Resolving the developmental mechanisms of cardiac microthrombosis of SARS-CoV-2 based on single-cell transcriptome analysis.

The coronavirus disease 2019 (COVID-19) outbreak caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) developed into a global health emergency. Systemic microthrombus caused by SARS-CoV-2 infection is a common complication in patients with COVID-19. Cardiac microthrombosis as a complication of SARS-CoV-2 infection is the primary cause of cardiac injury and death in patietns with severe COVID-19. In this study, we performed single-cell sequencing analysis of the right ventricular free wall tissue from healthy donors, patients who died during the hypercoagulable period of characteristic coagulation abnormality (CAC), and patients who died during the fibrinolytic period of CAC. We collected 61,187 cells enriched in 24 immune cell subsets and 13 cardiac-resident cell subsets. We found that in the course of SARS-CoV-2 infected heart microthrombus, MYO1EhighRASGEF1Bhighmonocyte-derived macrophages promoted hyperactivation of the immune system and initiated the extrinsic coagulation pathway by activating chemokines CCL3, CCL5. This series of events is the main cause of cardiac microthrombi following SARS-CoV-2 infection. In a SARS-CoV-2 infected heart microthrombus, excessive immune activation is accompanied by an increase in cellular iron content, which in turn promotes oxidative stress and intensifies intercellular competition. This induces cells to alter their metabolic environment, resulting in increased sugar uptake via the glycosaminoglycan synthesis pathway. In addition, high levels of reactive oxygen species generated by elevated iron levels promote increased endogenous malondialdehyde synthesis in a subpopulation of cardiac endothelial cells. This exacerbates endothelial cell dysfunction and exacerbates the coagulopathy process.

An Explainable Graph Neural Framework to Identify Cancer-Associated Intratumoral Microbial Communities.

Microbes are extensively present among various cancer tissues and play critical roles in carcinogenesis and treatment responses. However, the underlying relationships between intratumoral microbes and tumors remain poorly understood. Here, a MIcrobial Cancer-association Analysis using a Heterogeneous graph transformer (MICAH) to identify intratumoral cancer-associated microbial communities is presented. MICAH integrates metabolic and phylogenetic relationships among microbes into a heterogeneous graph representation. It uses a graph transformer to holistically capture relationships between intratumoral microbes and cancer tissues, which improves the explainability of the associations between identified microbial communities and cancers. MICAH is applied to intratumoral bacterial data across 5 cancer types and 5 fungi datasets, and its generalizability and reproducibility are demonstrated. After experimentally testing a representative observation using a mouse model of tumor-microbe-immune interactions, a result consistent with MICAH's identified relationship is observed. Source tracking analysis reveals that the primary known contributor to a cancer-associated microbial community is the organs affected by the type of cancer. Overall, this graph neural network framework refines the number of microbes that can be used for follow-up experimental validation from thousands to tens, thereby helping to accelerate the understanding of the relationship between tumors and intratumoral microbiomes.

Identification of candidate genes and genomic prediction of soybean fatty acid components in two soybean populations.

Soybean, a source of plant-derived lipids, contains an array of fatty acids essential for health. A comprehensive understanding of the fatty acid profiles in soybean is crucial for enhancing soybean cultivars and augmenting their qualitative attributes. Here, 180 F10 generation recombinant inbred lines (RILs), derived from the cross-breeding of the cultivated soybean variety 'Jidou 12' and the wild soybean 'Y9,' were used as primary experimental subjects. Using inclusive composite interval mapping (ICIM), this study undertook a quantitative trait locus (QTL) analysis on five distinct fatty acid components in the RIL population from 2019 to 2021. Concurrently, a genome-wide association study (GWAS) was conducted on 290 samples from a genetically diverse natural population to scrutinize the five fatty acid components during the same timeframe, thereby aiming to identify loci closely associated with fatty acid profiles. In addition, haplotype analysis and the Kyoto Encyclopedia of Genes and Genomes pathway analysis were performed to predict candidate genes. The QTL analysis elucidated 23 stable QTLs intricately associated with the five fatty acid components, exhibiting phenotypic contribution rates ranging from 2.78% to 25.37%. In addition, GWAS of the natural population unveiled 102 significant loci associated with these fatty acid components. The haplotype analysis of the colocalized loci revealed that Glyma.06G221400 on chromosome 6 exhibited a significant correlation with stearic acid content, with Hap1 showing a markedly elevated stearic acid level compared with Hap2 and Hap3. Similarly, Glyma.12G075100 on chromosome 12 was significantly associated with the contents of oleic, linoleic, and linolenic acids, suggesting its involvement in fatty acid biosynthesis. In the natural population, candidate genes associated with the contents of palmitic and linolenic acids were predominantly from the fatty acid metabolic pathway, indicating their potential role as pivotal genes in the critical steps of fatty acid metabolism. Furthermore, genomic selection (GS) for fatty acid components was conducted using ridge regression best linear unbiased prediction based on both random single nucleotide polymorphisms (SNPs) and SNPs significantly associated with fatty acid components identified by GWAS. GS accuracy was contingent upon the SNP set used. Notably, GS efficiency was enhanced when using SNPs derived from QTL mapping analysis and GWAS compared with random SNPs, and reached a plateau when the number of SNP markers exceeded 3,000. This study thus indicates that Glyma.06G221400 and Glyma.12G075100 are genes integral to the synthesis and regulatory mechanisms of fatty acids. It provides insights into the complex biosynthesis and regulation of fatty acids, with significant implications for the directed improvement of soybean oil quality and the selection of superior soybean varieties. The SNP markers delineated in this study can be instrumental in establishing an efficacious pipeline for marker-assisted selection and GS aimed at improving soybean fatty acid components.

