Gene Ontology: Pitfalls, Biases, and Remedies

A longitudinal analysis of function annotations of the human proteome reveals consistently high biases

The resources required to study gene function are limited, especially when considering the number of genes in the human genome and the complexity of their function. Therefore, genes are prioritized for experimental studies based on many different considerations, including, but not limited to, perceived biomedical importance, such as disease-associated genes, or the understanding of biological processes, such as cell signalling pathways. At the same time, most genes are not studied or are under-characterized, which hampers our understanding of their function and potential effects on human health and wellness. Understanding function annotation disparity is a necessary first step toward understanding how much functional knowledge is gained from the human genome, and toward guidelines for better targeting future studies of the genes in the human genome effectively. Here, we present a comprehensive longitudinal analysis of the human proteome utilizing data analysis tools from economics and information theory. Specifically, we view the human proteome as a population of proteins within a knowledge economy: we treat the quantified knowledge of the protein's function as the analogue of wealth and examine the distribution of information in a population of proteins in the proteome in the same manner distribution of wealth is studied in societies. Our results show a highly skewed distribution of information about human proteins over the last decade, in which the inequality in the annotations given to the proteins remains high. Additionally, we examine the correlation between the knowledge about protein function as captured in databases and the interest in proteins as reflected by mentions in the scientific literature. We show a large gap between knowledge and interest and dissect the factors leading to this gap. In conclusion, our study shows that research efforts should be redirected to less studied proteins to mitigate the disparity among human proteins both in databases and literature.

Cell differentiation controls iron assimilation in the choanoflagellate Salpingoeca rosetta

Marine microeukaryotes have evolved diverse cellular features that link their life histories to surrounding environments. How those dynamic life histories intersect with the ecological functions of microeukaryotes remains a frontier to understanding their roles in critical biogeochemical cycles. Choanoflagellates, phagotrophs that cycle nutrients through filter feeding, provide models to explore this intersection, for many choanoflagellate species transition between life history stages by differentiating into distinct cell types. Here, we report that cell differentiation in the marine choanoflagellate Salpingoeca rosetta endows one of its cell types with the ability to utilize insoluble ferric colloids. These colloids are a predominant form of iron in marine environments and are largely inaccessible to cell-walled microbes. Therefore, choanoflagellates and other phagotrophic eukaryotes may serve critical ecological roles by cycling this essential nutrient through iron utilization pathways. We found that S. rosetta can utilize these ferric colloids via the expression of a cytochrome b561 iron reductase (cytb561a). This gene and its mammalian ortholog, the duodenal cytochrome b561 (DCYTB) that reduces ferric cations for uptake in gut epithelia, belong to a subgroup of cytochrome b561 proteins with distinct biochemical features that contribute to iron reduction activity. Overall, our findings provide insight into the ecological roles choanoflagellates perform and inform reconstructions of early animal evolution where functionally distinct cell types became an integrated whole at the origin of animal multicellularity.

IMPORTANCE

This study examines how cell differentiation in a choanoflagellate enables the uptake of iron, an essential nutrient. Choanoflagellates are widespread, aquatic microeukaryotes that are the closest living relatives of animals. Similar to their animal relatives, we found that the model choanoflagellate, S. rosetta, divides metabolic functions between distinct cell types. One cell type uses an iron reductase to acquire ferric colloids, a key source of iron in the ocean. We also observed that S. rosetta has three variants of this reductase, each with distinct biochemical properties that likely lead to differences in how they reduce iron. These reductases are variably distributed across ocean regions, suggesting a role for choanoflagellates in cycling iron in marine environments.

Biological databases in the age of generative artificial intelligence

Summary

Modern biological research critically depends on public databases. The introduction and propagation of errors within and across databases can lead to wasted resources as scientists are led astray by bad data or have to conduct expensive validation experiments. The emergence of generative artificial intelligence systems threatens to compound this problem owing to the ease with which massive volumes of synthetic data can be generated. We provide an overview of several key issues that occur within the biological data ecosystem and make several recommendations aimed at reducing data errors and their propagation. We specifically highlight the critical importance of improved educational programs aimed at biologists and life scientists that emphasize best practices in data engineering. We also argue for increased theoretical and empirical research on data provenance, error propagation, and on understanding the impact of errors on analytic pipelines. Furthermore, we recommend enhanced funding for the stewardship and maintenance of public biological databases.

Availability and implementation

Not applicable.

