Bio-Chemoinformatics-Driven Analysis of nsp7 and nsp8 Mutations and Their Effects on Viral Replication Protein Complex Stability

The nonstructural proteins 7 and 8 (nsp7 and nsp8) of SARS-CoV-2 are highly important proteins involved in the RNA-dependent polymerase (RdRp) protein replication complex. In this study, we analyzed the global mutation of nsp7 and nsp8 in 2022 and 2023 and analyzed the effects of mutation on the viral replication protein complex using bio-chemoinformatics. Frequently occurring variants are found to be single amino acid mutations for both nsp7 and nsp8. The most frequently occurring mutations for nsp7 which include L56F, L71F, S25L, M3I, D77N, V33I and T83I are predicted to cause destabilizing effects, whereas those in nsp8 are predicted to cause stabilizing effects, with the threonine to isoleucine mutation (T89I, T145I, T123I, T148I, T187I) being a frequent mutation. A conserved domain database analysis generated critical interaction residues for nsp7 (Lys-7, His-36 and Asn-37) and nsp8 (Lys-58, Pro-183 and Arg-190), which, according to thermodynamic calculations, are prone to destabilization. Trp-29, Phe-49 of nsp7 and Trp-154, Tyr-135 and Phe-15 of nsp8 cause greater destabilizing effects to the protein complex based on a computational alanine scan suggesting them as possible new target sites. This study provides an intensive analysis of the mutations of nsp7 and nsp8 and their possible implications for viral complex stability.

Microbial composition and diversity in intraradicular biofilm formed in situ: New concepts based on next-generation sequencing

This study aimed to characterize the taxonomic composition of intraradicular multispecies biofilms (IMBs) formed in situ in a model to reproduce clinical conditions. Twelve palatal roots of maxillary molars had its canals prepared. Two roots were randomly selected to sterility control. Ten intraoral prosthetic appliances with lateral slots were fabricated. The roots were positioned in the slots with the canal access open to the oral cavity. Eight volunteers wore the appliance for 21 days, and two wore it at two different time points. One root from each appliance was removed and stored at -20°C until DNA extraction and sequencing (n = 10). Biofilm was analyzed using next-generation sequencing and bioinformatics. The V4 hyper-variable region of the 16SrRNA gene was amplified and sequenced. For data analyses, the mothur pipeline was used for 16SrRNA processing, and subsequent analyses of the sequence dataset were performed in R using the MicrobiomeAnalyst R package. The taxonomy-based analysis of bacterial communities identified 562 operational taxonomic units (OTUs), which belonged to 93 genera, 44 families, and 8 phyla. Bacterial colonization was different for each biofilm, and samples did not have the same group of bacteria. Alpha and beta diversity analysis revealed some general patterns of sample clustering. A core microbiome of prevalent OTUs and genera was identified. IMBs were heterogeneous when analyzed individually, but some diversity patterns were found after sample clustering. The experimental model seemed to reproduce the actual biofilm composition in endodontic infections, which suggests that it may be used to evaluate disinfection protocols.

Transcriptome profiling and analysis of patients with esophageal squamous cell carcinoma from Kazakhstan

Esophageal squamous cell carcinoma (ESCC) is the predominant subtype of esophageal cancer in Central Asia, often diagnosed at advanced stages. Understanding population-specific patterns of ESCC is crucial for tailored treatments. This study aimed to unravel ESCC's genetic basis in Kazakhstani patients and identify potential biomarkers for early diagnosis and targeted therapies. ESCC patients from Kazakhstan were studied. We analyzed histological subtypes and conducted in-depth transcriptome sequencing. Differential gene expression analysis was performed, and significantly dysregulated pathways were identified using KEGG pathway analysis (p-value < 0.05). Protein-protein interaction networks were constructed to elucidate key modules and their functions. Among Kazakhstani patients, ESCC with moderate dysplasia was the most prevalent subtype. We identified 42 significantly upregulated and two significantly downregulated KEGG pathways, highlighting molecular mechanisms driving ESCC pathogenesis. Immune-related pathways, such as viral protein interaction with cytokines, rheumatoid arthritis, and oxidative phosphorylation, were elevated, suggesting immune system involvement. Conversely, downregulated pathways were associated with extracellular matrix degradation, crucial in cancer invasion and metastasis. Protein-protein interaction network analysis revealed four distinct modules with specific functions, implicating pathways in esophageal cancer development. High-throughput transcriptome sequencing elucidated critical molecular pathways underlying esophageal carcinogenesis in Kazakhstani patients. Insights into dysregulated pathways offer potential for early diagnosis and precision treatment strategies for ESCC. Understanding population-specific patterns is essential for personalized approaches to ESCC management.

A novel stacking-based predictor for accurate prediction of antimicrobial peptides

Antimicrobial peptides (AMPs) are gaining acceptance and support as a chief antibiotic substitute since they boost human immunity. They retain a wide range of actions and have a low risk of developing resistance, which are critical properties to the pharmaceutical industry for drug discovery. Antibiotic sensitivity, however, is an issue that affects people all around the world and has the potential to one day lead to an epidemic. As cutting-edge therapeutic agents, AMPs are also expected to cure microbial infections. In order to produce tolerable drugs, it is crucial to understand the significance of the basic architecture of AMPs. Traditional laboratory methods are expensive and time-consuming for AMPs testing and detection. Currently, bioinformatics techniques are being successfully applied to the detection of AMPs. In this study, we have developed a novel STacking-based ensemble learning framework for AntiMicrobial Peptide (STAMP) prediction. First, we constructed 84 different baseline models by using 12 different feature encoding schemes and 7 popular machine learning algorithms. Second, these baseline models were trained and employed to create a new probabilistic feature vector. Finally, based on the feature selection strategy, we determined the optimal probabilistic feature vector, which was further utilized for the construction of our stacked model. Resultantly, the STAMP predictor achieved excellent performance during cross-validation with an accuracy and Matthew's correlation coefficient of 0.930 and 0.860, respectively. The corresponding metrics during the independent test were 0.710 and 0.464, respectively. Overall, STAMP achieved a more accurate and stable performance than the baseline models and significantly outperformed the existing predictors, demonstrating the effectiveness of our proposed hybrid framework. Furthermore, STAMP is expected to assist community-wide efforts in identifying AMPs and will contribute to the development of novel therapeutic methods and drug-design for immunity.Communicated by Ramaswamy H. Sarma.

Biomimetic nanocluster photoreceptors for adaptative circular polarization vision

Nanoclusters with atomically precise structures and discrete energy levels are considered as nanoscale semiconductors for artificial intelligence. However, nanocluster electronic engineering and optoelectronic behavior have remained obscure and unexplored. Hence, we create nanocluster photoreceptors inspired by mantis shrimp visual systems to satisfy the needs of compact but multi-task vision hardware and explore the photo-induced electronic transport. Wafer-scale arrayed photoreceptors are constructed by a nanocluster-conjugated molecule heterostructure. Nanoclusters perform as an in-sensor charge reservoir to tune the conductance levels of artificial photoreceptors by a light valve mechanism. A ligand-assisted charge transfer process takes place at nanocluster interface and it features an integration of spectral-dependent visual adaptation and circular polarization recognition. This approach is further employed for developing concisely structured, multi-task, and compact artificial visual systems and provides valuable guidelines for nanocluster neuromorphic devices.

PTCH1 Gene Variants, mRNA Expression, and Bioinformatics Insights in Mexican Cutaneous Squamous Cell Carcinoma Patients

BACKGROUND

Skin cancer is one of the most frequent types of cancer, and cutaneous squamous cell carcinoma (cSCC) constitutes 20% of non-melanoma skin cancer (NMSC) cases. PTCH1, a tumor suppressor gene involved in the Sonic hedgehog signaling pathway, plays a crucial role in neoplastic processes.

METHODS

An analytical cross-sectional study, encompassing 211 cSCC patients and 290 individuals in a control group (CG), was performed. A subgroup of samples was considered for the relative expression analysis, and the results were obtained using quantitative real-time PCR (qPCR) with TaqMan® probes. The functional, splicing, and disease-causing effects of the proposed variants were explored via bioinformatics.

RESULTS

cSCC was predominant in men, especially in sun-exposed areas such as the head and neck. No statistically significant differences were found regarding the rs357564, rs2236405, rs2297086, and rs41313327 variants of PTCH1, or in the risk of cSCC, nor in the mRNA expression between the cSCC group and CG. A functional effect of rs357564 and a disease-causing relation to rs41313327 was identified.