Graph Fourier transform for spatial omics representation and analyses of complex organs.

Spatial omics technologies decipher functional components of complex organs at cellular and subcellular resolutions. We introduce Spatial Graph Fourier Transform (SpaGFT) and apply graph signal processing to a wide range of spatial omics profiling platforms to generate their interpretable representations. This representation supports spatially variable gene identification and improves gene expression imputation, outperforming existing tools in analyzing human and mouse spatial transcriptomics data. SpaGFT can identify immunological regions for B cell maturation in human lymph nodes Visium data and characterize variations in secondary follicles using in-house human tonsil CODEX data. Furthermore, it can be integrated seamlessly into other machine learning frameworks, enhancing accuracy in spatial domain identification, cell type annotation, and subcellular feature inference by up to 40%. Notably, SpaGFT detects rare subcellular organelles, such as Cajal bodies and Set1/COMPASS complexes, in high-resolution spatial proteomics data. This approach provides an explainable graph representation method for exploring tissue biology and function.

Identification of DNA motif pairs on paired sequences based on composite heterogeneous graph.

Motivation: The interaction between DNA motifs (DNA motif pairs) influences gene expression through partnership or competition in the process of gene regulation. Potential chromatin interactions between different DNA motifs have been implicated in various diseases. However, current methods for identifying DNA motif pairs rely on the recognition of single DNA motifs or probabilities, which may result in local optimal solutions and can be sensitive to the choice of initial values. A method for precisely identifying DNA motif pairs is still lacking. Results: Here, we propose a novel computational method for predicting DNA Motif Pairs based on Composite Heterogeneous Graph (MPCHG). This approach leverages a composite heterogeneous graph model to identify DNA motif pairs on paired sequences. Compared with the existing methods, MPCHG has greatly improved the accuracy of motifs prediction. Furthermore, the predicted DNA motifs demonstrate heightened DNase accessibility than the background sequences. Notably, the two DNA motifs forming a pair exhibit functional consistency. Importantly, the interacting TF pairs obtained by predicted DNA motif pairs were significantly enriched with known interacting TF pairs, suggesting their potential contribution to chromatin interactions. Collectively, we believe that these identified DNA motif pairs held substantial implications for revealing gene transcriptional regulation under long-range chromatin interactions.

Enhancer-driven gene regulatory networks inference from single-cell RNA-seq and ATAC-seq data.

Deciphering the intricate relationships between transcription factors (TFs), enhancers, and genes through the inference of enhancer-driven gene regulatory networks (eGRNs) is crucial in understanding gene regulatory programs in a complex biological system. This study introduces STREAM, a novel method that leverages a Steiner forest problem model, a hybrid biclustering pipeline, and submodular optimization to infer eGRNs from jointly profiled single-cell transcriptome and chromatin accessibility data. Compared to existing methods, STREAM demonstrates enhanced performance in terms of TF recovery, TF-enhancer linkage prediction, and enhancer-gene relation discovery. Application of STREAM to an Alzheimer's disease dataset and a diffuse small lymphocytic lymphoma dataset reveals its ability to identify TF-enhancer-gene relations associated with pseudotime, as well as key TF-enhancer-gene relations and TF cooperation underlying tumor cells.

