Prediction of Solubility of Proteins in Escherichia coli Based on Functional and Structural Features Using Machine Learning Methods.

Protein solubility is a critical parameter that determines the stability, activity, and functionality of proteins, with broad and far-reaching implications in biotechnology and biochemistry. Accurate prediction and control of protein solubility are essential for successful protein expression and purification in research and industrial settings. This study gathered information on soluble and insoluble proteins. In characterizing the proteins, they were mapped to STRING and characterized by functional and structural features. All functional/structural features were integrated to create a 5768-dimensional binary vector to encode proteins. Seven feature-ranking algorithms were employed to analyze the functional/structural features, yielding seven feature lists. These lists were subjected to the incremental feature selection, incorporating four classification algorithms, one by one to build effective classification models and identify functional/structural features with classification-related importance. Some essential functional/structural features used to differentiate between soluble and insoluble proteins were identified, including GO:0009987 (intercellular communication) and GO:0022613 (ribonucleoprotein complex biogenesis). The best classification model using support vector machine as the classification algorithm and 295 optimized functional/structural features generated the F1 score of 0.825, which can be a powerful tool to differentiate soluble proteins from insoluble proteins.

Leveraging single-cell RNA-seq for uncovering naïve B cells associated with better prognosis of hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is a typical highly heterogeneous solid tumor with high morbidity and mortality worldwide, especially in China; however, the immune microenvironment of HCC has not been clarified so far. Here, we employed single-cell RNA sequencing (scRNA-seq) on diethylnitrosamine (DEN)-induced mouse HCC model to dissect the immune cell dynamics during tumorigenesis. Our findings reveal distinct immune profiles in both precancerous and cancerous lesions, indicating early tumor-associated immunological alterations. Notably, specific T and B cell subpopulations are preferentially enriched in the HCC tumor microenvironment (TME). Furthermore, we identified a subpopulation of naïve B cells with high CD83 expression, correlating with improved prognosis in human HCC. These signature genes were validated in The Cancer Genome Atlas HCC RNA-seq dataset. Moreover, cell interaction analysis revealed that subpopulations of B cells in both mouse and human samples are activated and may potentially contribute to oncogenic processes. In summary, our study provides insights into the dynamic immune microenvironment and cellular networks in HCC pathogenesis, with a specific emphasis on naïve B cells. These findings emphasize the significance of targeting TME in HCC patients to prevent HCC pathological progression, which may give a new perspective on the therapeutics for HCC.

Mesoporous metal-organic framework NH2-MIL-101(Cr) as an efficient photocatalyst for the epoxidation of styrene.

Oxidation of styrene is a key reaction in the synthesis of pharmaceuticals and fine chemicals, and therefore oxidizing styrene with selective, efficient, and recyclable heterogeneous catalysts is significant from an environmental and economic standpoint. In this study, we report the transition Cr-based metal-organic framework [NH2-MIL-101(Cr)] as a heterogeneous photocatalyst, which efficiently promotes styrene epoxidation using H2O2 as a green oxidant, achieving high conversion efficiency (98%) and excellent selectivity (82%) under ambient conditions. Radical detection and quenching experiments reveal that the superoxide radical anion (O2˙-) acts as an active oxygen species, selectively promoting the oxidation of styrene to its oxidized form. This work provides insight into the development of a sustainable and cost-effective method for producing styrene oxide.

Identification of Key Genes in Fetal Gut Development at Single-Cell Level by Exploiting Machine Learning Techniques.

The study of fetal gut development is critical due to its substantial influence on immediate neonatal and long-term adult health. Current research largely focuses on microbiome colonization, gut immunity, and barrier function, alongside the impact of external factors on these phenomena. Limited research has been dedicated to the categorization of developing fetal gut cells. Our study aimed to enhance our understanding of fetal gut development by employing advanced machine-learning techniques on single-cell sequencing data. This dataset consisted of 62,849 samples, each characterized by 33,694 distinct gene features. Four feature ranking algorithms were utilized to sort features according to their significance, resulting in four feature lists. Then, these lists were fed into an incremental feature selection method to extract essential genes, classification rules, and build efficient classifiers. Several important genes were recognized by multiple feature ranking algorithms, such as FGG, MDK, RBP1, RBP2, IGFBP7, and SPON2. These features were key in differentiating specific developing intestinal cells, including epithelial, immune, mesenchymal, and vasculature cells of the colon, duo jejunum, and ileum cells. The classification rules showed special gene expression patterns on some intestinal cell types and the efficient classifiers can be useful tools for identifying intestinal cells.

