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Abstract Genetic parasites, including viruses and transposons, exploit components from the host for their own replication. However, little is known about virus-transposon interactions within host cells. Here, we discover a strategy where human cytomegalovirus (HCMV) hijacks L1 retrotransposon encoded protein during its replication cycle. HCMV infection upregulates L1 expression by enhancing both the expression of L1-activating transcription factors, YY1 and RUNX3, and the chromatin accessibility of L1 promoter regions. Increased L1 expression, in turn, promotes HCMV replicative fitness. Affinity proteomics reveals UL44, HCMV DNA polymerase subunit, as the most abundant viral binding protein of the L1 ribonucleoprotein (RNP) complex. UL44 directly interacts with L1 ORF2p, inducing DNA damage responses in replicating HCMV compartments. While increased L1-induced mutagenesis is not observed in HCMV for genetic adaptation, the interplay between UL44 and ORF2p accelerates viral DNA replication by alleviating replication stress. Our findings shed light on how HCMV exploits host retrotransposons for enhanced viral fitness.

A powder-bed-based additive manufacturing process called electron beam melting (EBM) is defined by high temperature gradients during solidification, which produces an extremely fine microstructure compared to the traditional cast material. However, porosity and segregation defects are still present on a smaller scale which may lead to a reduction in mechanical properties. It is important to have a better knowledge of the influence of post-fabrication treatments on the microstructure and mechanical characteristics before the use of additive manufacturing parts in specific applications. In this study, the effects of solution heat treatment (SHT) and hot isostatic pressing (HIP) on the microstructure and mechanical properties of Ti-6Al-4V alloy fabricated by the EBM process have been investigated. The SHT and HIP treatments can significantly improve the ductility of EBM Ti-6Al-4V due to the coarsening of α laths and the formation of β grains.

Multi-gene assays have been widely used to predict the recurrence risk for hormone receptor (HR)-positive breast cancer patients. However, these assays lack explanatory power regarding the underlying mechanisms of the recurrence risk. To address this limitation, we proposed a novel multi-layered knowledge graph neural network for the multi-gene assays. Our model elucidated the regulatory pathways of assay genes and utilized an attention-based graph neural network to predict recurrence risk while interpreting transcriptional subpathways relevant to risk prediction. Evaluation on three multi-gene assays—Oncotype DX, Prosigna, and EndoPredict—using SCAN-B dataset demonstrated the efficacy of our method. Through interpretation of attention weights, we found that all three assays are mainly regulated by signaling pathways driving cancer proliferation especially RTK-ERK-ETS-mediated cell proliferation for breast cancer recurrence. In addition, our analysis highlighted that the important regulatory subpathways remain consistent across different knowledgebases used for constructing the multi-level knowledge graph. Furthermore, through attention analysis, we demonstrated the biological significance and clinical relevance of these subpathways in predicting patient outcomes. The source code is available at http://biohealth.snu.ac.kr/software/ExplainableMLKGNN.

Abstract Background Lysosomal dysfunction (LD) impacts cytokine regulation, inflammation, and immune responses, influencing the development and progression of cancer. Inflammation is implicated in the pathogenesis of myeloproliferative neoplasm (MPN). With a hypothesis that LD significantly contributes to MPN carcinogenesis by inducing abnormal inflammation, our objective was to elucidate the pathophysiological mechanisms of MPN arising from an LD background. Methods Genotyping of the LD background was performed in a cohort of MPN patients (n = 190) and healthy controls (n = 461). Logistic regression modeling, utilizing genotype data, was employed to estimate the correlation between LD and MPN. Whole transcriptome sequencing (WTS) (LD carriers = 8, non‐carriers = 6) and single‐cell RNA sequencing data (LD carriers = 2, non‐carriers = 2, healthy controls = 2) were generated and analyzed. Results A higher variant frequency of LD was observed in MPN compared to healthy controls (healthy, 4.9%; MPN, 7.8%), with the highest frequency seen in polycythemia vera (PV) (odds ratio = 2.33, p = 0.03). WTS revealed that LD carriers exhibited upregulated inflammatory cytokine ligand–receptor genes, pathways, and network modules in MPNs compared to non‐carriers. At the single‐cell level, there was monocyte expansion and elevation of cytokine ligand–receptor interactions, inflammatory transcription factors, and network modules centered on monocytes. Notably, Oncostatin‐M (OSM) consistently emerged as a candidate molecule involved in the pathogenesis of LD‐related PV. Conclusions In summary, an LD background is prevalent in MPN patients and leads to increased cytokine dysregulation and inflammation. OSM, as one of the potential molecules, plays a crucial role in PV pathogenesis by impairing lysosomal function.

