.82% for five different gases (air, ethanol, NO2, acetone, methanol) was developed. The µLED-based e-nose system can be stably battery-driven for a long period and is expected to be widely used in environmental internet of things (IoT) applications.

Loss of Monoallelic Expression of IGF2 in the Adult Liver Via Alternative Promoter Usage and Chromatin Reorganization.

In mammals, genomic imprinting operates via gene silencing mechanisms. Although conservation of the imprinting mechanism at the H19/IGF2 locus has been generally described in pigs, tissue-specific imprinting at the transcript level, monoallelic-to-biallelic conversion, and spatio-temporal chromatin reorganization remain largely uninvestigated. Here, we delineate spatially regulated imprinting of IGF2 transcripts, age-dependent hepatic mono- to biallelic conversion, and reorganization of topologically associating domains at the porcine H19/IGF2 locus for better translation to human and animal research. Whole-genome bisulfite sequencing (WGBS) and RNA sequencing (RNA-seq) of normal and parthenogenetic porcine embryos revealed the paternally hypermethylated H19 differentially methylated region and paternal expression of IGF2. Using a polymorphism-based approach and omics datasets from chromatin immunoprecipitation sequencing (ChIP-seq), whole-genome sequencing (WGS), RNA-seq, and Hi-C, regulation of IGF2 during development was analyzed. Regulatory elements in the liver were distinguished from those in the muscle where the porcine IGF2 transcript was monoallelically expressed. The IGF2 transcript from the liver was biallelically expressed at later developmental stages in both pigs and humans. Chromatin interaction was less frequent in the adult liver compared to the fetal liver and skeletal muscle. The duration of genomic imprinting effects within the H19/IGF2 locus might be reduced in the liver with biallelic conversion through alternative promoter usage and chromatin remodeling. Our integrative omics analyses of genome, epigenome, and transcriptome provided a comprehensive view of imprinting status at the H19/IGF2 cluster.

Multi-omics approaches for comprehensive analysis and understanding of the immune response in the miniature pig breed.

The porcine immune system has an important role in pre-clinical studies together with understanding the biological response mechanisms before entering into clinical trials. The size distribution of the Korean minipig is an important feature that make this breed ideal for biomedical research and safe practice in post clinical studies. The extremely tiny (ET) minipig serves as an excellent model for various biomedical research studies, but the comparatively frail and vulnerable immune response to the environment over its Large (L) size minipig breed leads to additional after born care. To overcome this pitfall, comparative analysis of the genomic regions under selection in the L type breed could provide a better understanding at the molecular level and lead to the development of an enhanced variety of ET type minipig. In this study, we utilized whole genome sequencing (WGS) to identify traces of artificial selection and integrated them with transcriptome data generated from blood samples to find strongly selected and differentially expressed genes of interest. We identified a total of 35 common genes among which 7 were differentially expressed and showed selective sweep in the L type over the ET type minipig breed. The stabilization of these genes were further confirmed using nucleotide diversity analysis, and these genes could serve as potential biomarkers for the development of a better variety of ET type pig breed.

Artificial Olfactory Neuron for an In-Sensor Neuromorphic Nose.

A neuromorphic module of an electronic nose (E-nose) is demonstrated by hybridizing a chemoresistive gas sensor made of a semiconductor metal oxide (SMO) and a single transistor neuron (1T-neuron) made of a metal-oxide-semiconductor field-effect transistor (MOSFET). By mimicking a biological olfactory neuron, it simultaneously detects a gas and encoded spike signals for in-sensor neuromorphic functioning. It identifies an odor source by analyzing the complicated mixed signals using a spiking neural network (SNN). The proposed E-nose does not require conversion circuits, which are essential for processing the sensory signals between the sensor array and processors in the conventional bulky E-nose. In addition, they do not have to include a central processing unit (CPU) and memory, which are required for von Neumann computing. The spike transmission of the biological olfactory system, which is known to be the main factor for reducing power consumption, is realized with the SNN for power savings compared to the conventional E-nose with a deep neural network (DNN). Therefore, the proposed neuromorphic E-nose is promising for application to Internet of Things (IoT), which demands a highly scalable and energy-efficient system. As a practical example, it is employed as an electronic sommelier by classifying different types of wines.

Nanogap Formation Using a Chromium Oxide Film and Its Application as a Palladium Hydrogen Switch.