Identification of gene and protein signatures associated with long-term effects of COVID-19 on the immune system after patient recovery by analyzing single-cell multi-omics data using a machine learning approach.

Viral infections significantly impact the immune system, and impact will persist until recovery. However, the influence of severe acute respiratory syndrome coronavirus 2 infection on the homeostatic immune status and secondary immune response in recovered patients remains unclear. To investigate these persistent alterations, we employed five feature-ranking algorithms (LASSO, MCFS, RF, CATBoost, and XGBoost), incremental feature selection, synthetic minority oversampling technique and two classification algorithms (decision tree and k-nearest neighbors) to analyze multi-omics data (surface proteins and transcriptome) from coronavirus disease 2019 (COVID-19) recovered patients and healthy controls post-influenza vaccination. The single-cell multi-omics dataset was divided into five subsets corresponding to five immune cell subtypes: B cells, CD4+ T cells, CD8+ T cells, Monocytes, and Natural Killer cells. Each cell was represented by 28,402 scRNA-seq (RNA) features, 3 Hash Tag Oligo (HTO) features, 138 Cellular indexing of transcriptomes and epitopes by sequencing (CITE) features and 23,569 Single Cell Transform (SCT) features. Some multi-omics markers were identified and effective classifiers were constructed. Our findings indicate a distinct immune status in COVID-19 recovered patients, characterized by low expression of ribosomal protein (RPS26) and high expression of immune cell surface proteins (CD33, CD48). Notably, TMEM176B, a membrane protein, was highly expressed in monocytes of COVID-19 convalescent patients. These observations aid in discerning molecular differences among immune cell subtypes and contribute to understanding the prolonged effects of COVID-19 on the immune system, which is valuable for treating infectious diseases like COVID-19.

Precise Post-Synthetic Modification of Heterometal-Organic Capsules for Selectively Encapsulating Tetrahedral Anions.

Post-synthetic modification plays a crucial role in precisely adjusting the structure and functions of advanced materials. Herein, we report the self-assembly of a tubular heterometallic Pd3Cu6L16 capsule that incorporates Pd(II) and CuL1 metalloligands. This capsule undergoes further modification with two tridentate anionic ligands (L2) to afford a bicapped Pd3Cu6L16L22 capsule with an Edshammer polyhedral structure. By employing transition metal ions, acid, and oxidation agents, the bicapped capsule can be converted into an uncapped one. This uncapped form can then revert back to the bicapped structure on the addition of Br- ions and a base. Interestingly, introducing Ag+ ions leads to the removal of one L2 ligand from the bicapped capsule, yielding a mono-capped Pd3Cu6L16L2 structure. Furthermore, the size of the anions critically influences the precise control over the post-synthetic modifications of the capsules. It was demonstrated that these capsules selectively encapsulate tetrahedral anions, offering a novel approach for the design of intelligent molecular delivery systems.

Visible-Light-Driven NO Release from Postmodified MOFs via Photoinduced Electron Transfer for Antibacterial Application.

Photoresponsive nitric oxide (NO)-releasing materials (NORMs) enable the spatiotemporal delivery of NO to facilitate their potential applications in physiological conditions. Here two novel metal-organic frameworks (MOFs)-based photoactive NORMs achieved by the incorporation of prefunctionalized NO donors into the photosensitive Fe-MOFs via a postmodification strategy is reported. The modified Fe-MOFs display superior photoactivity of NO release when exposed to visible light (up to 720 nm). Significantly, the visible-light-driven NO release properties are further corroborated by their efficient antibacterial performance.

Identification of key genes associated with persistent immune changes and secondary immune activation responses induced by influenza vaccination after COVID-19 recovery by machine learning methods.

COVID-19 is hypothesized to exert enduring effects on the immune systems of patients, leading to alterations in immune-related gene expression. This study aimed to scrutinize the persistent implications of SARS-CoV-2 infection on gene expression and its influence on subsequent immune activation responses. We designed a machine learning-based approach to analyze transcriptomic data from both healthy individuals and patients who had recovered from COVID-19. Patients were categorized based on their influenza vaccination status and then compared with healthy controls. The initial sample set encompassed 86 blood samples from healthy controls and 72 blood samples from recuperated COVID-19 patients prior to influenza vaccination. The second sample set included 123 blood samples from healthy controls and 106 blood samples from recovered COVID-19 patients who had been vaccinated against influenza. For each sample, the dataset captured expression levels of 17,060 genes. Above two sample sets were first analyzed by seven feature ranking algorithms, yielding seven feature lists for each dataset. Then, each list was fed into the incremental feature selection method, incorporating three classic classification algorithms, to extract essential genes, classification rules and build efficient classifiers. The genes and rules were analyzed in this study. The main findings included that NEXN and ZNF354A were highly expressed in recovered COVID-19 patients, whereas MKI67 and GZMB were highly expressed in patients with secondary immune activation post-COVID-19 recovery. These pivotal genes could provide valuable insights for future health monitoring of COVID-19 patients and guide the creation of continued treatment regimens.

