Biological Experiments Research Articles

Transcriptome-wide association study discovers functional long non-coding RNAs in coronary artery disease

Abstract Genetically regulated long non-coding RNAs (lncRNAs) in coronary artery disease (CAD) have not been investigated. To discover CAD-relevant lncRNAs, we performed the transcriptome-wide association study (TWAS) using a subset genomic data of CARDIoGRAMplusC4D Consortium and transcriptomic data of nine CAD-relevant tissue types from Genotyped Tissue Expression (GTEx) and Stockholm-Tartu Atherosclerosis Reverse Network Engineering Task (STARNET) projects. We identified 20 lncRNAs associated with CAD in a tissue-specific fashion. The eQTL-GWAS colocalization analysis associated 15 lncRNAs with cardiometabolic traits which could bridge the lncRNA to CAD risk. The co-expression analysis identified co-expressed protein-coding genes of 16 lncRNAs and suggested their involved biological pathways. Four antisense lncRNAs from the same locus, CDH13-AS2, CDH13-AS1, CEDORA, and CTD-3253I12.1, not only shared cardiometabolic traits but also exclusively co-expressed with the host gene, CDH13. Our biological experiments using dRfxCas13d-based RNA immunoprecipitation, CRISPR/Cas9 gene knockdown, and dCas9-VP64-based transcriptional activation in endothelial cells, elucidated CDH13-AS2 as a stabilizer of CDH13 mRNA and suggested therapeutic potential of CDH13-AS2 overexpression.

Human coronary blood single-cell sequencing combined with exosome proteomic profiling reveals the molecular mechanism of recurrent in-stent restenosis (R-ISR)

Abstract Background Percutaneous coronary intervention (PCI) is recognized as the core treatment for symptomatic myocardial ischemia and acute coronary syndrome (ACS). However, the complex pathogenesis of recurrent in-stent restenosis (R-ISR) after stent implantation is still insufficiently understood. Purpose This study reports the first single-cell RNA sequencing and proteomic profiling to characterize pathogenesis of R-ISR in humans. Methods To reveal the pathophysiology and molecular mechanism of R-ISR, we performed single-cell RNA sequencing to identify RISR-induced changes in transcriptional landscape in coronary blood mononuclear cell composition. Besides, proteomic profiling of human coronary plasma-derived exosomes was used to further screen different proteins. Transmission electron microscopy (TEM), nanoparticle tracking analysis (NTA) and western blot analysis were performed to identify exosomes. In vitro cell biology experiments were used to validate the discoveries of bioinformatics analysis. Results Compared with normal subjects, the percentage of FCGR3B high monocytes, memory T cells and plasmacytoid dendritic cells (DCs) was increased in R-ISR. Furthermore, we observed a marked increase in the expression of JUN, FOS, CXCL8, S100A8 enriched in the IL-17 signaling pathway that is known to play a crucial role in activating inflammation, mediating immune response, and even maintaining inflammation to chronicity. According to in vitro experiments, the co-cultured system demonstrated that FCGR3B high monocytes isolated from RISR patients activated the expression of inflammatory factors in endothelial cells (ECs), such as IL-1β, IL-6 and TNF-α. Inflammatory activated endothelial cells could release a large amount of exosomes, proteomic profiling of exosomes isolated from ECs observed a significant upregulation of MYLK expression, which could promote proliferation and migration of vascular smooth muscle cells (VSMCs) by activating VEGFR-2 signaling pathway. Conclusions Our study reveals a novel subset of FCGR3B high monocytes in R-ISR patients that could activate ECs inflammation via IL-17 signaling pathway. Activated ECs release exosomes rich in MYLK, which promote proliferation and migration of VSMCs, ultimately leading to repeated restenosis.

BES-Designer: A Web Tool to Design Guide RNAs for Base Editing to Simplify Library.