Convergent evolution of noncoding elements associated with short tarsus length in birds

BACKGROUND

Convergent evolution is the independent evolution of similar traits in unrelated lineages across the Tree of Life. Various genomic signatures can help identify cases of convergent evolution at the molecular level, including changes in substitution rate in the same genes or gene networks. In this study, utilizing tarsus measurements of ~ 5400 species of birds, we identify independent shifts in tarsus length and use both comparative genomic and population genetic data to identify convergent evolutionary changes among focal clades with shifts to shorter optimal tarsus length.

RESULTS

Using a newly generated, comprehensive and broadly accessible set of 932,467 avian conserved non-exonic elements (CNEEs) and a whole-genome alignment of 79 birds, we find strong evidence for convergent acceleration in short-tarsus clades among 14,422 elements. Analysis of 9854 protein-coding genes, however, yielded no evidence of convergent patterns of positive selection. Accelerated elements in short-tarsus clades are concentrated near genes with functions in development, with the strongest enrichment associated with skeletal system development. Analysis of gene networks supports convergent changes in regulation of broadly homologous limb developmental genes and pathways.

CONCLUSIONS

Our results highlight the important role of regulatory elements undergoing convergent acceleration in convergent skeletal traits and are consistent with previous studies showing the roles of regulatory elements and skeletal phenotypes.

Identification of critical biomarkers and immune infiltration in preeclampsia through bioinformatics and machine learning methods

BACKGROUND

Preeclampsia (PE) is a multisystem progressive disease that occurs during pregnancy. Previous studies have shown that the immune system is involved in the placental trophoblast function and the pathological process of uterine vascular remodeling in PE. However, its molecular mechanism is still unclear. This study aimed to identify critical genes and immune cells involved in the pathological process of PE.

METHODS

The PE-related GSE74341 and GSE160888 datasets were downloaded from the Gene Expression Omnibus (GEO) database, and differential expression analysis, weighted gene co-expression network analysis (WGCNA), and least absolute shrinkage and selection operator (LASSO) analysis were combined to screen the PE-related DEGs. Receiver operating characteristic (ROC) analysis was used to evaluate the diagnostic specificity of obtained DEGs, and the GSE35574 dataset was used for preliminary validation. Furthermore, the single-sample Gene Set Enrichment Analysis (ssGSEA) was used to elucidate the correlation between the DEGs and the 28 types of infiltrating immune cells in PE. Real-time reverse transcription polymerase chain reaction (RT-PCR) was used to verify the differential expression of DEGs in the PE placental tissues.

RESULTS

A total of 143 DEGs (DE-mRNAs) were screened using the PE datasets. The analysis of DEG modules and LASSO logistic regression were used to identify high-temperature requirement factor A4 (HtrA4), tumour suppressor candidate 3 (TUSC3), endothelial protein C receptor gene (PROCR), claudin 3 (CLDN3), and thioredoxin binding protein (TXNIP) as the hub DEGs in PE. Furthermore, validation with the GSE35574 dataset and ROC analysis was used to clarify that the HTRA4, PROCR, and TXNIP genes are potential markers of PE and are closely related to the infiltrating immune cells in PE, such as gamma delta T cells, mast cells, natural killer cells, and T follicular helper cells. Finally, differential HTRA4, PROCR, and TXNIP expression were confirmed in PE placental tissues (p < 0.001).

CONCLUSION

HTRA4, PROCR, and TXNIP can be used as potential PE biomarkers to provide a new strategy for early diagnosing and treating PE.

Supersaturation mutagenesis reveals adaptive rewiring of essential genes among malaria parasites

Malaria parasites are highly divergent from model eukaryotes. Large-scale genome engineering methods effective in model organisms are frequently inapplicable, and systematic studies of gene function are few. We generated more than 175,000 transposon insertions in the Plasmodium knowlesi genome, averaging an insertion every 138 base pairs, and used this "supersaturation" mutagenesis to score essentiality for 98% of genes. The density of mutations allowed mapping of putative essential domains within genes, providing a completely new level of genome annotation for any Plasmodium species. Although gene essentiality was largely conserved across P. knowlesi, Plasmodium falciparum, and rodent malaria model Plasmodium berghei, a large number of shared genes are differentially essential, revealing species-specific adaptations. Our results indicated that Plasmodium essential gene evolution was conditionally linked to adaptive rewiring of metabolic networks for different hosts.