Ability of Genomic Prediction to Bi-Parent-Derived Breeding Population Using Public Data for Soybean Oil and Protein Content.

Genomic selection (GS) is a marker-based selection method used to improve the genetic gain of quantitative traits in plant breeding. A large number of breeding datasets are available in the soybean database, and the application of these public datasets in GS will improve breeding efficiency and reduce time and cost. However, the most important problem to be solved is how to improve the ability of across-population prediction. The objectives of this study were to perform genomic prediction (GP) and estimate the prediction ability (PA) for seed oil and protein contents in soybean using available public datasets to predict breeding populations in current, ongoing breeding programs. In this study, six public datasets of USDA GRIN soybean germplasm accessions with available phenotypic data of seed oil and protein contents from different experimental populations and their genotypic data of single-nucleotide polymorphisms (SNPs) were used to perform GP and to predict a bi-parent-derived breeding population in our experiment. The average PA was 0.55 and 0.50 for seed oil and protein contents within the bi-parents population according to the within-population prediction; and 0.45 for oil and 0.39 for protein content when the six USDA populations were combined and employed as training sets to predict the bi-parent-derived population. The results showed that four USDA-cultivated populations can be used as a training set individually or combined to predict oil and protein contents in GS when using 800 or more USDA germplasm accessions as a training set. The smaller the genetic distance between training population and testing population, the higher the PA. The PA increased as the population size increased. In across-population prediction, no significant difference was observed in PA for oil and protein content among different models. The PA increased as the SNP number increased until a marker set consisted of 10,000 SNPs. This study provides reasonable suggestions and methods for breeders to utilize public datasets for GS. It will aid breeders in developing GS-assisted breeding strategies to develop elite soybean cultivars with high oil and protein contents.

A weighted two-stage sequence alignment framework to identify motifs from ChIP-exo data.

In this study, we introduce TESA (weighted two-stage alignment), an innovative motif prediction tool that refines the identification of DNA-binding protein motifs, essential for deciphering transcriptional regulatory mechanisms. Unlike traditional algorithms that rely solely on sequence data, TESA integrates the high-resolution chromatin immunoprecipitation (ChIP) signal, specifically from ChIP-exonuclease (ChIP-exo), by assigning weights to sequence positions, thereby enhancing motif discovery. TESA employs a nuanced approach combining a binomial distribution model with a graph model, further supported by a "bookend" model, to improve the accuracy of predicting motifs of varying lengths. Our evaluation, utilizing an extensive compilation of 90 prokaryotic ChIP-exo datasets from proChIPdb and 167 H. sapiens datasets, compared TESA's performance against seven established tools. The results indicate TESA's improved precision in motif identification, suggesting its valuable contribution to the field of genomic research.

De novo genome assembly of a high-protein soybean variety HJ117.

OBJECTIVES: Soybean is an important feed and oil crop in the world due to its high protein and oil content. China has a collection of more than 43,000 soybean germplasm resources, which provides a rich genetic diversity for soybean breeding. However, the rich genetic diversity poses great challenges to the genetic improvement of soybean. This study reports on the de novo genome assembly of HJ117, a soybean variety with high protein content of 52.99%. These data will prove to be valuable resources for further soybean quality improvement research, and will aid in the elucidation of regulatory mechanisms underlying soybean protein content. DATA DESCRIPTION: We generated a contiguous reference genome of 1041.94 Mb for HJ117 using a combination of Illumina short reads (23.38 Gb) and PacBio long reads (25.58 Gb), with high-quality sequence coverage of approximately 22.44× and 24.55×, respectively. HJ117 was developed through backcross breeding, using Jidou 12 as the recurrent parent and Chamoshidou as the donor parent. The assembly was further assisted by 114.5 Gb Hi-C data (109.9×), resulting in a contig N50 of 19.32 Mb and scaffold N50 of 51.43 Mb. Notably, Core Eukaryotic Genes Mapping Approach (CEGMA) assessment and Benchmarking Universal Single-Copy Orthologs (BUSCO) assessment results indicated that most core eukaryotic genes (97.18%) and genes in the BUSCO dataset (99.4%) were identified, and 96.44% of the genomic sequences were anchored onto twenty pseudochromosomes.

Highly accurate diagnosis of pancreatic cancer by integrative modeling using gut microbiome and exposome data.