Identification of key gene expression associated with quality of life after recovery from COVID-19.

Post-acute sequelae of COVID-19 (PASC) is a persistent complication of severe acute respiratory syndrome coronavirus 2 infection that includes symptoms, such as fatigue, cognitive impairment, and respiratory distress. These symptoms severely affect the quality of life of patients after their recovery from COVID-19. In this study, a group of machine learning algorithms analyzed the whole blood RNA-seq data from patients with different PASC levels. The purpose of this analysis was to identify the gene markers associated with PASC and the special expression patterns for different PASC levels. By comparing the quality of life of patients after the acute phase of COVID-19 and before the disease, samples in the dataset were divided into three groups, namely, "Better," "The Same," and "Worse." Each patient was represented by the expression levels of 58,929 genes. The machine learning-based workflow included six feature-ranking algorithms, incremental feature selection (IFS), and four classification algorithms. The feature ranking algorithms were in charge of assessing feature importance, whereas IFS with classification algorithms were used to extract essential genes and to construct efficient classifiers and classification rules. The expression of top genes in the results was associated with the immune response to viral infection, which is supported by the published literature. For example, patients with low CCDC18 expression and high CPED1 expression had good quality of life, whereas those with low CDC16 expression had poor quality of life.

Protocol to dissociate and isolate wide-diversity single cells by density gradient centrifugation from human hepatoblastoma tissue.

Single-cell transcriptome sequencing can characterize various cell types in human liver tissue and facilitate understanding of hepatoblastoma heterogeneity. Here, we present a protocol for isolating hepatocytes and immune cells from human hepatoblastoma samples with high viability. We describe steps for tissue processing, enzymatic digestion, Percoll density gradient separation, cell lysis, cell suspension quality control, and scRNA library construction. We then detail sequencing and data analysis. This protocol is applicable to preparing single-cell suspensions from other human liver tissue samples.

Self-Assembly of Two Tubular Metalloligand-Based Palladium-Organic Cages as Hosts for Polycyclic Aromatic Hydrocarbons.

Herein we report two tubular metal-organic cages (MOCs), synthesized by the self-assembly of bidentate metalloligands with different lengths and PdII. These two MOCs feature Pd4L8-type square tubular and Pd3L6-type triangular cage structures, respectively. Both MOCs have been fully characterized by NMR spectroscopy, mass spectrometry, and theoretical calculation. Both cages can be employed for encapsulating polycyclic aromatic hydrocarbons and show high binding affinity toward coronene.

Isolation of single cells from human hepatoblastoma tissues for whole-exome sequencing.

By combining single-cell processing with whole-exome sequencing, we have developed single-cell whole-exome sequencing to investigate the mechanisms of hepatoblastoma development and to provide potential targets and therapeutic approaches for clinical treatment. In the following protocol, we outline the steps involved in single-cell sorting, whole-genome amplification, amplification uniformity estimation, and whole-exome library construction. In addition to the cells we use, this protocol is also suitable for other cell lines and cell types.

Tailor-Made Pdn L2n Metal-Organic Cages through Covalent Post-Synthetic Modification.

Post-synthetic modification (PSM) is an effective approach for the tailored functionalization of metal-organic architectures, but its generalizability remains challenging. Herein we report a general covalent PSM strategy to functionalize Pdn L2n metal-organic cages (MOCs, n=2, 12) through an efficient Diels-Alder cycloaddition between peripheral anthracene substituents and various functional motifs bearing a maleimide group. As expected, the solubility of functionalized Pd12 L24 in common solvents can be greatly improved. Interestingly, concentration-dependent circular dichroism and aggregation-induced emission are achieved with chiral binaphthol (BINOL)- and tetraphenylethylene-modified Pd12 L24 , respectively. Furthermore, Pd12 L24 can be introduced with two different functional groups (e.g., chiral BINOL and achiral pyrene) through a step-by-step PSM route to obtain chirality-induced circularly polarized luminescence. Moreover, similar results are readily observed with a smaller Pd2 L4 system.