CONCLUSION

The proposed variants were not associated with cSCC risk in this Mexican population, but we recognize the need for analyzing larger population groups to elucidate the disease-causing role of rare variants.

Development of an immune-related diagnostic predictive model for oral lichen planus

Oral lichen planus (OLP) was a chronic inflammatory disease of unknown etiology with a 1.4% chance of progressing to malignancy. However, it has been suggested in several studies that immune system disorders played a dominant role in the onset and progression of OLP. Therefore, this experiment aimed to develop a diagnostic prediction model for OLP based on immunopathogenesis to achieve early diagnosis and treatment and prevent cancer. In this study, 2 publicly available OLP datasets from the gene expression omnibus database were filtered. In the experimental group (GSE52130), the level of immune cell infiltration was assessed using MCPcounter and ssGSEA algorithms. Subsequently, differential expression analysis and gene set enrichment analysis were performed between the OLP and control groups. The resulting differentially expressed genes were intersected with immunologically relevant genes provided on the immunology database and analysis portal database (ImmPort) website to obtain differentially expressed immunologically relevant genes (DEIRGs). Furthermore, the gene ontology and kyoto encyclopedia of genes and genomes analyses were carried out. Finally, protein-protein interaction network and least absolute shrinkage and selection operator regression analyses constructed a model for OLP. Receiver operating characteristic curves for the experimental and validation datasets (GSE38616) were plotted separately to validate the model's credibility. In addition, real-time quantitative PCR experiment was performed to verify the expression level of the diagnostic genes. Immune cell infiltration analysis revealed a more significant degree of inflammatory infiltration in the OLP group compared to the control group. In addition, the gene set enrichment analysis results were mainly associated with keratinization, antibacterial and immune responses, etc. A total of 774 differentially expressed genes was obtained according to the screening criteria, of which 65 were differentially expressed immunologically relevant genes. Ultimately, an immune-related diagnostic prediction model for OLP, which was composed of 5 hub genes (BST2, RNASEL, PI3, DEFB4A, CX3CL1), was identified. The verification results showed that the model has good diagnostic ability. There was a significant correlation between the 5 hub diagnostic biomarkers and immune infiltrating cells. The development of this model gave a novel insight into the early diagnosis of OLP.

VirtuousPocketome: a computational tool for screening protein-ligand complexes to identify similar binding sites

Protein residues within binding pockets play a critical role in determining the range of ligands that can interact with a protein, influencing its structure and function. Identifying structural similarities in proteins offers valuable insights into their function and activation mechanisms, aiding in predicting protein-ligand interactions, anticipating off-target effects, and facilitating the development of therapeutic agents. Numerous computational methods assessing global or local similarity in protein cavities have emerged, but their utilization is impeded by complexity, impractical automation for amino acid pattern searches, and an inability to evaluate the dynamics of scrutinized protein-ligand systems. Here, we present a general, automatic and unbiased computational pipeline, named VirtuousPocketome, aimed at screening huge databases of proteins for similar binding pockets starting from an interested protein-ligand complex. We demonstrate the pipeline's potential by exploring a recently-solved human bitter taste receptor, i.e. the TAS2R46, complexed with strychnine. We pinpointed 145 proteins sharing similar binding sites compared to the analysed bitter taste receptor and the enrichment analysis highlighted the related biological processes, molecular functions and cellular components. This work represents the foundation for future studies aimed at understanding the effective role of tastants outside the gustatory system: this could pave the way towards the rationalization of the diet as a supplement to standard pharmacological treatments and the design of novel tastants-inspired compounds to target other proteins involved in specific diseases or disorders. The proposed pipeline is publicly accessible, can be applied to any protein-ligand complex, and could be expanded to screen any database of protein structures.

Acoustic monitoring reveals a diel rhythm of an arctic seabird colony (little auk, Alle alle)

The child-like question of why birds sing in the morning is difficult to answer, especially in polar regions. There, in summer animals live without the time constraints of daylight, and little is known about the rhythmicity of their routines. Moreover, in situ monitoring of animal behavior in remote areas is challenging and rare. Here, we use audio data from Greenland to show that a colony of a key Arctic-breeding seabird, the little auk (Alle alle), erupts with acoustic excitement at night in August, under the midnight sun. We demonstrate that the acoustic activity cycle is consistent with previous direct observations of the feeding and attendance patterns of the little auk. We interpret this pattern as reflecting their foraging activities, but further investigation on fledging and predators is needed. The study demonstrates that acoustic monitoring is a promising alternative to otherwise demanding manual observations of bird colonies in remote Arctic areas.

Characteristics of MicroRNA Expression Depending on the Presence or Absence of Meningioma in Patients with Neurofibromatosis Type 2: A Secondary Analysis

Meningioma is the second most frequent tumor in patients with neurofibromatosis type 2 (NF2). The presence of meningioma is believed to be a negative prognostic marker in these patients. However, the molecular mechanisms involved in the tumorigenesis of NF2-associated meningioma are not well characterized. Epigenetic regulation, including microRNAs (miRNAs), may be involved in the development of different tumor types in patients with NF2. The objective of this study is to explore the different characteristics of serum miRNA expression depending on the presence or absence of meningioma in patients with NF2. Nine patients with NF2 who were treated at the Department of Neurosurgery, Hiroshima University Hospital, were included. Total RNA (including small RNAs) was extracted from serum samples for the preparation of a small RNA library for next-generation sequencing analysis. Differentially expressed miRNAs (DEMs) were analyzed using the DESeq2 package to compare the characteristic miRNA expression profiles of patients with and without meningioma. In small RNA sequencing analysis, out of a total of 1,879 miRNAs registered in the database, the expressions of 657 miRNAs were observed. In DEM analysis, the expressions of four miRNAs, namely, hsa-miR-664b, hsa-miR-7706, hsa-miR-590, and hsa-miR-6513, were downregulated in patients with NF2 with meningioma compared with patients with NF2 without meningioma. Hsa-miR-193a was identified as the only upregulated miRNA in patients with NF2 with meningioma. In conclusion, we identified different circulating miRNA expression characteristics depending on the presence or absence of meningioma in patients with NF2.

Multi-omics analysis of TLCD1 as a promising biomarker in pan-cancer

Background: The TLC Domain Containing 1 (TLCD1) protein, a key regulator of phosphatidylethanolamine (PE) composition, is distributed across several cellular membranes, including mitochondrial plasma membranes. Existing research has revealed the impact of TLCD1 on the development of non-alcoholic fatty liver disease. However, there remains a gap in comprehensive pan-cancer analyses of TLCD1, and the precise role of TLCD1 in cancer patient prognosis and immunological responses remains elusive. This study aims to provide a comprehensive visualization of the prognostic landscape associated with TLCD1 across a spectrum of cancers, while shedding light on the potential links between TLCD1 expression within the tumor microenvironment and immune infiltration characteristics. Methods: TLCD1 expression data were obtained from GTEx, TCGA, and HPA data repositories. Multiple databases including TIMER, HPA, TISIDB, cBioPortal, GEPIA2, STRING, KEGG, GO, and CancerSEA were used to investigate the expression pattern, diagnostic and prognostic significance, mutation status, functional analysis, and functional status of TLCD1. In addition, we evaluated the relationship between TLCD1 expression and immune infiltration, tumor mutational burden (TMB), microsatellite instability (MSI), and immune-related genes in pan-cancer. Furthermore, the association of TLCD1 with drug sensitivity was analyzed using the CellMiner database. Results: We found that TLCD1 is generally highly expressed in pan-cancers and is significantly associated with the staging and prognosis of various cancers. Furthermore, our results also showed that TLCD1 was significantly associated with immune cell infiltration and immune regulatory factor expression. Using CellMiner database analysis, we then found a strong correlation between TLCD1 expression and sensitivity to anticancer drugs, indicating its potential as a therapeutic target. The most exciting finding is that high TLCD1 expression is associated with worse survival and prognosis in GBM and SKCM patients receiving anti-PD1 therapy. These findings highlight the potential of TLCD1 as a predictive biomarker for response to immunotherapy. Conclusion: TLCD1 plays a role in the regulation of immune infiltration and affects the prognosis of patients with various cancers. It serves as both a prognostic and immunologic biomarker in human cancer.