CRISPR/Cas base editors offer precise conversion of single nucleotides without inducing double-strand breaks. This technology finds extensive applications in gene therapy, gene function analysis, and other domains. However, a crucial challenge lies in selecting the appropriate guide RNAs (gRNAs) for base editing. Although various gRNAs design tools exist, creating a simplified base-editing library with diverse protospacer adjacent motifs (PAM) sequences for gRNAs screening remains a challenge. We present a user-friendly web tool, BES-Designer ( https://bes-designer.aielab.net ), for gRNAs design based on base editors, aimed at streamlining the creation of a base-editing library. BES-Designer incorporates our proposed rules for target sequence simplification, helping researchers narrow down the scope of biological experiments in the lab. It allows users to design target sequences with various PAMs and editing types simultaneously, and prioritize them in the simplified base-editing library. This tool has been experimentally proven to achieve a 30% simplification efficiency on the base-editing-library.

Comprehensive analysis of clinical features, mRNA splicing, and immunological role of REEP5 in esophageal squamous cell carcinoma

Esophageal squamous cell carcinoma (ESCC) is a prevalent malignancy within the digestive system, characterized by high incidence and mortality rates. The biological role of REEP5 in ESCC progression remains poorly understood, despite its associations with various diseases, potentially accelerating tumor malignancy. We retrieved RNA-seq data and clinical information from 179 ESCC patients from the Gene Expression Omnibus (GEO) and 93 patients from The Cancer Genome Atlas (TCGA) databases. Bioinformatics analyses were conducted to explore the biological functions of REEP5 in ESCC, its role in the tumor microenvironment, and its prognostic value. Additionally, utilizing single-cell RNA-seq (scRNA-seq) data from 3 ESCC patients in the GEO database, we performed cluster analyses to investigate cell-specific expression differences of REEP5 between cancerous and adjacent non-cancerous tissues. Molecular biology experiments were also conducted to validate REEP5 expression disparities between tumor and non-tumor tissues. Compared to normal tissues, REEP5 was significantly enriched in ESCC tissues. High REEP5 expression was closely associated with poor prognosis in ESCC patients. Gene Ontology (GO) analysis revealed strong correlations between REEP5 and processes such as mRNA splicing and protein stabilization. Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) indicated positive correlations between REEP5 and mRNA spliceosome assembly and disassembly. Pearson correlation analysis demonstrated positive associations between REEP5 and cancer-inhibitory immune checkpoints CTLA-4, TIM-3, and HVEM. Single-cell clustering and CIBERSORT analysis showed that REEP5 expression was closely related to T-cell infiltration in ESCC, with significant enrichment effects observed in CD8+ T-cell infiltration. REEP5 expression is closely correlated with the pathological and molecular pathology of ESCC, potentially playing a crucial role in Mast cell or T-cell-mediated immune responses in ESCC. Therefore, REEP5 holds promise as a novel therapeutic target for ESCC.

Synthesis of a Small Library of Glycoderivative Putative Ligands of SGLT1 and Preliminary Biological Evaluation

Sodium–glucose co-transporter 1 (SGLT1) is primarily expressed on the membrane of enterocytes, a type of epithelial cell found in the intestines, where it mediates the unidirectional absorption of glucose and galactose. Beyond its well-established role in nutrient absorption, SGLT1 also plays a protective role in maintaining the integrity of the intestinal barrier. Specifically, the natural ligand of SGLT1 (d-glucose) and a synthetic C-glucoside developed by our group can induce a protective anti-inflammatory effect on the intestinal epithelium. In this paper, we report the creation of a small library of C-glycoside, putative ligands for SGLT1, to gain further insights into its unclear mechanism of action. Preliminary biological experiments performed on an in vitro model of doxorubicin-induced mucositis, a severe intestinal inflammatory condition, indicate that the aromatic moiety present in all the compounds of the library is crucial for biological activity, while the sugar component appears to have less influence. These findings will be exploited to develop new, more potent anti-inflammatory compounds and to better understand and rationalize the protective mechanism of action.

