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The recent advances in high-throughput single-cell sequencing have created an urgent demand for computational models which can address the high complexity of single-cell multiomics data. Meticulous single-cell multiomics integration models are required to avoid biases towards a specific modality and overcome sparsity. Batch effects obfuscating biological signals must also be taken into account. Here, we introduce a new single-cell multiomics integration model, Single-cell Multiomics Autoencoder Integration (scMaui) based on variational product-of-experts autoencoders and adversarial learning. scMaui calculates a joint representation of multiple marginal distributions based on a product-of-experts approach which is especially effective for missing values in the modalities. Furthermore, it overcomes limitations seen in previous VAE-based integration methods with regard to batch effect correction and restricted applicable assays. It handles multiple batch effects independently accepting both discrete and continuous values, as well as provides varied reconstruction loss functions to cover all possible assays and preprocessing pipelines. We demonstrate that scMaui achieves superior performance in many tasks compared to other methods. Further downstream analyses also demonstrate its potential in identifying relations between assays and discovering hidden subpopulations.

DNA methylation (DNAm) is a key epigenetic mark that shows profound alterations in cancer. Read-level methylomes enable more in-depth analyses, due to their broad genomic coverage and preservation of rare cell-type signals, compared to summarized data such as 450K/EPIC microarrays. Here, we propose MethylBERT, a Transformer-based model for read-level methylation pattern classification. MethylBERT identifies tumour-derived sequence reads based on their methylation patterns and local genomic sequence, and estimates tumour cell fractions within bulk samples. In our evaluation, MethylBERT outperforms existing deconvolution methods and demonstrates high accuracy regardless of methylation pattern complexity, read length and read coverage. Moreover, we show its applicability to cell-type deconvolution as well as non-invasive early cancer diagnostics using liquid biopsy samples. MethylBERT represents a significant advancement in read-level methylome analysis and enables accurate tumour purity estimation. The broad applicability of MethylBERT will enhance studies on both tumour and non-cancerous bulk methylomes. Mapping DNA methylomes in single cells is challenging, and thus studies using bulk samples remain common. Here, authors develop a transformer-based method for methylation pattern analysis to enhance bulk methylome deconvolution and cancer detection.

White matter hyperintensities (WMH) appear as regions of abnormally high signal intensity on T2-weighted magnetic resonance image (MRI) sequences. In particular, WMH have been noteworthy in age-related neuroscience for being a crucial biomarker for all types of dementia and brain aging processes. The automatic WMH segmentation is challenging because of their variable intensity range, size and shape. U-Net tackles this problem through the dense prediction and has shown competitive performances not only on WMH segmentation/detection but also on varied image segmentation tasks. However, its network architecture is high complex. In this study, we propose the use of Saliency U-Net and Irregularity map (IAM) to decrease the U-Net architectural complexity without performance loss. We trained Saliency U-Net using both: a T2-FLAIR MRI sequence and its correspondent IAM. Since IAM guides locating image intensity irregularities, in which WMH are possibly included, in the MRI slice, Saliency U-Net performs better than the original U-Net trained only using T2-FLAIR. The best performance was achieved with fewer parameters and shorter training time. Moreover, the application of dilated convolution enhanced Saliency U-Net by recognizing the shape of large WMH more accurately through multi-context learning. This network named Dilated Saliency U-Net improved Dice coefficient score to 0.5588 which was the best score among our experimental models, and recorded a relatively good sensitivity of 0.4747 with the shortest training time and the least number of parameters. In conclusion, based on our experimental results, incorporating IAM through Dilated Saliency U-Net resulted an appropriate approach for WMH segmentation.