Identification of key lncRNAs in age-related macular degeneration through integrated bioinformatics and experimental validation

This study aimed to identify key long noncoding RNAs (lncRNAs) in age-related macular degeneration (AMD) patients and to identify relevant pathological mechanisms of AMD development. We identified 407 differentially expressed mRNAs and 429 differentially expressed lncRNAs in retinal pigment epithelium (RPE) and retina in the macular region of AMD patients versus controls (P < 0.05 and |log2FC| > 0.585) from GSE135092. A total of 14 key differentially expressed mRNAs were obtained through external data validation from GSE115828. A miRNA-mRNA and miRNA-lncRNA network containing 52 lncRNA nodes, 49 miRNA nodes, 14 mRNA nodes and 351 edges was constructed via integrated analysis of these components. Finally, the LINC00276-miR-619-5p-IFIT3 axis was identified via protein-protein network analysis. In the t-BH-induced ARPE-19 senescent cell model, LINC00276 and IFIT3 were downregulated. Overexpression of LINC00276 could accelerate cell migration in combination with IFIT3 upregulation. This compelling finding suggests that LINC00276 plays an influential role in the progression of AMD, potentially through modulating senescence processes, thereby setting a foundation for future investigative efforts to verify this relationship.

Identifying microRNAs associated with tumor immunotherapy response using an interpretable machine learning model

Predicting clinical responses to tumor immunotherapy is essential to reduce side effects and the potential for sustained clinical responses. Nevertheless, preselecting patients who are likely to respond to such treatments remains highly challenging. Here, we explored the potential of microRNAs (miRNAs) as predictors of immune checkpoint blockade responses using a machine learning approach. First, we constructed random forest models to predict the response to tumor ICB therapy using miRNA expression profiles across 19 cancer types. The contribution of individual miRNAs to each prediction process was determined by employing SHapley Additive exPlanations (SHAP) for model interpretation. Remarkably, the predictive performance achieved by using a small number of miRNAs with high feature importance was similar to that achieved by using the entire miRNA set. Additionally, the genes targeted by these miRNAs were closely associated with tumor- and immune-related pathways. In conclusion, this study demonstrates the potential of miRNA expression data for assessing tumor immunotherapy responses. Furthermore, we confirmed the potential of informative miRNAs as biomarkers for the prediction of immunotherapy response, which will advance our understanding of tumor immunotherapy mechanisms.

Identification and validation of hub genes expressed in ulcerative colitis with metabolic dysfunction-associated steatotic liver disease

Objective

Ulcerative colitis (UC) and metabolic dysfunction-associated steatotic liver disease (MASLD) are closely intertwined; however, the precise molecular mechanisms governing their coexistence remain unclear.

Methods

We obtained UC (GSE75214) and MASLD (GSE151158) datasets from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were acquired by the 'edgeR' and 'limma' packages of R. We then performed functional enrichment analysis of common DEGs. Hub genes were selected using the cytoHubba plugin and validated using GSE87466 for UC and GSE33814 for MASLD. Immunohistochemistry was employed to validate the hub genes' expression in clinical samples. Immune infiltration and gene set enrichment analyses of the hub genes were performed. Finally, we estimated the Spearman's correlation coefficients for the clinical correlation of the core genes.

Results

Within a cohort of 26 differentially regulated genes in both UC and MASLD, pathways involving cytokine-mediated signaling, cell chemotaxis, and leukocyte migration were enriched. After further validation, CXCR4, THY1, CCL20, and CD2 were identified as the hub genes. Analysis of immune infiltration patterns highlighted an association between elevated pivotal gene expression and M1 macrophage activation. Immunohistochemical staining revealed widespread expression of pivotal genes in UC- and MASLD-affected tissues. Furthermore, significant correlations were observed between the increased expression of hub genes and biochemical markers, such as albumin and prothrombin time.

Conclusion

This bioinformatics analysis highlights CXCR4, THY1, CCL20, and CD2 as crucial genes involved in the co-occurrence of UC and MASLD, providing insights into the underlying mechanisms of these two conditions.

MHCII-peptide presentation: an assessment of the state-of-the-art prediction methods

Major histocompatibility complex Class II (MHCII) proteins initiate and regulate immune responses by presentation of antigenic peptides to CD4+ T-cells and self-restriction. The interactions between MHCII and peptides determine the specificity of the immune response and are crucial in immunotherapy and cancer vaccine design. With the ever-increasing amount of MHCII-peptide binding data available, many computational approaches have been developed for MHCII-peptide interaction prediction over the last decade. There is thus an urgent need to provide an up-to-date overview and assessment of these newly developed computational methods. To benchmark the prediction performance of these methods, we constructed an independent dataset containing binding and non-binding peptides to 20 human MHCII protein allotypes from the Immune Epitope Database, covering DP, DR and DQ alleles. After collecting 11 known predictors up to January 2022, we evaluated those available through a webserver or standalone packages on this independent dataset. The benchmarking results show that MixMHC2pred and NetMHCIIpan-4.1 achieve the best performance among all predictors. In general, newly developed methods perform better than older ones due to the rapid expansion of data on which they are trained and the development of deep learning algorithms. Our manuscript not only draws a full picture of the state-of-art of MHCII-peptide binding prediction, but also guides researchers in the choice among the different predictors. More importantly, it will inspire biomedical researchers in both academia and industry for the future developments in this field.

Context-dependent design of induced-fit enzymes using deep learning generates well-expressed, thermally stable and active enzymes

The potential of engineered enzymes in industrial applications is often limited by their expression levels, thermal stability, and catalytic diversity. De novo enzyme design faces challenges due to the complexity of enzymatic catalysis. An alternative approach involves expanding natural enzyme capabilities for new substrates and parameters. Here, we introduce CoSaNN (Conformation Sampling using Neural Network), an enzyme design strategy using deep learning for structure prediction and sequence optimization. CoSaNN controls enzyme conformations to expand chemical space beyond simple mutagenesis. It employs a context-dependent approach for generating enzyme designs, considering non-linear relationships in sequence and structure space. We also developed SolvIT, a graph NN predicting protein solubility in Escherichia coli, optimizing enzyme expression selection from larger design sets. Using this method, we engineered enzymes with superior expression levels, with 54% expressed in E. coli, and increased thermal stability, with over 30% having higher Tm than the template, with no high-throughput screening. Our research underscores AI's transformative role in protein design, capturing high-order interactions and preserving allosteric mechanisms in extensively modified enzymes, and notably enhancing expression success rates. This method's ease of use and efficiency streamlines enzyme design, opening broad avenues for biotechnological applications and broadening field accessibility.

Multi-modal molecular determinants of clinically relevant osteoporosis subtypes

Due to a rapidly aging global population, osteoporosis and the associated risk of bone fractures have become a wide-spread public health problem. However, osteoporosis is very heterogeneous, and the existing standard diagnostic measure is not sufficient to accurately identify all patients at risk of osteoporotic fractures and to guide therapy. Here, we constructed the first prospective multi-omics atlas of the largest osteoporosis cohort to date (longitudinal data from 366 participants at three time points), and also implemented an explainable data-intensive analysis framework (DLSF: Deep Latent Space Fusion) for an omnigenic model based on a multi-modal approach that can capture the multi-modal molecular signatures (M3S) as explicit functional representations of hidden genotypes. Accordingly, through DLSF, we identified two subtypes of the osteoporosis population in Chinese individuals with corresponding molecular phenotypes, i.e., clinical intervention relevant subtypes (CISs), in which bone mineral density benefits response to calcium supplements in 2-year follow-up samples. Many snpGenes associated with these molecular phenotypes reveal diverse candidate biological mechanisms underlying osteoporosis, with xQTL preferences of osteoporosis and its subtypes indicating an omnigenic effect on different biological domains. Finally, these two subtypes were found to have different relevance to prior fracture and different fracture risk according to 4-year follow-up data. Thus, in clinical application, M3S could help us further develop improved diagnostic and treatment strategies for osteoporosis and identify a new composite index for fracture prediction, which were remarkably validated in an independent cohort (166 participants).