DOT1L protects against podocyte injury in diabetic kidney disease through phospholipase C-like 1

BackgroundPodocyte injury causes proteinuria and accelerates glomerular sclerosis during diabetic kidney disease (DKD). Disruptor of telomeric silencing 1-like (DOT1L), an evolutionarily conserved histone methyltransferase, has been reported in preventing kidney fibrosis in chronic kidney disease models. However, whether DOT1L exerts beneficial effects in diabetes induced podocyte injury and the underlying molecular mechanisms need further exploration.MethodsThe expression of DOT1L was confirmed by Western blotting in MPC-5 cells and cortex of kidney from db/db mice, as well as immunofluorescence staining in human renal biopsy samples. The effect of DOT1L on podocyte injury was obtained using MPC-5 cells and db/db mice. The potential target genes regulated by DOT1L was measured by RNA-sequencing. Then, a series of molecular biological experiments was performed to investigate the regulation of PLCL1 by DOT1L in MCP-5 cells and db/db mice. Lipid accumulation was assessed by UPLC-MS/MS analysis and Oil Red O staining.ResultsDOT1L expression was significantly declined in high glucose (HG)-treated MPC-5 cells, podocyte regions of kidney tissues from db/db mice and human renal biopsy samples. Subsequent investigations revealed that upregulation of DOT1L ameliorated HG-induced cell apoptosis in MPC-5 cells as well as primary podocytes. Furthermore, podocyte-specific DOT1L overexpression inhibited diabetic podocyte injury in db/db mice. Mechanistically, we revealed that DOT1L upregulated phospholipase C-like 1 (PLCL1) expression by mediating H3K79me2 at its promoter and PLCL1 silencing suppressed the protective role of DOT1L on podocyte injury. Moreover, DOT1L improved diabetes induced abnormal fatty acid metabolism in podocytes and PLCL1 knockdown reversed its protective effects.ConclusionsTaken together, our results indicate that DOT1L protects podocyte injury via PLCL1-mediated fatty acid metabolism and provides new insights into the therapeutic target of DKD.

Plasma activated solution synergistic with exogenous RONS enhances cancer cell apoptosis for improved tumor therapy

Abstract The role of reactive oxygen and nitrogen species (RONS) in plasma-activated solution (PAS) for inducing cancer cell apoptosis is well-established, but suboptimal concentrations and rapid degradation often limit their efficacy. This study addresses a significant gap in current research by exploring how the incorporation of exogenous RONS into PAS can enhance apoptosis in cancer cells, potentially overcoming the limitations of traditional PAS treatments. In this study, the effect of treating saline with a plasma jet for different durations was investigated to analyze how its chemical composition triggers apoptosis in cancer cells in vitro. It was observed that longer plasma treatment times resulted in lower cell viability, the reactive species of H2O2, NO2- and ONOO- were found to play a crucial role in inducing apoptosis in cancer cells. Based on these findings, the anticancer efficacy of PAS was further evaluated after the addition of specific exogenous RONS, using measures such as cell viability, intracellular ROS levels, and microscopic imaging. Biological experiments demonstrated that the anticancer efficacy followed the order: addition of RONS to saline before plasma discharge treatment > addition of RONS to saline after plasma discharge treatment > exogenous RONS solution without plasma treatment. This suggests that the external introduction of RONS can modify the chemical composition of PAS to effectively enhance its anticancer properties, with the addition of H2O2 before plasma discharge treatment proving to be the most effective strategy. These results provide a novel perspective on harnessing the anticancer potential of PAS and expanding its potential applications in tumor therapy.