This study explored the correlation between microstructure and mechanical properties in additively manufactured Inconel 718 superalloys with low and high electron beam (EB) currents. The relative densities of the as-built Inconel 718 superalloys with low EB currents ranged from 99.45 to 99.79 %, while the alloys with high EB currents demonstrated relatively high relative densities ranging from 99.82 to 99.96 %. The microstructures of the alloys with low EB currents had columnar grain microstructures with a strong texture parallel to the built direction (BD). The high-angle grain boundary density of the alloys with low EB currents was lower than with high EB currents. In addition, the alloys contained fine γ″ precipitates with an average size of 32 nm. Although the fracture-initiating sites for the alloys consisted of defects, such as micro-cracks and gas-entrapped pores, the alloys with low EB currents demonstrated excellent tensile strength and good uniform elongation due to the single-crystal-like microstructures and fine γ″ precipitates. The γ″ precipitates easily grew in deep and narrow melt pools due to sufficient thermal transfer with increasing penetration depth. However, the growth of γ″ precipitates was limited in the shallow and wide melt pools, and the shallow and wide melt pools resulted in single-crystal-like microstructures and strong textures parallel to the BD. Therefore, the morphology of the melt pool, determined by the EB current, played an important role in the single-crystalline microstructure and growth of the γ” precipitates.

Abstract To respond to the external environmental changes for survival, bacteria regulates expression of a number of genes including transcription factors (TFs). To characterize complex biological phenomena, a biological system-level approach is necessary. Here we utilized six computational biology methods to infer regulatory network and to characterize underlying biologically mechanisms relevant to radiation-resistance. In particular, we inferred gene regulatory network (GRN) and operons of radiation-resistance bacterium Spirosoma montaniterrae DY10 $$^T$$ T and identified the major regulators for radiation-resistance. Our results showed that DNA repair and reactive oxygen species (ROS) scavenging mechanisms are key processes and Crp/Fnr family transcriptional regulator works as a master regulatory TF in early response to radiation.

Existing deep learning-based approaches for the prediction of gene expression by histone modifications (HMs) can only focus on narrow and linear genomic regions around promoters. Here, the authors address these problems by developing a transformer-based deep learning architecture named Chromoformer.

Phased DNA methylation states within bisulfite sequencing reads are valuable source of information that can be used to estimate epigenetic diversity across cells as well as epigenomic instability in individual cells. Various measures capturing the heterogeneity of DNA methylation states have been proposed for a decade. However, in routine analyses on DNA methylation, this heterogeneity is often ignored by computing average methylation levels at CpG sites, even though such information exists in bisulfite sequencing data in the form of phased methylation states, or methylation patterns. In this study, to facilitate the application of the DNA methylation heterogeneity measures in downstream epigenomic analyses, we present a Rust-based, extremely fast and lightweight bioinformatics toolkit called Metheor. As the analysis of DNA methylation heterogeneity requires the examination of pairs or groups of CpGs throughout the genome, existing softwares suffer from high computational burden, which almost make a large-scale DNA methylation heterogeneity studies intractable for researchers with limited resources. In this study, we benchmark the performance of Metheor against existing code implementations for DNA methylation heterogeneity measures in three different scenarios of simulated bisulfite sequencing datasets. Metheor was shown to dramatically reduce the execution time up to 300-fold and memory footprint up to 60-fold, while producing identical results with the original implementation, thereby facilitating a large-scale study of DNA methylation heterogeneity profiles. To demonstrate the utility of the low computational burden of Metheor, we show that the methylation heterogeneity profiles of 928 cancer cell lines can be computed with standard computing resources. With those profiles, we reveal the association between DNA methylation heterogeneity and various omics features. Source code for Metheor is at https://github.com/dohlee/metheor and is freely available under the GPL-3.0 license.

Cells survive and proliferate through complex interactions among diverse molecules across multiomics layers. Conventional experimental approaches for identifying these interactions have built a firm foundation for molecular biology, but their scalability is gradually becoming inadequate compared to the rapid accumulation of multiomics data measured by high-throughput technologies. Therefore, the need for data-driven computational modeling of interactions within cells has been highlighted in recent years. The complexity of multiomics interactions is primarily due to their nonlinearity. That is, their accurate modeling requires intricate conditional dependencies, synergies, or antagonisms between considered genes or proteins, which retard experimental validations. Artificial intelligence (AI) technologies, including deep learning models, are optimal choices for handling complex nonlinear relationships between features that are scalable and produce large amounts of data. Thus, they have great potential for modeling multiomics interactions. Although there exist many AI-driven models for computational biology applications, relatively few explicitly incorporate the prior knowledge within model architectures or training procedures. Such guidance of models by domain knowledge will greatly reduce the amount of data needed to train models and constrain their vast expressive powers to focus on the biologically relevant space. Therefore, it can enhance a model’s interpretability, reduce spurious interactions, and prove its validity and utility. Thus, to facilitate further development of knowledge-guided AI technologies for the modeling of multiomics interactions, here we review representative bioinformatics applications of deep learning models for multiomics interactions developed to date by categorizing them by guidance mode.