5-Aminopentanol (5-AP) is a valuable amino alcohol with potential applications in polymer synthesis and bioplastics. Conventional production methods rely on petroleum-based feedstocks and metal catalysts, which raise environmental and sustainability concerns. In this study, a de novo biosynthetic pathway for 5-AP production from l- lysine was developed in Escherichia coli . The engineered pathway consisted of lysine decarboxylase 2 (LdcC), putrescine aminotransferase (PatA), and tested aldehyde reductase (YahK, YihU, YqhD). Among the tested reductases, aldehyde reductase exhibited the highest catalytic efficiency, producing 44.5 ± 2.6 mM of 5-AP (0.44 ± 0.03 mol 5 − AP /mol l −lysine ). The replacement of the expression system with a T7-based dual-plasmid platform, pET24ma:: ldcC , and pCDFDuet-1:: yqhD :: patA co-transformed into E. coli , increased the production to 60.7 ± 5.8 mM, accompanied by reduced cadaverine accumulation. Further enhancement was achieved by increasing the gene dosage of PatA, leading to 68.5 ± 4.2 mM 5-AP and reduced by 40% in cadaverine levels. Cadaverine is a precursor in the production of 5-AP, and its accumulation is an important factor in the limitation of conversion to 5-AP. Intracellular cofactor regeneration is expected to cause an indirect supply of α-KG, a cofactor, to enhance conversion to 5-AP. To support intracellular cofactor regeneration, glucose supplementation and increased aeration were applied, resulting in a final titer of 78.5 ± 1.2 mM 5-AP and improved precursor utilization. This study is the first report of selective microbial 5-AP production and highlights the importance of PatA expression in pathway optimization. The newly established l -lysine (C6) valorization process which converts l -lysine to high-value materials such as 1,5-PDO, glutarate, and 5-AP offers a promising route for the sustainable biosynthesis of amino alcohols, laying the groundwork for future improvements through enzyme engineering and metabolic design.

Artemisiae annuae herba (AAH) has been traditionally used as a drug for the treatment of malaria, heat stroke, bacterial infection, and fever in East-Asia. Although AAH has been used for the treatment of inflammation-related symptoms, the underlying mechanism of antiinflammatory activity of AAH is still unknown. We investigated whether AAH have an inhibitory effect on the production of pro-inflammatory mediators in lipopolysaccharide-stimulated RAW 264.7 macrophage cells. The investigation was forced on the inhibitory effect of AAH on the production of tumor necrosis factor (TNF)-α, interleukin (IL)-6, nitric oxide (NO), and inducible NO synthase (iNOS) in macrophages. Furthermore, we examined the effect of AAH on the activation of nuclear factor kappa B (NF-κB) and mitogen-activated protein kinases (MAPKs) pathways. We found that AAH suppresses NO production and TNF-α, IL-6, and iNOS gene expression. Moreover, AAH inhibited the nuclear translocation of p65 and IκBα degradation in NF-κB pathway and decreased the extracellular signal-regulated kinase, p38, c-Jun NH2-terminal kinase phosphorylation in MAPK signaling pathway. Consequently, these results indicate that AAH contains antiinflammatory activity and this effect is derived from the repression on the activation of NF-κB and MAPKs pathways. We first demonstrated that antiinflammatory effect of AAH and its underlying mechanism in macrophage cells.

Hwangryunhaedoktang (HR) has been traditionally used in oriental medicine as a drug for the treatment of melena, hemoptysis, and apoplexy. We investigated whether HR and lactobacilli-fermented HRs have an inhibitory effect on the production of proinflammatory mediators in lipopolysaccharide (LPS)-stimulated RAW 264.7 macrophage cells. The investigation was focused on whether HR and fermented HRs could inhibit the production of prostaglandin (PG)E2, nitric oxide (NO), tumor necrosis factor (TNF)-α, interleukin (IL)-6, cyclooxygenase (COX)-2, inducible nitric oxide synthase (iNOS) and mitogen-activated protein kinases (MAPKs) in LPS-stimulated RAW 264.7 cells. We found that HR weakly inhibited various inflammatory mediators induced by LPS. However, fermentation with lactobacilli significantly increased the inhibitory effect of HR on most of the inflammatory mediator expression. Furthermore, fermented HRs exerted a stronger inhibitory effect on MAPKs phosphorylation than that by non-fermented HR. These results suggest that lactobacilli-fermented HRs contains elevated potent anti-inflammatory activity that is mediated by inhibiting MAPKs pathway in macrophages.