Thermal variation influences the transcriptome of the major malaria vector Anopheles stephensi

The distribution and abundance of ectothermic mosquitoes are strongly affected by temperature, but mechanisms remain unexplored. We describe the effect of temperature on the transcriptome of Anopheles stephensi, an invasive vector of human malaria. Adult females were maintained across a range of mean temperatures (20 °C, 24 °C and 28 °C), with daily fluctuations of +5 °C and -4 °C at each mean temperature. Transcriptomes were described up to 19 days post-blood meal. Of the >3100 differentially expressed genes, we observed shared temporal expression profiles across all temperatures, suggesting their indispensability to mosquito life history. Tolerance to 20 and 28 ( + 5°C/-4°C) was associated with larger and more diverse transcriptomes compared to 24 ( + 5 °C/-4 °C). Finally, we identified two distinct trends in gene expression in response to blood meal ingestion, oxidative stress, and reproduction. Our work has implications for mosquitoes' response to thermal variation, mosquito immune-physiology, mosquito-malaria interactions and the development of vector control tools.

Nucleosome repositioning in cardiac reprogramming

Early events in the reprogramming of fibroblasts to cardiac muscle cells are unclear. While various histone undergo modification and re-positioning, and these correlate with the activity of certain genes, it is unknown if these events are causal or happen in response to reprogramming. Histone modification and re-positioning would be expected to open up chromatin on lineage-specific genes and this can be ascertained by studying nucleosome architecture. We have recently developed a set of tools to identify significant changes in nucleosome architecture which we used to study skeletal muscle differentiation. In this report, we have applied these tools to understand nucleosome architectural changes during fibroblast to cardiac muscle reprogramming. We found that nucleosomes surrounding the transcription start sites of cardiac muscle genes induced during reprogramming were insensitive to reprogramming factors as well as to agents which enhance reprogramming efficacy. In contrast, significant changes in nucleosome architecture were observed distal to the transcription start site. These regions were associated with nucleosome build-up. In summary, investigations into nucleosome structure do not support the notion that fibroblasts to cardiac muscle cell reprogramming involves chromatin opening and suggests instead long-range effects such as breaking closed-loop inhibition.

Transcription factor prediction using protein 3D secondary structures

MOTIVATION

Transcription factors (TFs) are DNA-binding proteins that regulate gene expression. Traditional methods predict a protein as a TF if the protein contains any DNA-binding domains (DBDs) of known TFs. However, this approach fails to identify a novel TF that does not contain any known DBDs. Recently proposed TF prediction methods do not rely on DBDs. Such methods use features of protein sequences to train a machine learning model, and then use the trained model to predict whether a protein is a TF or not. Because the 3-dimensional (3D) structure of a protein captures more information than its sequence, using 3D protein structures will likely allow for more accurate prediction of novel TFs.

RESULTS

We propose a deep learning-based TF prediction method (StrucTFactor), which is the first method to utilize 3D secondary structural information of proteins. We compare StrucTFactor with recent state-of-the-art TF prediction methods based on ∼525 000 proteins across 12 datasets, capturing different aspects of data bias (including sequence redundancy) possibly influencing a method's performance. We find that StrucTFactor significantly (P-value < 0.001) outperforms the existing TF prediction methods, improving the performance over its closest competitor by up to 17% based on Matthews correlation coefficient.

AVAILABILITY AND IMPLEMENTATION

Data and source code are available at https://github.com/lieboldj/StrucTFactor and on our website at https://apps.cosy.bio/StrucTFactor.

Harnessing full-text publications for deep insights into C. elegans and Drosophila biomaps

In the rapidly expanding domain of scientific research, tracking and synthesizing information from the rapidly increasing volume of publications pose significant challenges. To address this, we introduce a novel high-throughput pipeline that employs ChatGPT to systematically extract and analyze connectivity information from the full-texts and abstracts of 24,237 and 150,538 research publications concerning Caenorhabditis elegans and Drosophila melanogaster, respectively. This approach has effectively identified 200,219 and 1,194,587 interactions within the C. elegans and Drosophila biomaps, respectively. Utilizing Cytoscape Web, we have developed a searchable online biomaps that link relevant keywords to their corresponding PubMed IDs, thus providing seamless access to an extensive knowledge network encompassing C. elegans and Drosophila. Our work highlights the transformative potential of integrating artificial intelligence with bioinformatics to deepen our understanding of complex biological systems. By revealing the intricate web of relationships among key entities in C. elegans and Drosophila, we offer invaluable insights that promise to propel advancements in genetics, developmental biology, neuroscience, longevity, and beyond. We also provide details and discuss significant nodes within both biomaps, including the insulin/IGF-1 signaling (IIS) and the notch pathways. Our innovative methodology sets a robust foundation for future research aimed at unravelling complex biological networks across diverse organisms. The two databases are available at worm.bio-map.com and drosophila.bio-map.com.