Analysis and visualization of quantitative proteomics data using FragPipe-Analyst

The FragPipe computational proteomics platform is gaining widespread popularity among the proteomics research community because of its fast processing speed and user-friendly graphical interface. Although FragPipe produces well-formatted output tables that are ready for analysis, there is still a need for an easy-to-use and user-friendly downstream statistical analysis and visualization tool. FragPipe-Analyst addresses this need by providing an R shiny web server to assist FragPipe users in conducting downstream analyses of the resulting quantitative proteomics data. It supports major quantification workflows including label-free quantification, tandem mass tags, and data-independent acquisition. FragPipe-Analyst offers a range of useful functionalities, such as various missing value imputation options, data quality control, unsupervised clustering, differential expression (DE) analysis using Limma, and gene ontology and pathway enrichment analysis using Enrichr. To support advanced analysis and customized visualizations, we also developed FragPipeAnalystR, an R package encompassing all FragPipe-Analyst functionalities that is extended to support site-specific analysis of post-translational modifications (PTMs). FragPipe-Analyst and FragPipeAnalystR are both open-source and freely available.

Interrogations of single-cell RNA splicing landscapes with SCASL define new cell identities with physiological relevance

RNA splicing shapes the gene regulatory programs that underlie various physiological and disease processes. Here, we present the SCASL (single-cell clustering based on alternative splicing landscapes) method for interrogating the heterogeneity of RNA splicing with single-cell RNA-seq data. SCASL resolves the issue of biased and sparse data coverage on single-cell RNA splicing and provides a new scheme for classifications of cell identities. With previously published datasets as examples, SCASL identifies new cell clusters indicating potentially precancerous and early-tumor stages in triple-negative breast cancer, illustrates cell lineages of embryonic liver development, and provides fine clusters of highly heterogeneous tumor-associated CD4 and CD8 T cells with functional and physiological relevance. Most of these findings are not readily available via conventional cell clustering based on single-cell gene expression data. Our study shows the potential of SCASL in revealing the intrinsic RNA splicing heterogeneity and generating biological insights into the dynamic and functional cell landscapes in complex tissues.

Challenges in computational discovery of bioactive peptides in 'omics data

Peptides have a plethora of activities in biological systems that can potentially be exploited biotechnologically. Several peptides are used clinically, as well as in industry and agriculture. The increase in available 'omics data has recently provided a large opportunity for mining novel enzymes, biosynthetic gene clusters, and molecules. While these data primarily consist of DNA sequences, other types of data provide important complementary information. Due to their size, the approaches proven successful at discovering novel proteins of canonical size cannot be naïvely applied to the discovery of peptides. Peptides can be encoded directly in the genome as short open reading frames (smORFs), or they can be derived from larger proteins by proteolysis. Both of these peptide classes pose challenges as simple methods for their prediction result in large numbers of false positives. Similarly, functional annotation of larger proteins, traditionally based on sequence similarity to infer orthology and then transferring functions between characterized proteins and uncharacterized ones, cannot be applied for short sequences. The use of these techniques is much more limited and alternative approaches based on machine learning are used instead. Here, we review the limitations of traditional methods as well as the alternative methods that have recently been developed for discovering novel bioactive peptides with a focus on prokaryotic genomes and metagenomes.

Banxia-Shengjiang drug pair inhibits gastric cancer development and progression by improving body immunity

To investigate the mechanism of action of Banxia-Shengjiang drug pair on the inhibition of gastric cancer (GC) using network pharmacology and bioinformatics techniques. The action targets of the Banxia (Pinellia ternata (Thunb.) Makino) -Shengjiang (Zingiber officinale Roscoe) drug pair obtained from the TCMSP database were intersected with differentially expressed genes (DEGs) and GC-related genes, and the intersected genes were analyzed for pathway enrichment to identify the signaling pathways and core target genes. Subsequently, the core target genes were analyzed for clinical relevance gene mutation analysis, methylation analysis, immune infiltration analysis and immune cell analysis. Finally, by constructing the PPI network of hub genes and corresponding active ingredients, the key active ingredients of the Banxia-Shengjiang drug pair were screened for molecular docking with the hub genes. In this study, a total of 557 target genes of Banxia-Shengjiang pairs, 7754 GC-related genes and 1799 DEGs in GC were screened. Five hub genes were screened, which were PTGS2, MMP9, PPARG, MMP2, and CXCR4. The pathway enrichment analyses showed that the intersecting genes were associated with RAS/MAPK signaling pathway. In addition, the clinical correlation analysis showed that hub genes were differentially expressed in GC and was closely associated with immune infiltration and immunotherapy. The results of single nucleotide variation (SNV) and copy number variation (CNV) indicated that mutations in the hub genes were associated with the survival of gastric cancer patients. Finally, the PPI network and molecular docking results showed that PTGS2 and MMP9 were potentially important targets for the inhibition of GC by Banxia-Shengjiang drug pair, while cavidine was an important active ingredient for the inhibition of GC by Banxia-Shengjiang drug pair. Banxia-Shengjiang drug pair may regulate the immune function and inhibit GC by modulating the expression of core target genes such as RAS/MAPK signaling pathway, PTGS2 and MMP9.

Exploring hub genes and crucial pathways linked to oxidative stress in bipolar disorder depressive episodes through bioinformatics analysis

Background

Bipolar disorder (BD) is a complex and serious psychiatric condition primarily characterized by bipolar depression, with the underlying genetic determinants yet to be elucidated. There is a substantial body of literature linking psychiatric disorders, including BD, to oxidative stress (OS). Consequently, this study aims to assess the relationship between BD and OS by identifying key hub genes implicated in OS pathways.

Methods

We acquired gene microarray data from GSE5392 through the Gene Expression Omnibus (GEO). Our approach encompassed differential expression analysis, weighted gene co-expression network analysis (WGCNA), and Protein-Protein Interaction (PPI) Network analysis to pinpoint hub genes associated with BD. Subsequently, we utilized Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) to identify hub genes relevant to OS. To evaluate the diagnostic accuracy of these hub genes, we performed receiver operating characteristic curve (ROC) analysis on both GSE5388 and GSE5389 datasets. Furthermore, we conducted a study involving ten BD patients and ten healthy controls (HCs) who met the special criteria, assessing the expression levels of these hub genes in their peripheral blood mononuclear cells (PBMCs).

Results

We identified 411 down-regulated genes and 69 up-regulated genes for further scrutiny. Through WGCNA, we obtained 22 co-expression modules, with the sienna3 module displaying the strongest association with BD. By integrating differential analysis with genes linked to OS, we identified 44 common genes. Subsequent PPI Network and WGCNA analyses confirmed three hub genes as potential biomarkers for BD. Functional enrichment pathway analysis revealed their involvement in neuronal signal transduction, oxidative phosphorylation, and metabolic obstacle pathways. Using the Cytoscape plugin "ClueGo assay," we determined that a majority of these targets regulate neuronal synaptic plasticity. ROC curve analysis underscored the excellent diagnostic value of these three hub genes. Quantitative reverse transcription-PCR (RT-qPCR) results indicated significant changes in the expression of these hub genes in the PBMCs of BD patients compared to HCs.

Conclusion

We identified three hub genes (TAC1, MAP2K1, and MAP2K4) in BD associated with OS, potentially influencing the diagnosis and treatment of BD. Based on the GEO database, our study provides novel insights into the relationship between BD and OS, offering promising therapeutic targets.

VIPERA: Viral Intra-Patient Evolution Reporting and Analysis

Viral mutations within patients nurture the adaptive potential of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) during chronic infections, which are a potential source of variants of concern. However, there is no integrated framework for the evolutionary analysis of intra-patient SARS-CoV-2 serial samples. Herein, we describe Viral Intra-Patient Evolution Reporting and Analysis (VIPERA), a new software that integrates the evaluation of the intra-patient ancestry of SARS-CoV-2 sequences with the analysis of evolutionary trajectories of serial sequences from the same viral infection. We have validated it using positive and negative control datasets and have successfully applied it to a new case, which revealed population dynamics and evidence of adaptive evolution. VIPERA is available under a free software license at https://github.com/PathoGenOmics-Lab/VIPERA.

DPP2/7 is a Potential Predictor of Prognosis and Target in Immunotherapy in Colorectal Cancer: An Integrative Multi-omics Analysis

BACKGROUND

Colorectal cancer (CRC) ranks among the leading causes of cancerrelated deaths.

OBJECTIVE

This study aimed to illuminate the relationship between DPP7 (also known as DPP2) and CRC through a combination of bioinformatics and experimental methodologies.

METHODS

A multi-dimensional bioinformatic analysis on DPP7 was executed, covering its expression, survival implications, clinical associations, functional roles, immune interactions, and drug sensitivities. Experimental validations involved siRNA-mediated DPP7 knockdown and various cellular assays.