The recent advances in high-throughput single-cell sequencing has significantly required computational models which can address the high complexity of single-cell multiomics data. Meticulous single-cell multiomics integration models are required to avoid biases towards a specific modality and overcome the sparsity. Batch effects obfuscating biological signals must also be taken into account. Here, we introduce a new single-cell multiomics integration model, Single-cell Multiomics Autoencoder Integration (scMaui) based on stacked variational encoders and adversarial learning. scMaui reduces the dimensionality of integrated data modalities to a latent space which outlines cellular heterogeneity. It can handle multiple batch effects independently accepting both discrete and continuous values, as well as provides varied reconstruction loss functions to cover various assays and preprocessing pipelines. We show that scMaui accomplishes superior performance in many tasks compared to other methods. Further downstream analyses also demonstrate its potential in identifying relations between assays and discovering hidden subpopulations.Competing Interest StatementThe authors have declared no competing interest.

DNA methylation (DNAm) is a key epigenetic mark that shows profound alterations in cancer. Read-level methylomes enable more in-depth DNAm analysis due to the broad coverage and preservation of rare cell-type signals, compared to array-based data such as 450K/EPIC array. Here, we propose MethylBERT, a novel Transformer-based model for read-level methylation pattern classification. MethylBERT identifies tumour-derived sequence reads based on their methylation patterns and genomic sequence. Using the read classification probability, the method estimates tumour cell fractions within bulk samples and provides an assessment of the model precision. In our evaluation, MethylBERT outperforms existing deconvolution methods and demonstrates high accuracy regardless of methylation pattern complexity, read length and read coverage. Moreover, we show its applicability to cell-type deconvolution as well as its potential for accurate non-invasive early cancer diagnostics using liquid biopsy samples. MethylBERT represents a significant advancement in read-level methylome analysis and enables accurate tumour purity estimation. The broad applicability of MethylBERT will enhance studies on both solid tumour tissues and circulating tumour DNA as well as non-cancerous bulk methylomes.

DNA methylation analysis by sequencing is becoming increasingly popular, yielding methylomes at single-base pair resolution. It has tremendous potential for cell-type heterogeneity analysis with intrinsic read-level information. Although diverse deconvolution methods were developed to infer cell-type composition based on bulk sequencing-based methylomes, the systematic evaluation has not been performed yet. Here, we thoroughly benchmark six previously published methods: Bayesian epiallele detection (BED), DXM, PRISM, csmFinder+coMethy, ClubCpG and MethylPurify, together with two array-based methods, MeDeCom and Houseman, as a comparison group. Sequencing-based deconvolution methods consist of two main steps, informative region selection and cell-type composition estimation, thus each was individually assessed. With these sophisticated evaluation, we demonstrate the method achieving the highest performance in different types of samples. We found that cell-type deconvolution performance is influenced by different factors depending on the number of cell types within the mixture. Finally, we propose a best-practice deconvolution strategy for sequencing data and limitations which need to be handled. Competing Interest Statement The authors have declared no competing interest.

White matter hyperintensities (WMH) appear as regions of abnormally high signal intensity on T2-weighted magnetic resonance image (MRI) sequences. In particular, WMH have been noteworthy in age-related neuroscience for being a crucial biomarker for Alzheimer's disease and brain aging processes. However, the automatic WMH segmentation is challenging because of the variable intensity range, size and shape. U-Net tackled this problem through the dense prediction and showed competitive performances on not only WMH segmentation/detection but also on varied image segmentation tasks, but it still accompanies a high complexity of the network architecture. In this study, we propose to use Saliency U-Net architecture and irregularity age map(IAM) to decrease the U-Net complexity without a performance loss. We trained Saliency U-Net using both T2-FLAIR MRI sequence and IAM. Since IAM guides where irregularities, in which WMH is possibly included, exist on the MRI slice, Saliency U-Net performs better than the original U-Net trained only using T2-FLAIR. The better performance was achieved with fewer parameters and shorter training time. Moreover, the application of dilated convolution enhanced Saliency U-Net to recognise the shape of large WMH more accurately by learning multi-context on MRI slices. This network named Dilated Saliency U-Net improved Dice coefficient score to 0.5588 which is the best score among our experimental models, and recorded a relatively good sensitivity of 0.4747 with the shortest train time and the least number of parameters. In conclusion, based on the experimental results, incorporating IAM through Dilated Saliency U-Net resulted an appropriate approach for WMH segmentation.