Nucleosome repositioning in cardiac reprogramming

Early events in the reprogramming of fibroblasts to cardiac muscle cells are unclear. While various histone undergo modification and re-positioning, and these correlate with the activity of certain genes, it is unknown if these events are causal or happen in response to reprogramming. Histone modification and re-positioning would be expected to open up chromatin on lineage-specific genes and this can be ascertained by studying nucleosome architecture. We have recently developed a set of tools to identify significant changes in nucleosome architecture which we used to study skeletal muscle differentiation. In this report, we have applied these tools to understand nucleosome architectural changes during fibroblast to cardiac muscle reprogramming. We found that nucleosomes surrounding the transcription start sites of cardiac muscle genes induced during reprogramming were insensitive to reprogramming factors as well as to agents which enhance reprogramming efficacy. In contrast, significant changes in nucleosome architecture were observed distal to the transcription start site. These regions were associated with nucleosome build-up. In summary, investigations into nucleosome structure do not support the notion that fibroblasts to cardiac muscle cell reprogramming involves chromatin opening and suggests instead long-range effects such as breaking closed-loop inhibition.

A metagene based similarity network fusion approach for multi-omics data integration identified novel subtypes in renal cell carcinoma

Renal cell carcinoma (RCC) ranks among the most prevalent cancers worldwide, with both incidence and mortality rates increasing annually. The heterogeneity among RCC patients presents considerable challenges for developing universally effective treatment strategies, emphasizing the necessity of in-depth research into RCC's molecular mechanisms, understanding the variations among RCC patients and further identifying distinct molecular subtypes for precise treatment. We proposed a metagene-based similarity network fusion (Meta-SNF) method for RCC subtype identification with multi-omics data, using a non-negative matrix factorization technique to capture alternative structures inherent in the dataset as metagenes. These latent metagenes were then integrated to construct a fused network under the Similarity Network Fusion (SNF) framework for more precise subtyping. We conducted simulation studies and analyzed real-world data from two RCC datasets, namely kidney renal clear cell carcinoma (KIRC) and kidney renal papillary cell carcinoma (KIRP) to demonstrate the utility of Meta-SNF. The simulation studies indicated that Meta-SNF achieved higher accuracy in subtype identification compared with the original SNF and other state-of-the-art methods. In analyses of real data, Meta-SNF produced more distinct and well-separated clusters, classifying both KIRC and KIRP into four subtypes with significant differences in survival outcomes. Subsequently, we performed comprehensive bioinformatics analyses focused on subtypes with poor prognoses in KIRC and KIRP and identified several potential biomarkers. Meta-SNF offers a novel strategy for subtype identification using multi-omics data, and its application to RCC datasets has yielded diverse biological insights which are highly valuable for informing clinical decision-making processes in the treatment of RCC.

Cell differentiation controls iron assimilation in a choanoflagellate

Marine microeukaryotes have evolved diverse cellular features that link their life histories to surrounding environments. How those dynamic life histories intersect with the ecological functions of microeukaryotes remains a frontier to understand their roles in essential biogeochemical cycles1,2. Choanoflagellates, phagotrophs that cycle nutrients through filter feeding, provide models to explore this intersection, for many choanoflagellate species transition between life history stages by differentiating into distinct cell types3-6. Here we report that cell differentiation in the marine choanoflagellate Salpingoeca rosetta endows one of its cell types with the ability to utilize insoluble ferric colloids for improved growth through the expression of a cytochrome b561 iron reductase (cytb561a). This gene is an ortholog of the mammalian duodenal cytochrome b561 (DCYTB) that reduces ferric cations prior to their uptake in gut epithelia7 and is part of an iron utilization toolkit that choanoflagellates and their closest living relatives, the animals, inherited from a last common eukaryotic ancestor. In a database of oceanic metagenomes8,9, the abundance of cytb561a transcripts from choanoflagellates positively correlates with upwellings, which are a major source of ferric colloids in marine environments10. As this predominant form of iron11,12 is largely inaccessible to cell-walled microbes13,14, choanoflagellates and other phagotrophic eukaryotes may serve critical ecological roles by first acquiring ferric colloids through phagocytosis and then cycling this essential nutrient through iron utilization pathways13-15. These findings provide insight into the ecological roles choanoflagellates perform and inform reconstructions of early animal evolution where functionally distinct cell types became an integrated whole at the origin of animal multicellularity16-22.