Developing high response hydrogen sensors manufacturable in a large scale is desirable in hydrogen industry. In this study, a chromium oxidation-based nanogap formation process was developed to fabricate a hydrogen switch with suspended palladium and gold films having a tens of nanometer-sized gap. The nanogap was formed by using oxidized chromium as a self-alignment shadow mask. The hydrogen switch operates by the principle of volume expansion of palladium upon exposure to the hydrogen gas and the current reading by closing of a nanogap formed between suspended palladium and gold films. Further improvement of the sensor performance was achieved by optimizing the design parameters such as suspended film lengths and thicknesses. The fabricated palladium nanogap hydrogen sensor showed an ultrahigh sensitivity of ΔI/I0 > 108 with a fast response time (22 s) to 4% hydrogen. The complementary metal-oxide-semiconductor-compatible fabrication of the hydrogen switch is easily scalable with low manufacturing cost.

High Accuracy Real-Time Multi-Gas Identification by a Batch-Uniform Gas Sensor Array and Deep Learning Algorithm.

Semiconductor metal oxide (SMO) gas sensors are attracting great attention as next-generation environmental monitoring sensors. However, there are limitations to the actual application of SMO gas sensors due to their low selectivity. Although the electronic nose (E-nose) systems based on a sensor array are regarded as a solution for the selectivity issue, poor accuracy caused by the nonuniformity of the fabricated gas sensors and difficulty of real-time gas detection have yet to be resolved. In this study, these problems have been solved by fabricating uniform gas sensor arrays and applying the deep learning algorithm to the data from the sensor arrays. Nanocolumnar films of metal oxides (SnO2, In2O3, WO3, and CuO) with a high batch uniformity deposited through glancing angle deposition were used as the sensing materials. The convolutional neural network (CNN) using the input data as a matrix form was adopted as a learning algorithm, which could conduct pattern recognition of the sensor responses. Finally, real-time selective gas detection for CO, NH3, NO2, CH4, and acetone (C3H6O) gas was achieved (minimum response time of 1, 8, 5, 19, and 2 s, respectively) with an accuracy of 98% by applying preprocessed response data to the CNN.

Comparative methylation and RNA-seq expression analysis in CpG context to identify genes involved in Backfat vs. Liver diversification in Nanchukmacdon Pig.

BACKGROUND: DNA methylation and demethylation at CpG islands is one of the main regulatory factors that allow cells to respond to different stimuli. These regulatory mechanisms help in developing tissue without affecting the genomic composition or undergoing selection. Liver and backfat play important roles in regulating lipid metabolism and control various pathways involved in reproductive performance, meat quality, and immunity. Genes inside these tissue store a plethora of information and an understanding of these genes is required to enhance tissue characteristics in the future generation. RESULTS: A total of 16 CpG islands were identified, and they were involved in differentially methylation regions (DMRs) as well as differentially expressed genes (DEGs) of liver and backfat tissue samples. The genes C7orf50, ACTB and MLC1 in backfat and TNNT3, SIX2, SDK1, CLSTN3, LTBP4, CFAP74, SLC22A23, FOXC1, GMDS, GSC, GATA4, SEMA5A and HOXA5 in the liver, were categorized as differentially-methylated. Subsequently, Motif analysis for DMRs was performed to understand the role of the methylated motif for tissue-specific differentiation. Gene ontology studies revealed association with collagen fibril organization, the Bone Morphogenetic Proteins (BMP) signaling pathway in backfat and cholesterol biosynthesis, bile acid and bile salt transport, and immunity-related pathways in methylated genes expressed in the liver. CONCLUSIONS: In this study, to understand the role of genes in the differentiation process, we have performed whole-genome bisulfite sequencing (WGBS) and RNA-seq analysis of Nanchukmacdon pigs. Methylation and motif analysis reveals the critical role of CpG islands and transcriptional factors binding site (TFBS) in guiding the differential patterns. Our findings could help in understanding how methylation of certain genes plays an important role and can be used as biomarkers to study tissue specific characteristics.

Customizable, conformal, and stretchable 3D electronics via predistorted pattern generation and thermoforming.

Recently, three-dimensional electronics (3DE) is attracting huge interest owing to the increasing demands for seamless integration of electronic systems on 3D curvilinear surfaces. However, it is still challenging to fabricate 3DE with high customizability, conformability, and stretchability. Here, we present a fabrication method of 3DE based on predistorted pattern generation and thermoforming. Through this method, custom-designed 3DE is fabricated through the thermoforming process. The fabricated 3DE has high 3D conformability because the thermoforming process enables the complete replication of both the overall shape and the surface texture of the 3D mold. Furthermore, the usage of thermoplastic elastomer and a liquid metal­based conductive electrode allows for high thermoformability during the device fabrication as well as high stretchability during the device operation. We believe that this technology can enable a wide range of new functionalities and multiscale 3D morphologies in wearable electronics.