The mechanical properties of the parts produced by binder jet printing (BJP) are often considerably inferior to those of the parts produced by traditional manufacturing processes because BJP parts retain at best about 95% of theoretical density after full sintering. To address this deficiency of BJP, a new processing protocol was developed, which enables us to produce fully dense parts under an isothermal sintering condition. This paper compares the mechanical properties of the stainless steel 420 (SS420) parts produced by the several variations of this new protocol against commercially available cold rolled SS420 parts with and without the heat-treatment under the similar sintering condition used in the new protocol. The mechanical properties examined include elastic modulus and both static and fatigue strengths. The only minor drawback of the new protocol is the dimensional shrinkage occurring during sintering. However, this can be easily compensated by printing the part geometry about 15% larger in the linear dimension with the starting powder used in this study. The final results showed the excellent static and fatigue strengths of the SS420 specimens produced with the new BJP protocol, implicating the potential structural applications of the BJP parts.

Many vehicles are connected to the Internet, and big data are continually created. Various studies have been conducted involving the development of artificial intelligence, machine learning technology, and big data frameworks. The analysis of smart mobility big data is essential and helps to address problems that arise as society faces increased future mobility. In this paper, we analyze application issues such as personal information leakage and data visualization due to increased data exchange in detail, as well as approaches focusing on analyses exploiting machine learning and architecture research exploiting big data frameworks, such as Apache Hadoop, Apache Spark, and Apache Kafka. Finally, future research directions and open challenges are discussed.

Abstract Cellular stages of biological processes have been characterized using fluorescence-activated cell sorting and genetic perturbations, charting a limited landscape of cellular states. Time series transcriptome data can help define new cellular states at the molecular level since the analysis of transcriptional changes can provide information on cell states and transitions. However, existing methods for inferring cell states from transcriptome data use additional information such as prior knowledge on cell types or cell-type-specific markers to reduce the complexity of data. In this study, we present a novel time series clustering framework to infer TRAnscriptomic Cellular States (TRACS) only from time series transcriptome data by integrating Gaussian process regression, shape-based distance, and ranked pairs algorithm in a single computational framework. TRACS determines patterns that correspond to hidden cellular states by clustering gene expression data. TRACS was used to analyse single-cell and bulk RNA sequencing data and successfully generated cluster networks that reflected the characteristics of key stages of biological processes. Thus, TRACS has a potential to help reveal unknown cellular states and transitions at the molecular level using only time series transcriptome data. TRACS is implemented in Python and available at http://github.com/BML-cbnu/TRACS/ .

Pharmacogenomics is the study of how genes affect a person's response to drugs. Thus, understanding the effect of drug at the molecular level can be helpful in both drug discovery and personalized medicine. Over the years, transcriptome data upon drug treatment has been collected and several databases compiled before drug treatment cancer cell multi-omics data with drug sensitivity (IC50, AUC) or time-series transcriptomic data after drug treatment. However, analyzing transcriptome data upon drug treatment is challenging since more than 20,000 genes interact in complex ways. In addition, due to the difficulty of both time-series analysis and multi-omics integration, current methods can hardly perform analysis of databases with different data characteristics. One effective way is to interpret transcriptome data in terms of well-characterized biological pathways. Another way is to leverage state-of-the-art methods for multi-omics data integration. In this paper, we developed Drug Response analysis Integrating Multi-omics and time-series data (DRIM), an integrative multi-omics and time-series data analysis framework that identifies perturbed sub-pathways and regulation mechanisms upon drug treatment. The system takes drug name and cell line identification numbers or user's drug control/treat time-series gene expression data as input. Then, analysis of multi-omics data upon drug treatment is performed in two perspectives. For the multi-omics perspective analysis, IC50-related multi-omics potential mediator genes are determined by embedding multi-omics data to gene-centric vector space using a tensor decomposition method and an autoencoder deep learning model. Then, perturbed pathway analysis of potential mediator genes is performed. For the time-series perspective analysis, time-varying perturbed sub-pathways upon drug treatment are constructed. Additionally, a network involving transcription factors (TFs), multi-omics potential mediator genes, and perturbed sub-pathways is constructed, and paths to perturbed pathways from TFs are determined by an influence maximization method. To demonstrate the utility of our system, we provide analysis results of sub-pathway regulatory mechanisms in breast cancer cell lines of different drug sensitivity. DRIM is available at: http://biohealth.snu.ac.kr/software/DRIM/.