RESULTS

Data from the Cancer Genome Atlas (TCGA) identified high DPP7 expression in solid CRC tumors, with elevated levels adversely affecting patient prognosis. A shift from the N0 to the N2 stage in CRC was associated with increased DPP7 expression. Functional insights indicated the involvement of DPP7 in cancer progression, particularly in extracellular matrix disassembly. Immunological analyses showed its association with immunosuppressive entities, and in vitro experiments in CRC cell lines underscored its oncogenic attributes.

CONCLUSION

DPP7 could serve as a CRC prognosis marker, functioning as an oncogene and representing a potential immunotherapeutic target.

Bioinformatics identifies key genes and potential drugs for energy metabolism disorders in heart failure with dilated cardiomyopathy

Background: Dysfunction in myocardial energy metabolism plays a vital role in the pathological process of Dilated Cardiomyopathy (DCM). However, the precise mechanisms remain unclear. This study aims to investigate the key molecular mechanisms of energy metabolism and potential therapeutic agents in the progression of dilated cardiomyopathy with heart failure. Methods: Gene expression profiles and clinical data for patients with dilated cardiomyopathy complicated by heart failure, as well as healthy controls, were sourced from the Gene Expression Omnibus (GEO) database. Gene sets associated with energy metabolism were downloaded from the Molecular Signatures Database (MSigDB) for subsequent analysis. Weighted Gene Co-expression Network Analysis (WGCNA) and differential expression analysis were employed to identify key modules and genes related to heart failure. Potential biological mechanisms were investigated through Gene Set Enrichment Analysis (GSEA), Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and the construction of a competing endogenous RNA (ceRNA) network. Molecular docking simulations were then conducted to explore the binding affinity and conformation of potential therapeutic drugs with hub genes. Results: Analysis of the left ventricular tissue expression profiles revealed that, compared to healthy controls, patients with dilated cardiomyopathy exhibited 234 differentially expressed genes and 2 genes related to myocardial energy metabolism. Additionally, Benzoylaconine may serve as a potential therapeutic agent for the treatment of dilated cardiomyopathy. Conclusion: The study findings highlight the crucial role of myocardial energy metabolism in the progression of Dilated Cardiomyopathy. Notably, Benzoylaconine emerges as a potential candidate for treating Dilated Cardiomyopathy, potentially exerting its therapeutic effects by targeted modulation of myocardial energy metabolism through NRK and NT5.

Elucidating the role of TWIST1 in ulcerative colitis: a comprehensive bioinformatics and machine learning approach

Background: Ulcerative colitis (UC) is a common and progressive inflammatory bowel disease primarily affecting the colon and rectum. Prolonged inflammation can lead to colitis-associated colorectal cancer (CAC). While the exact cause of UC remains unknown, this study aims to investigate the role of the TWIST1 gene in UC. Methods: Second-generation sequencing data from adult UC patients were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified, and characteristic genes were selected using machine learning and Lasso regression. The Receiver Operating Characteristic (ROC) curve assessed TWIST1's potential as a diagnostic factor (AUC score). Enriched pathways were analyzed, including Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Variation Analysis (GSVA). Functional mechanisms of marker genes were predicted, considering immune cell infiltration and the competing endogenous RNA (ceRNA) network. Results: We found 530 DEGs, with 341 upregulated and 189 downregulated genes. TWIST1 emerged as one of four potential UC biomarkers via machine learning. TWIST1 expression significantly differed in two datasets, GSE193677 and GSE83687, suggesting its diagnostic potential (AUC = 0.717 in GSE193677, AUC = 0.897 in GSE83687). Enrichment analysis indicated DEGs associated with TWIST1 were involved in processes like leukocyte migration, humoral immune response, and cell chemotaxis. Immune cell infiltration analysis revealed higher rates of M0 macrophages and resting NK cells in the high TWIST1 expression group, while TWIST1 expression correlated positively with M2 macrophages and resting NK cell infiltration. We constructed a ceRNA regulatory network involving 1 mRNA, 7 miRNAs, and 32 long non-coding RNAs (lncRNAs) to explore TWIST1's regulatory mechanism. Conclusion: TWIST1 plays a significant role in UC and has potential as a diagnostic marker. This study sheds light on UC's molecular mechanisms and underscores TWIST1's importance in its progression. Further research is needed to validate these findings in diverse populations and investigate TWIST1 as a therapeutic target in UC.

Application of Machine Learning in the Quantitative Analysis of the Surface Characteristics of Highly Abundant Cytoplasmic Proteins: Toward AI-Based Biomimetics

Proteins in the crowded environment of human cells have often been studied regarding nonspecific interactions, misfolding, and aggregation, which may cause cellular malfunction and disease. Specifically, proteins with high abundance are more susceptible to these issues due to the law of mass action. Therefore, the surfaces of highly abundant cytoplasmic (HAC) proteins directly exposed to the environment can exhibit specific physicochemical, structural, and geometrical characteristics that reduce nonspecific interactions and adapt to the environment. However, the quantitative relationships between the overall surface descriptors still need clarification. Here, we used machine learning to identify HAC proteins using hydrophobicity, charge, roughness, secondary structures, and B-factor from the protein surfaces and quantified the contribution of each descriptor. First, several supervised learning algorithms were compared to solve binary classification problems for the surfaces of HAC and extracellular proteins. Then, logistic regression was used for the feature importance analysis of descriptors considering model performance (80.2% accuracy and 87.6% AUC) and interpretability. The HAC proteins showed positive correlations with negatively and positively charged areas but negative correlations with hydrophobicity, the B-factor, the proportion of beta structures, roughness, and the proportion of disordered regions. Finally, the details of each descriptor could be explained concerning adaptative surface strategies of HAC proteins to regulate nonspecific interactions, protein folding, flexibility, stability, and adsorption. This study presented a novel approach using various surface descriptors to identify HAC proteins and provided quantitative design rules for the surfaces well-suited to human cellular crowded environments.

Metagenomic evidence for antibiotic-associated actinomycetes in the Karamay Gobi region

Due to the misuse of antibiotics, there is an increasing emergence and spread of multidrug-resistant (MDR) bacteria, leading to a human health crisis. To address clinical antibiotic resistance and prevent/control pathogenic microorganisms, the development of novel antibiotics is essential. This also offers a new approach to discovering valuable actinobacterial flora capable of producing natural bioactive products. In this study, we employed bioinformatics and macro-genome sequencing to collect 15 soil samples from three different locations in the Karamay Gobi region. First, we assessed the diversity of microorganisms in soil samples from different locations, analyzing the content of bacteria, archaea, actinomycetes, and fungi. The biodiversity of soil samples from outside the Gobi was found to be higher than that of soil samples from within and in the center of the Gobi. Second, through microbial interaction network analysis, we identified actinomycetes as the dominant group in the system. We have identified the top four antibiotic genes, such as Ecol_fabG_TRC, Efac_liaR_DAP, tetA (58), and macB, by CARD. These genes are associated with peptide antibiotics, disinfecting agents and antiseptics, tetracycline antibiotics, and macrolide antibiotics. In addition, we also obtained 40 other antibiotic-related genes through CARD alignment. Through in-depth analysis of desert soil samples, we identified several unstudied microbial species belonging to different families, including Erythrobacteriaceae, Solirubrobacterales, Thermoleophilaceae, Gaiellaceae, Nocardioidaceae, Actinomycetia, Egibacteraceae, and Acidimicrobiales. These species have the capability to produce peptide antibiotics, macrolide antibiotics, and tetracycline antibiotics, as well as disinfectants and preservatives. This study provides valuable theoretical support for future in-depth research.

Enhancing TCR specificity predictions by combined pan- and peptide-specific training, loss-scaling, and sequence similarity integration

Predicting the interaction between Major Histocompatibility Complex (MHC) class I-presented peptides and T-cell receptors (TCR) holds significant implications for vaccine development, cancer treatment, and autoimmune disease therapies. However, limited paired-chain TCR data, skewed towards well-studied epitopes, hampers the development of pan-specific machine-learning (ML) models. Leveraging a larger peptide-TCR dataset, we explore various alterations to the ML architectures and training strategies to address data imbalance. This leads to an overall improved performance, particularly for peptides with scant TCR data. However, challenges persist for unseen peptides, especially those distant from training examples. We demonstrate that such ML models can be used to detect potential outliers, which when removed from training, leads to augmented performance. Integrating pan-specific and peptide-specific models alongside with similarity-based predictions, further improves the overall performance, especially when a low false positive rate is desirable. In the context of the IMMREP22 benchmark, this modeling framework attained state-of-the-art performance. Moreover, combining these strategies results in acceptable predictive accuracy for peptides characterized with as little as 15 positive TCRs. This observation places great promise on rapidly expanding the peptide covering of the current models for predicting TCR specificity. The NetTCR 2.2 model incorporating these advances is available on GitHub (https://github.com/mnielLab/NetTCR-2.2) and as a web server at https://services.healthtech.dtu.dk/services/NetTCR-2.2/.