GWAS and Post-GWAS High-Resolution Mapping Analyses Identify Strong Novel Candidate Genes Influencing the Fatty Acid Composition of the Longissimus dorsi Muscle in Pigs.

Fatty acid (FA) composition is one of the most important parameters for the assessment of meat quality in pigs. The FA composition in pork can also affect human health. Our aim was to identify quantitative trait loci (QTLs) and positional candidate genes affecting the FA profile of the longissimus dorsi muscle in a large F2 intercross between Landrace and Korean native pigs comprising 1105 F2 progeny by genome-wide association studies (GWAS) and post-GWAS high-resolution mapping analyses. We performed GWAS using the PorcineSNP60K BeadChip and a linear mixed model. Four genome-wide significant QTL regions in SSC8, SSC12, SSC14, and SSC16 were detected (p < 2.53 × 10-7). Several co-localizations of QTLs in SSC12 for oleic acid, linoleic acid, arachidonic acid, monounsaturated FAs, polyunsaturated FAs, and the polyunsaturated/saturated FA ratio were observed. To refine the QTL region in SSC12, a linkage and linkage disequilibrium analysis was applied and could narrow down the critical region to a 0.749 Mb region. Of the genes in this region, GAS7, MYH2, and MYH3 were identified as strong novel candidate genes based on further conditional association analyses. These findings provide a novel insight into the genetic basis of FA composition in pork and could contribute to the improvement of pork quality.

Integration of multi-omics approaches for functional characterization of muscle related selective sweep genes in Nanchukmacdon.

Pig as a food source serves daily dietary demand to a wide population around the world. Preference of meat depends on various factors with muscle play the central role. In this regards, selective breeding abled us to develop "Nanchukmacdon" a pig breeds with an enhanced variety of meat and high fertility rate. To identify genomic regions under selection we performed whole-genome resequencing, transcriptome, and whole-genome bisulfite sequencing from Nanchukmacdon muscles samples and used published data for three other breeds such as Landrace, Duroc, Jeju native pig and analyzed the functional characterization of candidate genes. In this study, we present a comprehensive approach to identify candidate genes by using multi-omics approaches. We performed two different methods XP-EHH, XP-CLR to identify traces of artificial selection for traits of economic importance. Moreover, RNAseq analysis was done to identify differentially expressed genes in the crossed breed population. Several genes (UGT8, ZGRF1, NDUFA10, EBF3, ELN, UBE2L6, NCALD, MELK, SERP2, GDPD5, and FHL2) were identified as selective sweep and differentially expressed in muscles related pathways. Furthermore, nucleotide diversity analysis revealed low genetic diversity in Nanchukmacdon for identified genes in comparison to related breeds and whole-genome bisulfite sequencing data shows the critical role of DNA methylation pattern in identified genes that leads to enhanced variety of meat. This work demonstrates a way to identify the molecular signature and lays a foundation for future genomic enabled pig breeding.

Chemo-Mechanically Operating Palladium-Polymer Nanograting Film for a Self-Powered H2 Gas Sensor.

This study proposes a reliable and self-powered hydrogen (H2) gas sensor composed of a chemo-mechanically operating nanostructured film and photovoltaic cell. Specifically, the nanostructured film has a configuration in which an asymmetrically coated palladium (Pd) film is coated on a periodic polyurethane acrylate (PUA) nanograting. The asymmetric Pd nanostructures, optimized by a finite element method simulation, swell upon reacting with H2 and thereby bend the PUA nanograting, changing the amount of transmitted light and the current output of the photovoltaic cell. Since the degree of warping is determined by the concentration of H2 gas, a wide concentration range of H2 (0.1-4.0%) can be detected by measuring the self-generated electrical current of the photovoltaic cell without external power. The normalized output current changes are ∼1.5%, ∼2.8%, ∼3.5%, ∼5.0%, ∼21.5%, and 25.3% when the concentrations of H2 gas are 0.1%, 0.5%, 1.0%, 1.6%, 2%, and 4%, respectively. Moreover, because Pd is highly chemically reactive to H2 and also because there is no electrical current applied through Pd, the proposed sensor can avoid device failure due to the breakage of the Pd sensing material, resulting in high reliability, and can show high selectivity against various gases such as carbon monoxide, hydrogen sulfide, nitrogen dioxide, and water vapor. Finally, using only ambient visible light, the sensor was modularized to produce an alarm in the presence of H2 gas, verifying a potential always-on H2 gas monitoring application.