CMAGN: circRNA–miRNA association prediction based on graph attention auto-encoder and network consistency projection

Background: As noncoding RNAs, circular RNAs (circRNAs) can act as microRNA (miRNA) sponges due to their abundant miRNA binding sites, allowing them to regulate gene expression and influence disease development. Accurately identifying circRNA–miRNA associations (CMAs) is helpful to understand complex disease mechanisms. Given that biological experiments are time consuming and labor intensive, alternative computational methods to predict CMAs are urgently needed. Results: This study proposes a novel computational model named CMAGN, which incorporates several advanced computational methods, for predicting CMAs. First, similarity networks for circRNAs and miRNAs are constructed according to their sequences. Graph attention autoencoder is then applied to these networks to generate the first representations of circRNAs and miRNAs. The second representations of circRNAs and miRNAs are obtained from the CMA network via node2vec. The similarity networks of circRNAs and miRNAs are reconstructed on the basis of these new representations. Finally, network consistency projection is applied to the reconstructed similarity networks and the CMA network to generate a recommendation matrix. Conclusion: Five-fold cross-validation of CMAGN reveals that the area under ROC and PR curves exceed 0.96 on two widely used CMA datasets, outperforming several existing models. Additional tests elaborate the reasonability of the architecture of CMAGN and uncover its strengths and weaknesses.

Exploration of the Computational Predicted Internal Tagging Preferred Protein Properties

Protein tags are commonly used in many biological experiments, and adding a tag to an intolerant sequence position can significantly damage the functions of a protein and affect the outcome of an experiment. Recently, an in-house computational prediction method, TagScore, was developed which uses sequence homology to identify non-conservative regions of proteins permissive of tagging. The properties of the proteins that are predicted by TagScore to prefer internal tagging were explored using gene enrichment strategies. Proteins that prefer internal tagging were found to be related to GTPase-associated proteins and sequence features with disordered and polar regions, as predicted by TagScore.

Blautia coccoides and its metabolic products enhance the efficacy of bladder cancer immunotherapy by promoting CD8+ T cell infiltration

BackgroundImmune checkpoint inhibitors (ICIs) have emerged as a novel and effective treatment strategy, yet their effectiveness is limited to a subset of patients. The gut microbiota, recognized as a promising anticancer adjuvant, is being increasingly suggested to augment the efficacy of ICIs. Despite this, the causal link between the gut microbiota and the success of immunotherapy is not well understood. This gap in knowledge has driven us to identify beneficial microbiota and explore the underlying molecular mechanisms.MethodsThrough 16S rDNA sequencing, we identified distinct gut microbiota in patients undergoing treatment with ICIs. Following this, we assessed the impact of probiotics on anti-PD-1 therapy in bladder cancer using mouse models, employing a multi-omics strategy. Subsequently, we uncovered the mechanisms through which Blautia-produced metabolites enhance antitumor immunity, utilizing untargeted metabolomics and a range of molecular biology techniques.ResultsIn our research, the LEfSe analysis revealed a significant enrichment of the Blautia genus in the gut microbiota of patients who responded to immunotherapy. We discovered that the external addition of Blautia coccoides hampers tumor growth in a bladder cancer mouse model by enhancing the infiltration of CD8+ T cells within the tumor microenvironment (TME). Further investigations through untargeted metabolomics and molecular biology experiments showed that oral administration of Blautia coccoides elevated trigonelline levels. This, in turn, suppresses the β-catenin expression both in vitro and in vivo, thereby augmenting the cancer-killing activity of CD8+ T cells.ConclusionsThis research provided valuable insights into enhancing the efficacy of PD-1 inhibitors in clinical settings. It was suggested that applying Blautia coccoides and its metabolic product, trigonelline, could serve as a synergistic treatment method with PD-1 inhibitors in clinical applications.Graphical

Elucidating macrophage scavenger receptor 1’s mechanistic contribution as a shared molecular mediator in obesity and thyroid cancer pathogenesis via bioinformatics analysis