Identification of Transcriptome Biomarkers for Severe COVID-19 with Machine Learning Methods.

The rapid spread of COVID-19 has become a major concern for people's lives and health all around the world. COVID-19 patients in various phases and severity require individualized treatment given that different patients may develop different symptoms. We employed machine learning methods to discover biomarkers that may accurately classify COVID-19 in various disease states and severities in this study. The blood gene expression profiles from 50 COVID-19 patients without intensive care, 50 COVID-19 patients with intensive care, 10 non-COVID-19 individuals without intensive care, and 16 non-COVID-19 individuals with intensive care were analyzed. Boruta was first used to remove irrelevant gene features in the expression profiles, and then, the minimum redundancy maximum relevance was applied to sort the remaining features. The generated feature-ranked list was fed into the incremental feature selection method to discover the essential genes and build powerful classifiers. The molecular mechanism of some biomarker genes was addressed using recent studies, and biological functions enriched by essential genes were examined. Our findings imply that genes including UBE2C, PCLAF, CDK1, CCNB1, MND1, APOBEC3G, TRAF3IP3, CD48, and GZMA play key roles in defining the different states and severity of COVID-19. Thus, a new point of reference is provided for understanding the disease's etiology and facilitating a precise therapy.

Identification of COVID-19 severity biomarkers based on feature selection on single-cell RNA-Seq data of CD8+ T cells.

The global outbreak of the COVID-19 epidemic has become a major public health problem. COVID-19 virus infection triggers a complex immune response. CD8+ T cells, in particular, play an essential role in controlling the severity of the disease. However, the mechanism of the regulatory role of CD8+ T cells on COVID-19 remains poorly investigated. In this study, single-cell gene expression profiles from three CD8+ T cell subtypes (effector, memory, and naive T cells) were downloaded. Each cell subtype included three disease states, namely, acute COVID-19, convalescent COVID-19, and unexposed individuals. The profiles on each cell subtype were individually analyzed in the same way. Irrelevant features in the profiles were first excluded by the Boruta method. The remaining features for each CD8+ T cells subtype were further analyzed by Max-Relevance and Min-Redundancy, Monte Carlo feature selection, and light gradient boosting machine methods to obtain three feature lists. These lists were then brought into the incremental feature selection method to determine the optimal features for each cell subtype. Their corresponding genes may be latent biomarkers to determine COVID-19 severity. Genes, such as ZFP36, DUSP1, TCR, and IL7R, can be confirmed to play an immune regulatory role in COVID-19 infection and recovery. The results of functional enrichment analysis revealed that these important genes may be associated with immune functions, such as response to cAMP, response to virus, T cell receptor complex, T cell activation, and T cell differentiation. This study further set up different gene expression pattens, represented by classification rules, on three states of COVID-19 and constructed several efficient classifiers to distinguish COVID-19 severity. The findings of this study provided new insights into the biological processes of CD8+ T cells in regulating the immune response.

Characterization of spleen and lymph node cell types via CITE-seq and machine learning methods.

The spleen and lymph nodes are important functional organs for human immune system. The identification of cell types for spleen and lymph nodes is helpful for understanding the mechanism of immune system. However, the cell types of spleen and lymph are highly diverse in the human body. Therefore, in this study, we employed a series of machine learning algorithms to computationally analyze the cell types of spleen and lymph based on single-cell CITE-seq sequencing data. A total of 28,211 cell data (training vs. test = 14,435 vs. 13,776) involving 24 cell types were collected for this study. For the training dataset, it was analyzed by Boruta and minimum redundancy maximum relevance (mRMR) one by one, resulting in an mRMR feature list. This list was fed into the incremental feature selection (IFS) method, incorporating four classification algorithms (deep forest, random forest, K-nearest neighbor, and decision tree). Some essential features were discovered and the deep forest with its optimal features achieved the best performance. A group of related proteins (CD4, TCRb, CD103, CD43, and CD23) and genes (Nkg7 and Thy1) contributing to the classification of spleen and lymph nodes cell types were analyzed. Furthermore, the classification rules yielded by decision tree were also provided and analyzed. Above findings may provide helpful information for deepening our understanding on the diversity of cell types.

A Comparative Study of Hip Arthroplasty and Closed Reduction Proximal Femur Nail in the Treatment of Elderly Patients with Hip Fractures.