A single-cell genomic strategy for alternative transcript start sites identification

Alternative transcription start sites (TSSs) usage plays a critical role in gene transcription regulation in mammals. However, precisely identifying alternative TSSs remains challenging at the genome-wide level. We report a single-cell genomic technology for alternative TSSs annotation and cell heterogeneity detection. In the method, we utilize Fluidigm C1 system to capture individual cells of interest, SMARTer cDNA synthesis kit to recover full-length cDNAs, then dual priming oligonucleotide system to specifically enrich TSSs for genomic analysis. We apply this method to a genome-wide study of alternative TSSs identification in two different IFN-β stimulated mouse embryonic fibroblasts (MEFs). The data clearly discriminate two IFN-β stimulated MEFs. Moreover, our results indicate 81% expressed genes in these two cell types containing multiple TSSs, which is much higher than previous predictions based on Cap-Analysis Gene Expression (CAGE) (58%) or empirical determination (54%) in various cell types. This indicates that alternative TSSs are more pervasive than expected and implies our strategy could position them at an unprecedented sensitivity. It would be helpful for elucidating their biological insights in future.

Trioxidized cysteine in the aging proteome mimics the structural dynamics and interactome of phosphorylated serine

Aging is the primary risk factor for the development of numerous human chronic diseases. On a molecular level, it significantly impacts the regulation of protein modifications, leading to the accumulation of degenerative protein modifications (DPMs) such as aberrant serine phosphorylation (p-Ser) and trioxidized cysteine (t-Cys) within the proteome. The altered p-Ser is linked to abnormal cell signaling, while the accumulation of t-Cys is associated with chronic diseases induced by oxidative stress. Despite this, the potential cross-effects and functional interplay between these two critical molecular factors of aging remain undisclosed. This study analyzes the aging proteome of wild-type C57BL/6NTac mice over 2 years using advanced proteomics and bioinformatics. Our objective is to provide a comprehensive analysis of how t-Cys affects cell signaling and protein structure in the aging process. The results obtained indicate that t-Cys residues accumulate in the aging proteome, interact with p-Ser interacting enzymes, as validated in vitro, and alter their structures similarly to p-Ser. These findings have significant implications for understanding the interplay of oxidative stress and phosphorylation in the aging process. Additionally, they open new venues for further research on the role(s) of these protein modifications in various human chronic diseases and aging, wherein exacerbated oxidation and aberrant phosphorylation are implicated.

The value of a three-microRNA panel in serum for prostate cancer screening

BACKGROUND

Globally, prostate cancer is the second most common malignancy in males. Serum microRNAs (miRNAs) may function as non-invasive and innovative biomarkers for various cancers. Our study aimed to determine potential miRNAs for prostate cancer screening.

METHODS

A three-stage study was accomplished to ascertain crucial miRNAs as markers. In the screening stage, we searched PubMed for aberrantly expressed miRNAs relevant to prostate cancer and selected them as candidate miRNAs. In training and validation stages, with serum specimens from 112 prostate cancer patients and 112 healthy controls, expressions of candidate miRNAs were identified through quantitative reverse transcription-polymerase chain reaction. The diagnostic capabilities of miRNAs were determined by receiver operating characteristic curves. Bioinformatic analysis was utilized to explore the function of the critical miRNAs.

RESULTS

Expression of six serum miRNAs (miR-34b-3p, miR-556-5p, miR-200c-3p, miR-361-5p, miR-369-3p, miR-485-3p) were significantly altered in prostate cancer patients contrasted with healthy controls. The optimal combination of critical miRNAs is a three-miRNA panel (miR-34b-3p, miR-200c-3p, and miR-361-5p) with good diagnostic capability. FLRT2, KIAA1755, LDB3, and NTRK3 were identified as the potential genes targeted by the three-miRNA panel.

CONCLUSIONS

The three-miRNA panel may perform as an innovative and promising serum marker for prostate cancer screening.

Navigating the landscapes of spatial transcriptomics: How computational methods guide the way

Spatially resolved transcriptomics has been dramatically transforming biological and medical research in various fields. It enables transcriptome profiling at single-cell, multi-cellular, or sub-cellular resolution, while retaining the information of geometric localizations of cells in complex tissues. The coupling of cell spatial information and its molecular characteristics generates a novel multi-modal high-throughput data source, which poses new challenges for the development of analytical methods for data-mining. Spatial transcriptomic data are often highly complex, noisy, and biased, presenting a series of difficulties, many unresolved, for data analysis and generation of biological insights. In addition, to keep pace with the ever-evolving spatial transcriptomic experimental technologies, the existing analytical theories and tools need to be updated and reformed accordingly. In this review, we provide an overview and discussion of the current computational approaches for mining of spatial transcriptomics data. Future directions and perspectives of methodology design are proposed to stimulate further discussions and advances in new analytical models and algorithms. This article is categorized under: RNA Methods > RNA Analyses in Cells RNA Evolution and Genomics > Computational Analyses of RNA RNA Export and Localization > RNA Localization.

Identification of key biomarkers for predicting CAD progression in inflammatory bowel disease via machine-learning and bioinformatics strategies

The study aimed to identify the biomarkers for predicting coronary atherosclerotic lesions progression in patients with inflammatory bowel disease (IBD). Related transcriptome datasets were seized from Gene Expression Omnibus database. IBD-related modules were identified via Weighted Gene Co-expression Network Analysis. The 'Limma' was applied to screen differentially expressed genes between stable coronary artery disease (CAD) and acute myocardial infarction (AMI). Subsequently, we employed protein-protein interaction (PPI) network and three machine-learning strategies to further screen for candidate hub genes. Application of the receiver operating characteristics curve to quantitatively evaluate candidates to determine key diagnostic biomarkers, followed by a nomogram construction. Ultimately, we performed immune landscape analysis, single-gene GSEA and prediction of target-drugs. 3227 IBD-related module genes and 570 DEGs accounting for AMI were recognized. Intersection yielded 85 shared genes and mostly enriched in immune and inflammatory pathways. After filtering through PPI network and multi-machine learning algorithms, five candidate genes generated. Upon validation, CTSD, CEBPD, CYP27A1 were identified as key diagnostic biomarkers with a superior sensitivity and specificity (AUC > 0.8). Furthermore, all three genes were negatively correlated with CD4+ T cells and positively correlated with neutrophils. Single-gene GSEA highlighted the importance of pathogen invasion, metabolism, immune and inflammation responses during the pathogenesis of AMI. Ten target-drugs were predicted. The discovery of three peripheral blood biomarkers capable of predicting the risk of CAD proceeding into AMI in IBD patients. These identified biomarkers were negatively correlated with CD4+ T cells and positively correlated with neutrophils, indicating a latent therapeutic target.

Identification of key regulatory molecules in the early development stage of Alzheimer's disease

Alzheimer's disease (AD) is one of the most common neurodegenerative diseases, the incidence of which increases with age, and the pathological changes in the brain are irreversible. Recent studies have highlighted the essential role of long noncoding RNAs (lncRNAs) in AD by acting as competing endogenous RNAs (ceRNAs). Our aim was to construct lncRNA-associated ceRNA regulatory networks composed of potential biomarkers for the early stage of AD. AD related datasets come from AlzData and GEO databases. The R package 'Limma' identifies differentially expressed genes (DEGs), Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) databases for functional enrichment analysis. Protein-protein interactions (PPIs) in DEGs were constructed in the STRING database, and Cytoscape software identified DEGs. Convergent functional genomics (CFG) analysis of differentially expressed hub genes (referred to as early-DEGs) in the brain before the development of AD pathology. The AlzData database analyses the expression levels of early-DEGs in different nerve cells. The lncRNA-miRNA-mRNA regulatory network was established according to the ceRNA hypothesis. We identified four lncRNAs (XIST, NEAT1, KCNQ1OT1 and HCG18) and four miRNAs (hsa-let-7c-5p, hsa-miR-107, hsa-miR-129-2-3p and hsa-miR-214-3p) were preliminarily identified as potential biomarkers for early AD, competitively regulating Atp6v0b, Atp6v1e1 Atp6v1f and Syt1. This study indicates that NEAT1, XIST, HCG18 and KCNQ1OT1 act as ceRNAs in competitive binding with miRNAs to regulate the expression of Atp6v0b, Atp6v1e1, Atp6v1f and Syt1 before the occurrence of pathological changes in AD.