IntroductionObesity is a disease characterized by the excessive accumulation of fat. Concurrently, thyroid carcinoma (THCA) stands as the foremost endocrine malignancy. Despite the observed escalation in concurrent prevalence of both conditions, the underlying interconnections remain elusive. This indicates the need to identify potential biomarkers to predict the pathways through which obesity and THCA coexist.MethodsThe study employed a variety of methods, including differential gene expression analysis, Weighted Gene Co-expression Network Analysis (WGCNA), and gene enrichment analysis. It was also supplemented with immunohistochemical data from the Human Protein Atlas (HPA), advanced machine learning techniques, and related experiments such as qPCR, to identify important pathways and key genes shared between obesity and THCA.ResultsThrough differential gene expression analysis, WGCNA, and machine learning methods, we identified three biomarkers (IL6R, GZMB, and MSR1) associated with obesity. After validation analysis using THCA-related datasets and biological experiments, we selected Macrophage Scavenger Receptor 1 (MSR1) as a key gene for THCA analysis. The final analysis revealed that MSR1 is closely related to the degree of immune cell infiltration in patients with obesity and THCA, suggesting that this gene may be a potential intervention target for both obesity and THCA.DiscussionOur research indicates that MSR1 may influence the occurrence and development of obesity and THCA by regulating the infiltration level of immune cells. This lays the foundation for future research on targeted therapies based on their shared mechanisms.

Integrative Human Genetic and Cellular Analysis of the Pathophysiological Roles of AnxA2 in Alzheimer's Disease.

Alzheimer's disease (AD) is an intractable and progressive neurodegenerative disease. Amyloid beta (Aβ) aggregation is the hallmark of AD. Aβ induces neurotoxicity through a variety of mechanisms, including interacting with membrane receptors to alter downstream signaling, damaging cellular or organelle membranes, interfering with protein degradation and synthesis, and inducing an excessive immune-inflammatory response, all of which lead to neuronal death and other pathological changes associated with AD. In this study, we extracted gene expression profiles from the GSE5281 and GSE97760 microarray datasets in the GEO (Gene Expression Omnibus) database, as well as from the Human Gene Database. We identified differentially expressed genes in the brain tissues of AD patients and healthy persons. Through GO, KEGG, and ROC analyses, annexin A2 (AnxA2) was identified as a putative target gene. Notably, accumulating evidence suggests that intracellular AnxA2 is a key regulator in various biological processes, including endocytosis, transmembrane transport, neuroinflammation, and apoptosis. Thus, we conducted a series of cell biology experiments to explore the biological function of AnxA2 in AD. The results indicate that AnxA2 gene knockdown primarily affects oxidative phosphorylation, cell cycle, AD, protein processing in the endoplasmic reticulum, SNARE interactions in vesicular transport, and autophagy. In SH-SY5Y cells secreting Aβ42, AnxA2 gene knockdown exacerbated Aβ42-induced cytotoxicity, including cell death, intracellular ROS levels, and neuronal senescence, altered cell cycle, and reduced ATP levels, suggesting its critical role in mitochondrial function maintenance. AnxA2 gene knockdown also exacerbated the inhibitory effect of Aβ42 on cell migration. AnxA2 overexpression reduced the inflammatory response induced by Aβ42, while its absence increased pro-inflammatory and decreased anti-inflammatory responses. Furthermore, AnxA2 gene knockdown facilitated apoptosis and decreased autophagy. These results indicated potential pathophysiological roles of AnxA2 in AD.