Objective: To compare the clinical effect of hip arthroplasty and closed reduction intramedullary nailing of proximal femur in the treatment of elderly hip fracture patients. Methods: There are 90 elderly hip fracture patients being recruited in the present study. Fifty patients in Group A received closed reduction intramedullary nailing of proximal femur, and 40 patients in Group B received hip arthroplasty. All patients were followed up for 12 months after surgery, clinical outcomes included surgical indicators, visual analog scale (VAS) score, Harris score, quality of life, mental status, and complications. Results: The surgery time, bleeding volume, infusion volume of patients in Group A are all significantly lower than those in Group B (p < 0.05), while the weight-bearing activity time and first workout time of Group A are all significantly higher than those in Group B (p < 0.05). The VAS score in patients of Group A at 1 week postoperative is significantly lower than that in patients of Group B (p < 0.05). The Harris score in patients of Group A at 3, 6, and 12 months postoperative are all significantly higher than those in patients of Group B (p < 0.05), and the excellent and good rate of hip function recovery at 12 months postoperative in patient of Group A is significantly lower than that in patients of Group B (80% vs. 95%, p < 0.05). Furthermore, The score of SF-36 standardized physical component, SF-36 standardized mental component and Barthel in patients of Group A at 6 months postoperative are significantly lower than those in patients of Group B (p < 0.05), and the score of mini-mental state examination is significantly higher (p < 0.05), while there are not significantly different at 12 months postoperative (p > 0.05). The incidence of postoperative complications in Group A was significantly lower than that in Group B (10% vs. 27.5%, p < 0.05). Conclusion: Elderly hip fracture patients treated with closed reduction intramedullary nailing of proximal femur has less surgical trauma and lower complication rates, but slower postoperative recovery compared with hip arthroplasty.

Identifying COVID-19 Severity-Related SARS-CoV-2 Mutation Using a Machine Learning Method.

SARS-CoV-2 shows great evolutionary capacity through a high frequency of genomic variation during transmission. Evolved SARS-CoV-2 often demonstrates resistance to previous vaccines and can cause poor clinical status in patients. Mutations in the SARS-CoV-2 genome involve mutations in structural and nonstructural proteins, and some of these proteins such as spike proteins have been shown to be directly associated with the clinical status of patients with severe COVID-19 pneumonia. In this study, we collected genome-wide mutation information of virulent strains and the severity of COVID-19 pneumonia in patients varying depending on their clinical status. Important protein mutations and untranslated region mutations were extracted using machine learning methods. First, through Boruta and four ranking algorithms (least absolute shrinkage and selection operator, light gradient boosting machine, max-relevance and min-redundancy, and Monte Carlo feature selection), mutations that were highly correlated with the clinical status of the patients were screened out and sorted in four feature lists. Some mutations such as D614G and V1176F were shown to be associated with viral infectivity. Moreover, previously unreported mutations such as A320V of nsp14 and I164ILV of nsp14 were also identified, which suggests their potential roles. We then applied the incremental feature selection method to each feature list to construct efficient classifiers, which can be directly used to distinguish the clinical status of COVID-19 patients. Meanwhile, four sets of quantitative rules were set up, which can help us to more intuitively understand the role of each mutation in differentiating the clinical status of COVID-19 patients. Identified key mutations linked to virologic properties will help better understand the mechanisms of infection and will aid in the development of antiviral treatments.

Identification of Cell Markers and Their Expression Patterns in Skin Based on Single-Cell RNA-Sequencing Profiles.

Atopic dermatitis and psoriasis are members of a family of inflammatory skin disorders. Cellular immune responses in skin tissues contribute to the development of these diseases. However, their underlying immune mechanisms remain to be fully elucidated. We developed a computational pipeline for analyzing the single-cell RNA-sequencing profiles of the Human Cell Atlas skin dataset to investigate the pathological mechanisms of skin diseases. First, we applied the maximum relevance criterion and the Boruta feature selection method to exclude irrelevant gene features from the single-cell gene expression profiles of inflammatory skin disease samples and healthy controls. The retained gene features were ranked by using the Monte Carlo feature selection method on the basis of their importance, and a feature list was compiled. This list was then introduced into the incremental feature selection method that combined the decision tree and random forest algorithms to extract important cell markers and thus build excellent classifiers and decision rules. These cell markers and their expression patterns have been analyzed and validated in recent studies and are potential therapeutic and diagnostic targets for skin diseases because their expression affects the pathogenesis of inflammatory skin diseases.