The noninvasive detection of pancreatic ductal adenocarcinoma (PDAC) remains an immense challenge. In this study, we proposed a robust, accurate, and noninvasive classifier, namely Multi-Omics Co-training Graph Convolutional Networks (MOCO-GCN). It achieved high accuracy (0.9 ± 0.06), F1 score (0.9± 0.07), and AUROC (0.89± 0.08), surpassing contemporary approaches. The performance of model was validated on an external cohort of German PDAC patients. Additionally, we discovered that the exposome may impact PDAC development through its complex interplay with gut microbiome by mediation analysis. For example, Fusobacterium hwasookii nucleatum, known for its ability to induce inflammatory responses, may serve as a mediator for the impact of rheumatoid arthritis on PDAC. Overall, our study sheds light on how exposome and microbiome in concert could contribute to PDAC development, and enable PDAC diagnosis with high fidelity and interpretability.

Graph Fourier transform for spatial omics representation and analyses of complex organs.

Spatial omics technologies are capable of deciphering detailed components of complex organs or tissue in cellular and subcellular resolution. A robust, interpretable, and unbiased representation method for spatial omics is necessary to illuminate novel investigations into biological functions, whereas a mathematical theory deficiency still exists. We present SpaGFT (Spatial Graph Fourier Transform), which provides a unique analytical feature representation of spatial omics data and elucidates molecular signatures linked to critical biological processes within tissues and cells. It outperformed existing tools in spatially variable gene prediction and gene expression imputation across human/mouse Visium data. Integrating SpaGFT representation into existing machine learning frameworks can enhance up to 40% accuracy of spatial domain identification, cell type annotation, cell-to-spot alignment, and subcellular hallmark inference. SpaGFT identified immunological regions for B cell maturation in human lymph node Visium data, characterized secondary follicle variations from in-house human tonsil CODEX data, and detected extremely rare subcellular organelles such as Cajal body and Set1/COMPASS. This new method lays the groundwork for a new theoretical model in explainable AI, advancing our understanding of tissue organization and function.

MarsGT: Multi-omics analysis for rare population inference using single-cell graph transformer.

Rare cell populations are key in neoplastic progression and therapeutic response, offering potential intervention targets. However, their computational identification and analysis often lag behind major cell types. To fill this gap, we introduce MarsGT: Multi-omics Analysis for Rare population inference using a Single-cell Graph Transformer. It identifies rare cell populations using a probability-based heterogeneous graph transformer on single-cell multi-omics data. MarsGT outperforms existing tools in identifying rare cells across 550 simulated and four real human datasets. In mouse retina data, it reveals unique subpopulations of rare bipolar cells and a Müller glia cell subpopulation. In human lymph node data, MarsGT detects an intermediate B cell population potentially acting as lymphoma precursors. In human melanoma data, it identifies a rare MAIT-like population impacted by a high IFN-I response and reveals the mechanism of immunotherapy. Hence, MarsGT offers biological insights and suggests potential strategies for early detection and therapeutic intervention of disease.

Inference of disease-associated microbial gene modules based on metagenomic and metatranscriptomic data.

The identification of microbial characteristics associated with diseases is crucial for disease diagnosis and therapy. However, the presence of heterogeneity, high dimensionality, and large amounts of microbial data presents tremendous challenges in discovering key microbial features. In this paper, we present IDAM, a novel computational method for inferring disease-associated gene modules from metagenomic and metatranscriptomic data. This method integrates gene context conservation (uber-operons) and regulatory mechanisms (gene co-expression patterns) within a mathematical graph model to explore gene modules associated with specific diseases. It alleviates reliance on prior meta-data. We applied IDAM to publicly available datasets from inflammatory bowel disease, melanoma, type 1 diabetes mellitus, and irritable bowel syndrome. The results demonstrated the superior performance of IDAM in inferring disease-associated characteristics compared to existing popular tools. Furthermore, we showcased the high reproducibility of the gene modules inferred by IDAM using independent cohorts with inflammatory bowel disease. We believe that IDAM can be a highly advantageous method for exploring disease-associated microbial characteristics. The source code of IDAM is freely available at https://github.com/OSU-BMBL/IDAM, and the web server can be accessed at https://bmblx.bmi.osumc.edu/idam/.

MarsGT: Multi-omics analysis for rare population inference using single-cell graph transformer.