Assessment of toxicity changes induced by exposure of human cells to lunar dust simulant

The toxicity of lunar dust (LD) to astronauts' health has been confirmed in the Apollo missions and subsequent biological experiments. Therefore, it is crucial to understand the biological toxicity of lunar dust for future human missions to the Moon. In this study, we exposed human lung epithelial cells (BEAS-2B) and peripheral blood B lymphocytes (AHH-1) to varying concentrations (0, 500, 1000, and 1500 μg/ml) of a lunar dust simulant (LDS) called CLDS-i for 24 and 48 h. The results provided the following key findings: (1) LDS induction of cell damage occurred through oxidative stress, with the levels of reactive oxygen species (ROS) in BEAS-2B cells being dependent on the duration of exposure. (2) Necrosis and early apoptosis were observed in BEAS-2B cells and AHH-1 cells, respectively. In addition, both cells showed lysosomal damage. (3) Genes CXCL1, SPP1, CSF2, MMP1, and POSTN are implicated in immune response and cytoskeletal arrangement regulation in BEAS-2B cells. Considering the similarities in composition and properties between CLDS-i and real lunar dust, our findings not only enhance the understanding of LDS toxicity, but also contribute to a better comprehension of the genomic alterations and molecular mechanisms underlying cellular toxicity induced by LD. These insights will contribute to the development of a biotoxicology framework aimed at safeguarding the health of astronauts and, consequently, facilitating future human missions to the Moon.

A Comparative Study of the Chemical Composition and Skincare Activities of Red and Yellow Ginseng Berries.

This study was conducted to investigate the differences in chemical composition between red (RGBs) and yellow ginseng berries (YGBs) and their whitening and anti-aging skincare effects. The differences in the chemical composition between RGB and YGB were analyzed by ultra-high-performance liquid chromatography tandem quadrupole electrostatic field orbit trap mass spectrometry (UHPLC-Q-Exactive-MS/MS) combined with multivariate statistics. An aging model was established using UVB radiation induction, and the whitening and anti-aging effects of the two ginseng berries were verified in vitro and in vivo using cell biology (HaCaT and B16-F10 cells) and zebrafish model organisms. A total of 31 differential compounds, including saponins, flavonoids, phenolic acids, and other chemical constituents, were identified between the two groups. Superoxide dismutase (SOD) activity was more significantly increased (p < 0.05) and malondialdehyde (MDA) content was more significantly decreased (p < 0.01) in RGB more than YGB induced by UVB ultraviolet radiation. In terms of whitening effects, YGB was more effective in inhibiting the melanin content of B16-F10 cells (p < 0.01). The results of zebrafish experiments were consistent with those of in vitro experiments and cell biology experiments. The DCFH fluorescence staining results revealed that both ginseng berries were able to significantly reduce the level of reactive oxygen species (ROS) in zebrafish (p < 0.01). Comparison of chemical composition and skin care activities based on RGB and YGB can provide a theoretical basis for the deep development and utilization of ginseng berry resources.

Toll-Like Receptor 4 (TLR4) Promotes DRG Regeneration and Repair after Sciatic Nerve Injury via the ERK-NF-kB Pathway.

Previously, we found that the expression of Toll-like receptor 4 (TLR4) is altered after sciatic nerve injury, and its differential expression plays a key role in recovery. However, the mechanisms by which TLR4 affects neuronal function in the dorsal root ganglion (DRG) have not been completely evaluated. The objective is to determine TLR4 expression in DRG tissues after sciatic neural injury and exploring the effects of TLR4 knockdown and overexpression in the DRG on neuronal function and nerve regeneration in rats in vivo and in vitro. We established a model of nerve injury and utilized molecular biology and cell biology experiments to explore the molecular mechanisms by which TLR4 in the DRG affects sciatic nerve restoration and regeneration after injury. Verified the localization of TLR4 in DRG neurons. Investigated pathways that related to apoptosis or nerve regeneration by which TLR4 regulates the function of DRG neurons. TLR4 expression was upregulated in the DRG tissues of rats after sciatic nerve injury. TLR4 overexpression promoted axon regeneration and inhibited apoptosis in DRG neurons. TLR4 promoted the regeneration of axons and the recovery of motor and sensory functions in the sciatic nerve after injury in vivo, and the data showed that TLR4 may regulate the function of DRG neurons and promote nerve repair and regeneration through the ERK and NF-κB signaling pathways in vivo and ex vivo. The study suggests that TLR4 may regulate the function of DRG neurons and promote nerve regeneration by affecting the ERK and NF-κB signaling pathways.