Artificial intelligence-driven multiomics predictive model for abdominal aortic aneurysm subtypes to identify heterogeneous immune cell infiltration and predict disease progression

BACKGROUND

Abdominal aortic aneurysm (AAA) poses a significant health risk and is influenced by various compositional features. This study aimed to develop an artificial intelligence-driven multiomics predictive model for AAA subtypes to identify heterogeneous immune cell infiltration and predict disease progression. Additionally, we investigated neutrophil heterogeneity in patients with different AAA subtypes to elucidate the relationship between the immune microenvironment and AAA pathogenesis.

METHODS

This study enrolled 517 patients with AAA, who were clustered using k-means algorithm to identify AAA subtypes and stratify the risk. We utilized residual convolutional neural network 200 to annotate and extract contrast-enhanced computed tomography angiography images of AAA. A precise predictive model for AAA subtypes was established using clinical, imaging, and immunological data. We performed a comparative analysis of neutrophil levels in the different subgroups and immune cell infiltration analysis to explore the associations between neutrophil levels and AAA. Quantitative polymerase chain reaction, Western blotting, and enzyme-linked immunosorbent assay were performed to elucidate the interplay between CXCL1, neutrophil activation, and the nuclear factor (NF)-κB pathway in AAA pathogenesis. Furthermore, the effect of CXCL1 silencing with small interfering RNA was investigated.

RESULTS

Two distinct AAA subtypes were identified, one clinically more severe and more likely to require surgical intervention. The CNN effectively detected AAA-associated lesion regions on computed tomography angiography, and the predictive model demonstrated excellent ability to discriminate between patients with the two identified AAA subtypes (area under the curve, 0.927). Neutrophil activation, AAA pathology, CXCL1 expression, and the NF-κB pathway were significantly correlated. CXCL1, NF-κB, IL-1β, and IL-8 were upregulated in AAA. CXCL1 silencing downregulated NF-κB, interleukin-1β, and interleukin-8.

CONCLUSION

The predictive model for AAA subtypes demonstrated accurate and reliable risk stratification and clinical management. CXCL1 overexpression activated neutrophils through the NF-κB pathway, contributing to AAA development. This pathway may, therefore, be a therapeutic target in AAA.

Fine-tuning protein embeddings for functional similarity evaluation

MOTIVATION

Proteins with unknown function are frequently compared to better characterized relatives, either using sequence similarity, or recently through similarity in a learned embedding space. Through comparison, protein sequence embeddings allow for interpretable and accurate annotation of proteins, as well as for downstream tasks such as clustering for unsupervised discovery of protein families. However, it is unclear whether embeddings can be deliberately designed to improve their use in these downstream tasks.

RESULTS

We find that for functional annotation of proteins, as represented by Gene Ontology (GO) terms, direct fine-tuning of language models on a simple classification loss has an immediate positive impact on protein embedding quality. Fine-tuned embeddings show stronger performance as representations for K-nearest neighbor classifiers, reaching stronger performance for GO annotation than even directly comparable fine-tuned classifiers, while maintaining interpretability through protein similarity comparisons. They also maintain their quality in related tasks, such as rediscovering protein families with clustering.

AVAILABILITY AND IMPLEMENTATION

github.com/mofradlab/go_metric.

Exploring ABHD5 as a Lipid-Related Biomarker in Idiopathic Pulmonary Fibrosis: Integrating Machine Learning, Bioinformatics, and In Vitro Experiments