Comprehensive molecular and cellular characterization of endoplasmic reticulum stress-related key genes in renal ischemia/reperfusion injury

Background

Renal ischemia-reperfusion injury (RIRI) is an inevitable complication in the process of kidney transplantation and lacks specific therapy. The study aims to determine the underlying mechanisms of RIRI to uncover a promising target for efficient renoprotection.

Method

Four bulk RNA-seq datasets including 495 renal samples of pre- and post-reperfusion were collected from the GEO database. The machine learning algorithms were utilized to ascertain pivotal endoplasmic reticulum stress genes. Then, we incorporated correlation analysis and determined the interaction pathways of these key genes. Considering the heterogeneous nature of bulk-RNA analysis, the single-cell RNA-seq analysis was performed to investigate the mechanisms of key genes at the single-cell level. Besides, 4-PBA was applied to inhibit endoplasmic reticulum stress and hence validate the pathological role of these key genes in RIRI. Finally, three clinical datasets with transcriptomic profiles were used to assess the prognostic role of these key genes in renal allograft outcomes after RIRI.

Results

In the bulk-RNA analysis, endoplasmic reticulum stress was identified as the top enriched pathway and three endoplasmic reticulum stress-related genes (PPP1R15A, JUN, and ATF3) were ranked as top performers in both LASSO and Boruta analyses. The three genes were found to significantly interact with kidney injury-related pathways, including apoptosis, inflammatory response, oxidative stress, and pyroptosis. For oxidative stress, these genes were more strongly related to oxidative markers compared with antioxidant markers. In single-cell transcriptome, the three genes were primarily upregulated in endothelium, distal convoluted tubule cells, and collecting duct principal cells among 12 cell types of renal tissues in RIRI. Furthermore, distal convoluted tubule cells and collecting duct principal cells exhibited pro-inflammatory status and the highest pyroptosis levels, suggesting their potential as main effectors of three key genes for mediating RIRI-associated injuries. Importantly, inhibition of these key genes using 4-phenyl butyric acid alleviated functional and histological damage in a mouse RIRI model. Finally, the three genes demonstrated highly prognostic value in predicting graft survival outcomes.

Conclusion

The study identified three key endoplasmic reticulum stress-related genes and demonstrated their prognostic value for graft survival, providing references for individualized clinical prevention and treatment of postoperative complications after renal transplantation.

[Study on mechanism of icariin-induced ferroptosis in HepG2 hepatoma carcinoma cells through PPARG/FABP4/GPX4 pathway]

The aim of this study was to explore the mechanism of icaritin-induced ferroptosis in hepatoma HepG2 cells. By bioinformatics screening, the target of icariin's intervention in liver cancer ferroptosis was selected, the protein-protein interaction(PPI) network was constructed, the related pathways were focused, the binding ability of icariin and target protein was evaluated by molecular docking, and the impact on patients' survival prognosis was predicted and the clinical prediction model was built. CCK-8, EdU, and clonal formation assays were used to detect cell viability and cell proliferation; colorimetric method and BODIPY 581/591 C1 fluorescent probe were used to detect the levels of Fe~(2+), MDA and GSH in cells, and the ability of icariin to induce HCC cell ferroptosis was evaluated; RT-qPCR and Western blot detection were used to verify the mRNA and protein levels of GPX4, xCT, PPARG, and FABP4 to determine the expression changes of these ferroptosis-related genes in response to icariin. Six intervention targets(AR, AURKA, PPARG, AKR1C3, ALB, NQO1) identified through bioinformatic analysis were used to establish a risk scoring system that aids in estimating the survival prognosis of HCC patients. In conjunction with patient age and TNM staging, a comprehensive Nomogram clinical prediction model was developed to forecast the 1-, 3-, and 5-year survival of HCC patients. Experimental results revealed that icariin effectively inhibited the activity and proliferation of HCC cells HepG2, significantly modulating levels of Fe~(2+), MDA, and lipid peroxidation ROS while reducing GSH levels, hence revealing its potential to induce ferroptosis in HCC cells. Icariin was found to diminish the expression of GPX4 and xCT(P<0.01), inducing ferroptosis in HCC cells, potentially in relation to inhibition of PPARG and FABP4(P<0.01). In summary, icariin induces ferroptosis in HCC cells via the PPARG/FABP4/GPX4 pathway, providing an experimental foundation for utilizing the traditional Chinese medicine icariin in the prevention or treatment of HCC.

Accurate and sensitive mutational signature analysis with MuSiCal

Mutational signature analysis is a recent computational approach for interpreting somatic mutations in the genome. Its application to cancer data has enhanced our understanding of mutational forces driving tumorigenesis and demonstrated its potential to inform prognosis and treatment decisions. However, methodological challenges remain for discovering new signatures and assigning proper weights to existing signatures, thereby hindering broader clinical applications. Here we present Mutational Signature Calculator (MuSiCal), a rigorous analytical framework with algorithms that solve major problems in the standard workflow. Our simulation studies demonstrate that MuSiCal outperforms state-of-the-art algorithms for both signature discovery and assignment. By reanalyzing more than 2,700 cancer genomes, we provide an improved catalog of signatures and their assignments, discover nine indel signatures absent in the current catalog, resolve long-standing issues with the ambiguous 'flat' signatures and give insights into signatures with unknown etiologies. We expect MuSiCal and the improved catalog to be a step towards establishing best practices for mutational signature analysis.

CFTR expression in human salivary gland acinar cells

The key role of CFTR in secretory epithelia has been extensively documented. Additionally, CFTR plays a significant role in ion absorption in exocrine glands, including salivary and sweat glands. Most of the knowledge about CFTR expression comes from animal models such as the mouse or the rat, but there is limited information about CFTR expression in human tissues. In the present study, we assessed the expression of CFTR in human submandibular and parotid glands. Consistent with findings in rodent salivary glands, our immunolocalization studies show that CFTR is expressed in duct cells. However, CFTR expression in human salivary glands differs from that in rodents, as immunolocalization and single-cell RNA sequencing analysis from a previous study performed in the human parotid gland revealed the presence of CFTR protein and transcripts within a distinct cell cluster. Based on cell marker expression, this cluster corresponds to acinar cells. To obtain functional evidence supporting CFTR expression, we isolated human parotid acinar cells through collagenase digestion. Acinar cells displayed an anion conductance that was activated in response to cAMP-increasing agents and was effectively blocked by CFTRInh172, a known CFTR blocker. This study provides novel evidence of CFTR expression within acinar cells of human salivary glands. This finding challenges the established model positioning CFTR exclusively in duct cells from exocrine glands.NEW & NOTEWORTHY This study addresses the uncertainty about the impact of CFTR on human salivary gland function. We found CFTR transcripts in a subset of duct cells known as ionocytes, as well as in acinar cells. Isolated human parotid acinar cells exhibited Cl- conductance consistent with CFTR activity. This marks the first documented evidence of functional CFTR expression in human salivary gland acinar cells.

A novel bioinformatic method for the identification of antimicrobial peptides in metagenomes

AIMS

This study aimed to develop a new bioinformatic approach for the identification of novel antimicrobial peptides (AMPs), which did not depend on sequence similarity to known AMPs held within databases, but on structural mimicry of another antimicrobial compound, in this case an ultrashort, synthetic, cationic lipopeptide (C12-OOWW-NH2).

METHODS AND RESULTS

When applied to a collection of metagenomic datasets, our outlined bioinformatic method successfully identified several short (8-10aa) functional AMPs, the activity of which was verified via disk diffusion and minimum inhibitory concentration assays against a panel of 12 bacterial strains. Some peptides had activity comparable to, or in some cases, greater than, those from published studies that identified AMPs using more conventional methods. We also explored the effects of modifications, including extension of the peptides, observing an activity peak at 9-12aa. Additionally, the inclusion of a C-terminal amide enhanced activity in most cases. Our most promising candidate (named PB2-10aa-NH2) was thermally stable, lipid-soluble, and possessed synergistic activity with ethanol but not with a conventional antibiotic (streptomycin).