DeepPL: A deep-learning-based tool for the prediction of bacteriophage lifecycle.

Bacteriophages (phages) are viruses that infect bacteria and can be classified into two different lifecycles. Virulent phages (or lytic phages) have a lytic cycle that can lyse the bacteria host after their infection. Temperate phages (or lysogenic phages) can integrate their phage genomes into bacterial chromosomes and replicate with bacterial hosts via the lysogenic cycle. Identifying phage lifecycles is a crucial step in developing suitable applications for phages. Compared to the complicated traditional biological experiments, several tools have been designed for predicting phage lifecycle using different algorithms, such as random forest (RF), linear support-vector classifier (SVC), and convolutional neural network (CNN). In this study, we developed a natural language processing (NLP)-based tool-DeepPL-for predicting phage lifecycles via nucleotide sequences. The test results showed that our DeepPL had an accuracy of 94.65% with a sensitivity of 92.24% and a specificity of 95.91%. Moreover, DeepPL had 100% accuracy in lifecycle prediction on the phages we isolated and biologically verified previously in the lab. Additionally, a mock phage community metagenomic dataset was used to test the potential usage of DeepPL in viral metagenomic research. DeepPL displayed a 100% accuracy for individual phage complete genomes and high accuracies ranging from 71.14% to 100% on phage contigs produced by various next-generation sequencing technologies. Overall, our study indicates that DeepPL has a reliable performance on phage lifecycle prediction using the most fundamental nucleotide sequences and can be applied to future phage and metagenomic research.

How the technologies behind self-driving cars, social networks, ChatGPT, and DALL-E2 are changing structural biology.

The performance of deep Neural Networks (NNs) in the text (ChatGPT) and image (DALL-E2) domains has attracted worldwide attention. Convolutional NNs (CNNs), Large Language Models (LLMs), Denoising Diffusion Probabilistic Models (DDPMs)/Noise Conditional Score Networks (NCSNs), and Graph NNs (GNNs) have impacted computer vision, language editing and translation, automated conversation, image generation, and social network management. Proteins can be viewed as texts written with the alphabet of amino acids, as images, or as graphs of interacting residues. Each of these perspectives suggests the use of tools from a different area of deep learning for protein structural biology. Here, I review how CNNs, LLMs, DDPMs/NCSNs, and GNNs have led to major advances in protein structure prediction, inverse folding, protein design, and small molecule design. This review is primarily intended as a deep learning primer for practicing experimental structural biologists. However, extensive references to the deep learning literature should also make it relevant to readers who have a background in machine learning, physics or statistics, and an interest in protein structural biology.

Predicting Lactobacillus delbrueckii subsp. bulgaricus-Streptococcus thermophilus interactions based on a highly accurate semi-supervised learning method.

Lactobacillus delbrueckii subsp. bulgaricus (L. bulgaricus) and Streptococcus thermophilus (S. thermophilus) are commonly used starters in milk fermentation. Fermentation experiments revealed that L. bulgaricus-S. thermophilus interactions (LbStI) substantially impact dairy product quality and production. Traditional biological humidity experiments are time-consuming and labor-intensive in screening interaction combinations, an artificial intelligence-based method for screening interactive starter combinations is necessary. However, in the current research on artificial intelligence based interaction prediction in the field of bioinformatics, most successful models adopt supervised learning methods, and there is a lack of research on interaction prediction with only a small number of labeled samples. Hence, this study aimed to develop a semi-supervised learning framework for predicting LbStI using genomic data from 362 isolates (181 per species). The framework consisted of a two-part model: a co-clustering prediction model (based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) dataset) and a Laplacian regularized least squares prediction model (based on K-mer analysis and gene composition of all isolates datasets). To enhance accuracy, we integrated the separate outcomes produced by each component of the two-part model to generate the ultimate LbStI prediction results, which were verified through milk fermentation experiments. Validation through milk fermentation experiments confirmed a high precision rate of 85% (17/20; validated with 20 randomly selected combinations of expected interacting isolates). Our data suggest that the biosynthetic pathways of cysteine, riboflavin, teichoic acid, and exopolysaccharides, as well as the ATP-binding cassette transport systems, contribute to the mutualistic relationship between these starter bacteria during milk fermentation. However, this finding requires further experimental verification. The presented model and data are valuable resources for academics and industry professionals interested in screening dairy starter cultures and understanding their interactions.