Epigenetic gene regulation is a major control mechanism of gene expression. Most existing methods for modeling control mechanisms of gene expression use only a single epigenetic marker and very few methods are successful in modeling complex mechanisms of gene regulations using multiple epigenetic markers on transcriptional regulation. In this paper, we propose a multi-attention based deep learning model that integrates multiple markers to characterize complex gene regulation mechanisms. In experiments with 18 cell line multi-omics data, our proposed model predicted the gene expression level more accurately than the state-of-the-art model. Moreover, the model successfully revealed cell-type-specific gene expression control mechanisms. Finally, the model was used to identify genes enriched for specific cell types in terms of their functions and epigenetic regulation.

Abstract Background Waste animal fat is a promising feedstock to replace vegetable oil that widely used in commercial biodiesel process, however the high content of free fatty acid in waste fat makes it unfeasible to be processed with commercial base-catalytic process. Enzymatic process is preferable to convert waste fat into biodiesel since enzyme can catalyze both esterification of free fatty acid and transesterification of triglyceride. However, enzymatic reaction still has some drawbacks such as lower reaction rates than base-catalyzed transesterification and the limitation of reactant concentration due to the enzyme inhibition of methanol. Supercritical CO2 is a promising reaction media for enzyme-catalyzed transesterification to overcome those drawbacks. Result The transesterification of waste animal fat was carried out in supercritical CO2 with varied concentration of feedstock and methanol in CO2. The CO2 to feedstock mass ratio of 10:1 showed the highest yield compared to other ratios, and the highest FAME yield obtained from waste animal fat was 78%. The methanol concentration effect was also observed with variation 12%, 14%, and 16% of methanol to feedstock ratio. The best yield was 87% obtained at the CO2 to feedstock ratio of 10: 1 and at the methanol to feedstock ratio of 14% after 6 h of reaction. Conclusion Enzymatic transesterification to produce biodiesel from waste animal fat in supercritical fluid media is a potential method for commercialization since it could enhance enzyme activity due to supercritical fluid properties to remove mass transfer limitation. The high yield of FAME when using high mass ratio of CO2 to oil showed that supercritical CO2 could increase the reaction and mass transfer rate while reducing methanol toxicity to enzyme activity. The increase of methanol concentration also increased the FAME yield because it might shift the reaction equilibrium to FAME production. This finding describes that the application of supercritical CO2 in the enzymatic reaction enables the application of simple process such as a packed-bed reactor.

This article examines distinct trajectories of childhood exposure to poverty and provides estimates of their effect on high school graduation. The analysis incorporates three key insights from the life course and human capital formation literatures: (1) the temporal dimensions of exposure to poverty, that is, timing, duration, stability, and sequencing, are confounded with one another; (2) age-varying exposure to poverty not only affects, but also is affected by, other factors that vary with age; and (3) the effect of poverty trajectories is heterogeneous across racial and ethnic groups. Results from the Children of the National Longitudinal Survey of Youth show that any extended exposures to poverty substantially lower children’s odds of graduating from high school. Persistent, early, and middle-to-late childhood exposures to poverty reduce the odds of high school graduation by 77 percent, 55 percent, and 58 percent, respectively, compared to no childhood exposure to poverty. The findings thus suggest that the impact of poverty trajectories is insensitive to observed age-varying confounders. These impacts are more pronounced for white children than for black and Hispanic children.

A large body of literature has documented a negative correlation between teenage childbearing and teen mothers' socioeconomic outcomes, yet researchers continue to disagree as to whether the association represents a true causal effect. This article extends the extant literature by employing propensity score matching with a sensitivity analysis using Rosenbaum bounds. The analysis of recent cohort data from the National Longitudinal Study of Adolescent Health shows that (1) teenage childbearing has modest but significant negative effects on early socioeconomic outcomes and (2) unobserved covariates would have to be more powerful than known covariates to nullify the propensity score matching estimates. The author concludes by suggesting that more research should be done to address unobserved heterogeneity and the long-term effects of teenage childbearing for this young cohort.