Recent studies increasingly suggest a connection between lipids and idiopathic pulmonary fibrosis (IPF). This study was aimed at exploring potential lipid-related biomarkers for IPF and uncovering the mechanisms underlying pulmonary fibrosis. IPF-related datasets were retrieved from the GEO database, and the ComBat algorithm was used to merge multiple datasets and eliminate batch effects. Weighted gene co-expression network analysis (WGCNA) was utilized to identify modules and genes associated with IPF. Potential hub genes were determined by intersecting these genes with lipid-related genes from the GeneCards database. A machine learning-based integrative approach was developed to construct diagnostic and prognostic signatures, which were validated across several datasets. Additionally, single-cell sequencing data was used to validate the expression differences of core IPF-related genes across various cell types. The effect of ABHD5 on fibroblasts was assessed using the cell counting kit-8, 5-ethynyl-2'-deoxyuridine, and cell scratch assays. The expression levels of fibrotic factors were measured using real-time quantitative polymerase chain reaction and western blot analysis. WGCNA identified a red module potentially related to IPF, and the intersection with lipid-related genes yielded 51 hub genes. These genes were used to build diagnostic and prognostic models that demonstrated robust validation capabilities across multiple datasets. Single-cell sequencing analysis revealed low expression of ABHD5 in the lung tissues of IPF patients, with a higher proportion of fibroblasts exhibiting low ABHD5 expression. Cell experiments showed that under the influence of TGF-β1, knockdown of ABHD5 slightly promoted fibroblast proliferation. Additionally, fibroblasts with low ABHD5 expression exhibited enhanced migratory capabilities and secreted more fibrotic factors. Lipid-related diagnostic and prognostic models for IPF were developed, and ABHD5 may serve as a potential biomarker. Low ABHD5 expression could potentially accelerate the progression of pulmonary fibrosis.

FORWARD: A Learning Framework for Logical Network Perturbations to Prioritize Targets for Drug Development

Despite advances in artificial intelligence (AI), target-based drug development remains a costly, complex and imprecise process. We introduce F.O.R.W.A.R.D [ Framework for Outcome-based Research and Drug Development ], a network-based target prioritization approach and test its utility in the challenging therapeutic area of Inflammatory Bowel Diseases (IBD), which is a chronic condition of multifactorial origin. F.O.R.W.A.R.D leverages real-world outcomes, using a machine-learning classifier trained on transcriptomic data from seven prospective randomized clinical trials involving four drugs. It establishes a molecular signature of remission as the therapeutic goal and computes, by integrating principles of network connectivity, the likelihood that a drug's action on its target(s) will induce the remission-associated genes. Benchmarking F.O.R.W.A.R.D against 210 completed clinical trials on 52 targets showed a perfect predictive accuracy of 100%. The success of F.O.R.W.A.R.D was achieved despite differences in targets, mechanisms, and trial designs. F.O.R.W.A.R.D-driven in-silico phase '0' trials revealed its potential to inform trial design, justify re-trialing failed drugs, and guide early terminations. With its extendable applications to other therapeutic areas and its iterative refinement with emerging trials, F.O.R.W.A.R.D holds the promise to transform drug discovery by generating foresight from hindsight and impacting research and development as well as human-in-the-loop clinical decision-making.

Multiplexed imaging to reveal tissue dendritic cell spatial localisation and function

Dendritic cells (DCs) play a pivotal role in immune surveillance, acting as sentinels that coordinate immune responses within tissues. Although differences in the identity and functional states of DC subpopulations have been identified through multiparametric flow cytometry and single-cell RNA sequencing, these methods do not provide information about the spatial context in which the cells are located. This knowledge is crucial for understanding tissue organisation and cellular cross-talk. Recent developments in multiplex imaging techniques can now offer insights into this complex spatial and functional landscape. This review provides a concise overview of these imaging methodologies, emphasising their application in identifying DCs to delineate their tissue-specific functions and aiding newcomers in navigating this field.

DNA Electrotransfer Regulates Molecular Functions in Skeletal Muscle

Background

Tissues, such as skeletal muscle, have been targeted for the delivery of plasmid DNA (pDNA) encoding vaccines and therapeutics. The application of electric pulses (electroporation or electrotransfer) increases cell membrane permeability to enhance plasmid delivery and expression. However, the molecular effects of DNA electrotransfer on the muscle tissue are poorly characterized.

Materials and Methods

Four hours after intramuscular plasmid electrotransfer, we evaluated gene expression changes by RNA sequencing. Differentially expressed genes were analyzed by gene ontology (GO) pathway enrichment analysis.

Results

GO analysis highlighted many enriched molecular functions. The terms regulated by pulse application were related to muscle stress, the cytoskeleton and inflammation. The terms regulated by pDNA injection were related to a DNA-directed response and its control. Several terms regulated by pDNA electrotransfer were similar to those regulated by pulse application. However, the terms related to pDNA injection differed, focusing on entry of the plasmid into the cells and intracellular trafficking.