MisORFPred: A Novel Method to Mine Translatable sORFs in Plant Pri-miRNAs Using Enhanced Scalable k-mer and Dynamic Ensemble Voting Strategy.

The primary microRNAs (pri-miRNAs) have been observed to contain translatable small open reading frames (sORFs) that can encode peptides as an independent element. Relevant studies have proven that those of sORFs are of significance in regulating the expression of biological traits. The existing methods for predicting the coding potential of sORFs frequently overlook this data or categorize them as negative samples, impeding the identification of additional translatable sORFs in pri-miRNAs. In light of this, a novel method named misORFPred has been proposed. Specifically, an enhanced scalable k-mer (ESKmer) that simultaneously integrates the composition information within a sequence and distance information between sequences is designed to extract the nucleotide sequence features. After feature selection, the optimal features and several machine learning classifiers are combined to construct the ensemble model, where a newly devised dynamic ensemble voting strategy (DEVS) is proposed to dynamically adjust the weights of base classifiers and adaptively select the optimal base classifiers for each unlabeled sample. Cross-validation results suggest that ESKmer and DEVS are essential for this classification task and could boost model performance. Independent testing results indicate that misORFPred outperforms the state-of-the-art methods. Furthermore, we execute misORFPerd on the genomes of various plant species and perform a thorough analysis of the predicted outcomes. Taken together, misORFPred is a powerful tool for identifying the translatable sORFs in plant pri-miRNAs and can provide highly trusted candidates for subsequent biological experiments.

Soil Microorganism Interactions under Biological Fumigations Compared with Chemical Fumigation.

Biological fumigation, a potential alternative to chemical fumigation, shows a wide range of prospective applications. In this study, we carried out biological fumigation experiments to evaluate its effect on alleviating consecutive cropping problems (CRPs) when compared with chemical fumigation. We designed five treatments, namely, CR (no treatment), LN (chemical fumigation with lime nitrogen), Ta (fumigation with marigold), Ra (fumigation with radish), and Br (fumigation with mustard), for soils for replanting eggplant and measured the crop's growth status, soil bacterial and fungal communities, and soil physicochemical properties. The results showed that the Br and Ra treatments formed similar microbial communities, while the Ta treatment formed unique microbial communities. The genera Olpidiomycota and Rozellomycota could be used as indicator species for the transformation process of soil microbial communities after the Br and Ta treatments, respectively. When compared with the CR and LN treatments, the soil's physicochemical properties were optimized under the Br treatment, and the soil organic matter content increased by 64.26% and 79.22%, respectively. Moreover, under the Br treatment, the soil's biological properties enhanced the bacterial and fungal alpha diversity, and the saprotrophic fungi increased with the depletion of pathotrophic fungi, while some specific probiotic microorganisms (such as Olpidiomycota, Microascales, Bacillus, etc.) were significantly enriched. In contrast, under the Ta treatment, soil nutrient levels decreased and the soil's biological indices deteriorated, whereas the bacterial diversity decreased and the pathogenic fungi increased. Among these three biological fumigation methods, the Br pre-treatment was the best way to alleviate the crop's CRPs and may be a good substitute for chemical fumigation in some situations. However, the Ta treatment also had some risks, such as the loss of land quality and reduced productivity.