Rare cell populations are key in neoplastic progression and therapeutic response, offering potential intervention targets. However, their computational identification and analysis often lag behind major cell types. To fill this gap, we introduced MarsGT: Multi-omics Analysis for Rare population inference using Single-cell Graph Transformer. It identifies rare cell populations using a probability-based heterogeneous graph transformer on single-cell multi-omics data. MarsGT outperformed existing tools in identifying rare cells across 400 simulated and four real human datasets. In mouse retina data, it revealed unique subpopulations of rare bipolar cells and a Müller glia cell subpopulation. In human lymph node data, MarsGT detected an intermediate B cell population potentially acting as lymphoma precursors. In human melanoma data, it identified a rare MAIT-like population impacted by a high IFN-I response and revealed the mechanism of immunotherapy. Hence, MarsGT offers biological insights and suggests potential strategies for early detection and therapeutic intervention of disease.

Identification of candidate genes for soybean seed coat-related traits using QTL mapping and GWAS.

Seed coat color is a typical morphological trait that can be used to reveal the evolution of soybean. The study of seed coat color-related traits in soybeans is of great significance for both evolutionary theory and breeding practices. In this study, 180 F10 recombinant inbred lines (RILs) derived from the cross between the yellow-seed coat cultivar Jidou12 (ZDD23040, JD12) and the wild black-seed coat accession Y9 (ZYD02739) were used as materials. Three methods, single-marker analysis (SMA), interval mapping (IM), and inclusive composite interval mapping (ICIM), were used to identify quantitative trait loci (QTLs) controlling seed coat color and seed hilum color. Simultaneously, two genome-wide association study (GWAS) models, the generalized linear model (GLM) and mixed linear model (MLM), were used to jointly identify seed coat color and seed hilum color QTLs in 250 natural populations. By integrating the results from QTL mapping and GWAS analysis, we identified two stable QTLs (qSCC02 and qSCC08) associated with seed coat color and one stable QTL (qSHC08) related to seed hilum color. By combining the results of linkage analysis and association analysis, two stable QTLs (qSCC02, qSCC08) for seed coat color and one stable QTL (qSHC08) for seed hilum color were identified. Upon further investigation using Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, we validated the previous findings that two candidate genes (CHS3C and CHS4A) reside within the qSCC08 region and identified a new QTL, qSCC02. There were a total of 28 candidate genes in the interval, among which Glyma.02G024600, Glyma.02G024700, and Glyma.02G024800 were mapped to the glutathione metabolic pathway, which is related to the transport or accumulation of anthocyanin. We considered the three genes as potential candidate genes for soybean seed coat-related traits. The QTLs and candidate genes detected in this study provide a foundation for further understanding the genetic mechanisms underlying soybean seed coat color and seed hilum color and are of significant value in marker-assisted breeding.

Dysfunction of GmVPS8a causes compact plant architecture in soybean.

Vacuolar Protein Sorting 8 (Vps8) protein is a specific subunit of the class C core vacuole/endosome tethering (CORVET) complex that plays a key role in endosomal trafficking in yeast (Saccharomyces cerevisiae). However, its functions remain largely unclear in plant vegetative growth. Here, we identified a soybean (Glycine max) T4219 mutant characterized with compact plant architecture. Map-based cloning targeted to a candidate gene GmVPS8a (Glyma.07g049700) and further found that two nucleotides deletion in the first exon of GmVPS8a causes a premature termination of the encoded protein in the T4219 mutant. Its functions were validated by CRISPR/Cas9-engineered mutation in the GmVPS8a gene that recapitulated the T4219 mutant phenotypes. Furthermore, NbVPS8a-silenced tobacco (Nicotiana benthamiana) plants exhibited similar phenotypes to the T4219 mutant, suggesting its conserved roles in plant growth. The GmVPS8a is widely expressed in multiple organs and its protein interacts with GmAra6a and GmRab5a. Combined analysis of transcriptomic and proteomic data revealed that dysfunction of GmVPS8a mainly affects pathways on auxin signal transduction, sugar transport and metabolism, and lipid metabolism. Collectively, our work reveals the function of GmVPS8a in plant architecture, which may extend a new way for genetic improvement of ideal plant-architecture breeding in soybean and other crops.

Single-cell biological network inference using a heterogeneous graph transformer.

Single-cell multi-omics (scMulti-omics) allows the quantification of multiple modalities simultaneously to capture the intricacy of complex molecular mechanisms and cellular heterogeneity. Existing tools cannot effectively infer the active biological networks in diverse cell types and the response of these networks to external stimuli. Here we present DeepMAPS for biological network inference from scMulti-omics. It models scMulti-omics in a heterogeneous graph and learns relations among cells and genes within both local and global contexts in a robust manner using a multi-head graph transformer. Benchmarking results indicate DeepMAPS performs better than existing tools in cell clustering and biological network construction. It also showcases competitive capability in deriving cell-type-specific biological networks in lung tumor leukocyte CITE-seq data and matched diffuse small lymphocytic lymphoma scRNA-seq and scATAC-seq data. In addition, we deploy a DeepMAPS webserver equipped with multiple functionalities and visualizations to improve the usability and reproducibility of scMulti-omics data analysis.