CONCLUSIONS

While several bioinformatic methods exist to predict AMPs, the approach outlined here is much simpler and can be used to quickly scan huge datasets. Searching for peptide sequences bearing structural similarity to other antimicrobial compounds may present a further opportunity to identify novel AMPs with clinical relevance, and provide a meaningful contribution to the pressing global issue of AMR.

The potential mechanism of ursolic acid in the treatment of bladder cancer based on network pharmacology and molecular docking

OBJECTIVE

This study explored the potential molecular mechanisms of ursolic acid (UA) in bladder cancer treatment using network pharmacology and molecular docking.

METHODS

The Traditional Chinese Medicine Systems Pharmacology and UniProt databases were used to screen potential targets of UA. Relevant bladder cancer target genes were extracted using the GeneCards database. All data were pooled and intercrossed to obtain common target genes of UA and bladder cancer. Gene Ontology functional annotation and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were performed. Molecular docking was conducted to verify the possible binding conformation between UA and bladder cancer cells. Then, in vitro experiments were performed to further validate the predicted results.

RESULTS

UA exerts anti-tumor effects on bladder cancer through multiple targets and pathways. Molecular docking indicated that UA undergoes stable binding with the proteins encoded by the top six core genes (STAT3, VEGFA, CASP3, TP53, IL1B, and CCND1). The in vitro experiments verified that UA can induce bladder cancer cell apoptosis by regulating the PI3K/Akt signaling pathway.

CONCLUSIONS

Our study illustrated the potential mechanism of UA in bladder cancer based on network pharmacology and molecular docking. The results will provide scientific references for follow-up studies and clinical treatment.

Advances in methods for tRNA sequencing and quantification

In the past decade tRNA sequencing (tRNA-seq) has attracted considerable attention as an important tool for the development of novel approaches to quantify highly modified tRNA species and to propel tRNA research aimed at understanding the cellular physiology and disease and development of tRNA-based therapeutics. Many methods are available to quantify tRNA abundance while accounting for modifications and tRNA charging/acylation. Advances in both library preparation methods and bioinformatic workflows have enabled developments in next-generation sequencing (NGS) workflows. Other approaches forgo NGS applications in favor of hybridization-based approaches. In this review we provide a brief comparative overview of various tRNA quantification approaches, focusing on the advantages and disadvantages of these methods, which together facilitate reliable tRNA quantification.

Immune infiltration is associated with pan-cancer prognostic biomarker RING finger protein 187

Cancer is associated with the highest mortality rate globally. While life-saving screening and treatments exist, better awareness is needed. RNF187, an E3 ligase regulating biological processes, belongs to the RING domain-containing E3 ligase family. RNF187 may serve as an oncogene due to abnormal expression in tumors. However, its association with immune infiltration and prognosis across various cancers remains unclear. We searched several databases including TCGA, GTE x, CCLE, TIMER, and GSEA. R software was used to evaluate RNF187 differential expression, survival, pathology stage, DNA methylation, tumor mutational burden (TMB), microsatellite instability (MSI), gene co-expression analysis, mismatch repairs (MMRs), tumor microenvironment (TME), and immune cell infiltration. Clinicopathological data were collected, and immunohistochemistry was used to verify RNF187 expression in tumor tissues. RNF187 expression was up-regulated in various cancers compared to that in normal tissues and associated with poor patient outcomes. Dysregulation of RNF187 expression in multiple cancer types was strongly correlated with DNA methylation, MMR, MSI, and TMB. RNF187 could interact with different immune cells in cancers. Biomarkers associated with RNF187 may be helpful for prognosis and immunology in treating pan-cancer patients.

Construction of an oxidative stress-associated genes signature in breast cancer by machine learning algorithms

OBJECTIVE

To construct a prognostic model of a breast cancer-related oxidative stress-related gene (OSRG) signature using machine learning algorithms.

METHODS

The OSRGs of breast cancer were constructed by least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analysis. The Cancer Genome Atlas (TCGA) was used to analyse the gene expression and prognostic value. The Human Protein Atlas was used to analyse the protein expression of hub genes. Receiver operating characteristic analysis, calibration curve and decision curve analysis were used to predict the stability of this model.

RESULTS

The area under the curve of 1-, 3- and 5-year overall survival were 0.751, 0.707 and 0.645 in the TCGA training dataset; and 0.692, 0.678 and 0.602 in the TCGA testing dataset, respectively. Calibration plot showed good agreement between predicted probabilities and observed outcomes. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Set Enrichment Analysis (GSEA) pathway analysis indicated that multiple cancer-related pathways were highly enriched in the high-risk group. Immune infiltration analysis showed immune cells and their functions may play a key role in the development and mechanism of breast cancer.

CONCLUSIONS

This new OSRG signature was associated with the immune infiltration and it might be useful in predicting the prognosis in patients with breast cancer.

Microbial Community Profiling Protocol with Full-length 16S rRNA Sequences and Emu

16S rRNA targeted amplicon sequencing is an established standard for elucidating microbial community composition. While high-throughput short-read sequencing can elicit only a portion of the 16S rRNA gene due to their limited read length, third generation sequencing can read the 16S rRNA gene in its entirety and thus provide more precise taxonomic classification. Here, we present a protocol for generating full-length 16S rRNA sequences with Oxford Nanopore Technologies (ONT) and a microbial community profile with Emu. We select Emu for analyzing ONT sequences as it leverages information from the entire community to overcome errors due to incomplete reference databases and hardware limitations to ultimately obtain species-level resolution. This pipeline provides a low-cost solution for characterizing microbiome composition by exploiting real-time, long-read ONT sequencing and tailored software for accurate characterization of microbial communities. © 2024 Wiley Periodicals LLC. Basic Protocol: Microbial community profiling with Emu Support Protocol 1: Full-length 16S rRNA microbial sequences with Oxford Nanopore Technologies sequencing platform Support Protocol 2: Building a custom reference database for Emu.

Xanthomonas rydalmerensis sp. nov., a non-pathogenic member of Group 1 Xanthomonas

Five bacterial isolates were isolated from Fragaria × ananassa in 1976 in Rydalmere, Australia, during routine biosecurity surveillance. Initially, the results of biochemical characterisation indicated that these isolates represented members of the genus Xanthomonas. To determine their species, further analysis was conducted using both phenotypic and genotypic approaches. Phenotypic analysis involved using MALDI-TOF MS and BIOLOG GEN III microplates, which confirmed that the isolates represented members of the genus Xanthomonas but did not allow them to be classified with respect to species. Genome relatedness indices and the results of extensive phylogenetic analysis confirmed that the isolates were members of the genus Xanthomonas and represented a novel species. On the basis the minimal presence of virulence-associated factors typically found in genomes of members of the genus Xanthomonas, we suggest that these isolates are non-pathogenic. This conclusion was supported by the results of a pathogenicity assay. On the basis of these findings, we propose the name Xanthomonas rydalmerensis, with DAR 34855T = ICMP 24941 as the type strain.

Community recommendations on cryoEM data archiving and validation

In January 2020, a workshop was held at EMBL-EBI (Hinxton, UK) to discuss data requirements for the deposition and validation of cryoEM structures, with a focus on single-particle analysis. The meeting was attended by 47 experts in data processing, model building and refinement, validation, and archiving of such structures. This report describes the workshop's motivation and history, the topics discussed, and the resulting consensus recommendations. Some challenges for future methods-development efforts in this area are also highlighted, as is the implementation to date of some of the recommendations.

BANKSY unifies cell typing and tissue domain segmentation for scalable spatial omics data analysis

Spatial omics data are clustered to define both cell types and tissue domains. We present Building Aggregates with a Neighborhood Kernel and Spatial Yardstick (BANKSY), an algorithm that unifies these two spatial clustering problems by embedding cells in a product space of their own and the local neighborhood transcriptome, representing cell state and microenvironment, respectively. BANKSY's spatial feature augmentation strategy improved performance on both tasks when tested on diverse RNA (imaging, sequencing) and protein (imaging) datasets. BANKSY revealed unexpected niche-dependent cell states in the mouse brain and outperformed competing methods on domain segmentation and cell typing benchmarks. BANKSY can also be used for quality control of spatial transcriptomics data and for spatially aware batch effect correction. Importantly, it is substantially faster and more scalable than existing methods, enabling the processing of millions of cell datasets. In summary, BANKSY provides an accurate, biologically motivated, scalable and versatile framework for analyzing spatially resolved omics data.