Conclusion

Each muscle stimulus resulted in specific regulated molecular functions. Identifying the unique intrinsic molecular changes driven by intramuscular DNA electrotransfer will aid in the design of preventative and therapeutic gene therapies.

Automated annotation of disease subtypes

BACKGROUND

Distinguishing diseases into distinct subtypes is crucial for study and effective treatment strategies. The Open Targets Platform (OT) integrates biomedical, genetic, and biochemical datasets to empower disease ontologies, classifications, and potential gene targets. Nevertheless, many disease annotations are incomplete, requiring laborious expert medical input. This challenge is especially pronounced for rare and orphan diseases, where resources are scarce.

METHODS

We present a machine learning approach to identifying diseases with potential subtypes, using the approximately 23,000 diseases documented in OT. We derive novel features for predicting diseases with subtypes using direct evidence. Machine learning models were applied to analyze feature importance and evaluate predictive performance for discovering both known and novel disease subtypes.

RESULTS

Our model achieves a high (89.4%) ROC AUC (Area Under the Receiver Operating Characteristic Curve) in identifying known disease subtypes. We integrated pre-trained deep-learning language models and showed their benefits. Moreover, we identify 515 disease candidates predicted to possess previously unannotated subtypes.

CONCLUSIONS

Our models can partition diseases into distinct subtypes. This methodology enables a robust, scalable approach for improving knowledge-based annotations and a comprehensive assessment of disease ontology tiers. Our candidates are attractive targets for further study and personalized medicine, potentially aiding in the unveiling of new therapeutic indications for sought-after targets.

Genome sequences and population genomics reveal climatic adaptation and genomic divergence between two closely related sweetgum species

Understanding the genetic basis of population divergence and adaptation is an important goal in population genetics and evolutionary biology. However, the relative roles of demographic history, gene flow, and/or selective regime in driving genomic divergence, climatic adaptation, and speciation in non-model tree species are not yet fully understood. To address this issue, we generated whole-genome resequencing data of Liquidambar formosana and L. acalycina, which are broadly sympatric but altitudinally segregated in the Tertiary relict forests of subtropical China. We integrated genomic and environmental data to investigate the demographic history, genomic divergence, and climatic adaptation of these two sister species. We inferred a scenario of allopatric species divergence during the late Miocene, followed by secondary contact during the Holocene. We identified multiple genomic islands of elevated divergence that mainly evolved through divergence hitchhiking and recombination rate variation, likely fostered by long-term refugial isolation and recent differential introgression in low-recombination genomic regions. We also found some candidate genes with divergent selection signatures potentially involved in climatic adaptation and reproductive isolation. Our results contribute to a better understanding of how late Tertiary/Quaternary climatic change influenced speciation, genomic divergence, climatic adaptation, and introgressive hybridization in East Asia's Tertiary relict flora. In addition, they should facilitate future evolutionary, conservation genomics, and molecular breeding studies in Liquidambar, a genus of important medicinal and ornamental values.

Decoding bee cleptoparasitism through comparative transcriptomics of Coelioxoides waltheriae and its host Tetrapedia diversipes

Cleptoparasitism, also known as brood parasitism, is a widespread strategy among bee species in which the parasite lays eggs into the nests of the host species. Even though this behavior has significant ecological implications for the dynamics of several species, little is known about the molecular pathways associated with cleptoparasitism. To shed some light on this issue, we used gene expression data to perform a comparative analysis between two solitary neotropical bees: Coelioxoides waltheriae, an obligate parasite, and their specific host Tetrapedia diversipes. We found that ortholog genes involved in signal transduction, sensory perception, learning, and memory formation were differentially expressed between the cleptoparasite and the host. We hypothesize that these genes and their associated molecular pathways are engaged in cleptoparasitism-related processes and, hence, are appealing subjects for further investigation into functional and evolutionary aspects of cleptoparasitism in bees.

The Mechanism of "Treating Different Diseases with the Same Treatment" by Qiangji Jianpi Decoction in Ankylosing Spondylitis Combined with Inflammatory Bowel Disease: A Comprehensive Analysis of Multiple Methods

Background

Ankylosing spondylitis (AS) and inflammatory bowel disease (IBD) are prevalent autoimmune disorders that often co-occur, posing significant treatment challenges. This investigation adopts a multidisciplinary strategy, integrating bioinformatics, network pharmacology, molecular docking, and Mendelian randomization, to elucidate the relationship between AS and IBD and to investigate the potential mechanisms of traditional Chinese medicine formulations, represented by Qiangji Jianpi (QJJP) decoction, in treating these comorbid conditions.