Computational methods and challenges in analyzing intratumoral microbiome data.

The human microbiome is intimately related to cancer biology and plays a vital role in the efficacy of cancer treatments, including immunotherapy. Extraordinary evidence has revealed that several microbes influence tumor development through interaction with the host immune system, that is, immuno-oncology-microbiome (IOM). This review focuses on the intratumoral microbiome in IOM and describes the available data and computational methods for discovering biological insights of microbial profiling from host bulk, single-cell, and spatial sequencing data. Critical challenges in data analysis and integration are discussed. Specifically, the microorganisms associated with cancer and cancer treatment in the context of IOM are collected and integrated from the literature. Lastly, we provide our perspectives for future directions in IOM research.

CEMIG: prediction of the cis-regulatory motif using the de Bruijn graph from ATAC-seq.

Sequence motif discovery algorithms enhance the identification of novel deoxyribonucleic acid sequences with pivotal biological significance, especially transcription factor (TF)-binding motifs. The advent of assay for transposase-accessible chromatin using sequencing (ATAC-seq) has broadened the toolkit for motif characterization. Nonetheless, prevailing computational approaches have focused on delineating TF-binding footprints, with motif discovery receiving less attention. Herein, we present Cis rEgulatory Motif Influence using de Bruijn Graph (CEMIG), an algorithm leveraging de Bruijn and Hamming distance graph paradigms to predict and map motif sites. Assessment on 129 ATAC-seq datasets from the Cistrome Data Browser demonstrates CEMIG's exceptional performance, surpassing three established methodologies on four evaluative metrics. CEMIG accurately identifies both cell-type-specific and common TF motifs within GM12878 and K562 cell lines, demonstrating its comparative genomic capabilities in the identification of evolutionary conservation and cell-type specificity. In-depth transcriptional and functional genomic studies have validated the functional relevance of CEMIG-identified motifs across various cell types. CEMIG is available at https://github.com/OSU-BMBL/CEMIG, developed in C++ to ensure cross-platform compatibility with Linux, macOS and Windows operating systems.

Design and Analysis of Area and Energy Efficient Reconfigurable Cryptographic Accelerator for Securing IoT Devices.

Achieving low-cost and high-performance network security communication is necessary for Internet of Things (IoT) devices, including intelligent sensors and mobile robots. Designing hardware accelerators to accelerate multiple computationally intensive cryptographic primitives in various network security protocols is challenging. Different from existing unified reconfigurable cryptographic accelerators with relatively low efficiency and high latency, this paper presents design and analysis of a reconfigurable cryptographic accelerator consisting of a reconfigurable cipher unit and a reconfigurable hash unit to support widely used cryptographic algorithms for IoT Devices, which require block ciphers and hash functions simultaneously. Based on a detailed and comprehensive algorithmic analysis of both the block ciphers and hash functions in terms of basic algorithm structures and common cryptographic operators, the proposed reconfigurable cryptographic accelerator is designed by reusing key register files and operators to build unified data paths. Both the reconfigurable cipher unit and the reconfigurable hash unit contain a unified data path to implement Data Encryption Standard (DES)/Advanced Encryption Standard (AES)/ShangMi 4 (SM4) and Secure Hash Algorithm-1 (SHA-1)/SHA-256/SM3 algorithms, respectively. A reconfigurable S-Box for AES and SM4 is designed based on the composite field Galois field (GF) GF(((22)2)2), which significantly reduces hardware overhead and power consumption compared with the conventional implementation by look-up tables. The experimental results based on 65-nm application-specific integrated circuit (ASIC) implementation show that the achieved energy efficiency and area efficiency of the proposed design is 441 Gbps/W and 37.55 Gbps/mm2, respectively, which is suitable for IoT devices with limited battery and form factor. The result of delay analysis also shows that the number of delay cycles of our design can be reduced by 83% compared with the state-of-the-art design, which shows that the proposed design is more suitable for applications including 5G/Wi-Fi/ZigBee/Ethernet network standards to accelerate block ciphers and hash functions simultaneously.