Methods

We utilized databases to pinpoint common targets among AS, IBD, and QJJP decoction's active compounds through intersection analysis. Through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses, we mapped a network in Cytoscape, isolating critical targets. Molecular docking with AutoDock validated the affinity between targets and compounds. ROC analysis and dataset validation assessed diagnostic performance, while Gene Set Enrichment Analysis (GSEA) offered pathway insights. Mendelian randomization explored the AS-IBD causal relationship.

Results

Screening identified 105 targets for QJJP decoction, 414 for AS, and 2420 for IBD, with 85 overlapping. These targets predominantly participate in organismal responses and DNA transcription factor binding, with a significant cellular presence in the endoplasmic reticulum and vesicle lumen. Molecular docking, facilitated by Cytoscape, confirmed IL1A, IFNG, TGFB1, and EDN1 as critical targets, with IFNG demonstrating diagnostic potential through GEO dataset validation. The integration of GSEA with network pharmacology highlighted the therapeutic significance of the relaxin, osteoclast differentiation, HIF-1, and AGE-RAGE signaling pathways in QJJP decoction's action. Mendelian randomization analysis indicated a positive causal relationship between IBD and AS, pinpointing rs2193041 as a key SNP influencing IFNG.

Conclusion

Based on the principle of "treating different diseases with the same method" in traditional Chinese medicine theory, we explored the intricate mechanisms through which QJJP decoction addresses AS and IBD comorbidity. Our research spotlighted the pivotal role of the IFNG gene. IFNG emerges not only as a key therapeutic target but also assumes significance as a potential diagnostic biomarker through its genetic underpinnings. This investigation establishes a solid base for subsequent experimental inquiries. Our findings introduce novel approaches for incorporating traditional Chinese medicine into the treatment of AS-IBD comorbidity, setting the stage for groundbreaking research directions.

Skeletal muscle differentiation induces wide-ranging nucleosome repositioning in muscle gene promoters

In a previous report, we demonstrated that Cbx1, PurB and Sp3 inhibited cardiac muscle differentiation by increasing nucleosome density around cardiac muscle gene promoters. Since cardiac and skeletal muscle express many of the same proteins, we asked if Cbx1, PurB and Sp3 similarly regulated skeletal muscle differentiation. In a C2C12 model of skeletal muscle differentiation, Cbx1 and PurB knockdown increased myotube formation. In contrast, Sp3 knockdown inhibited myotube formation, suggesting that Sp3 played opposing roles in cardiac muscle and skeletal muscle differentiation. Consistent with this finding, Sp3 knockdown also inhibited various muscle-specific genes. The Cbx1, PurB and Sp3 proteins are believed to influence gene-expression in part by altering nucleosome position. Importantly, we developed a statistical approach to determine if changes in nucleosome positioning were significant and applied it to understanding the architecture of muscle-specific genes. Through this novel statistical approach, we found that during myogenic differentiation, skeletal muscle-specific genes undergo a set of unique nucleosome changes which differ significantly from those shown in commonly expressed muscle genes. While Sp3 binding was associated with nucleosome loss, there appeared no correlation with the aforementioned nucleosome changes. In summary, we have identified a novel role for Sp3 in skeletal muscle differentiation and through the application of quantifiable MNase-seq have discovered unique fingerprints of nucleosome changes for various classes of muscle genes during myogenic differentiation.

Proximity Mapping of Desmosomes Reveals a Striking Shift in Their Molecular Neighborhood Associated With Maturation

Desmosomes are multiprotein adhesion complexes that link intermediate filaments to the plasma membrane, ensuring the mechanical integrity of cells across tissues, but how they participate in the wider signaling network to exert their full function is unclear. To investigate this, we carried out protein proximity mapping using biotinylation (BioID). The combined interactomes of the essential desmosomal proteins desmocollin 2a, plakoglobin, and plakophilin 2a (Pkp2a) in Madin-Darby canine kidney epithelial cells were mapped and their differences and commonalities characterized as desmosome matured from Ca2+ dependence to the mature, Ca2+-independent, hyper-adhesive state, which predominates in tissues. Results suggest that individual desmosomal proteins have distinct roles in connecting to cellular signaling pathways and that these roles alter substantially when cells change their adhesion state. The data provide further support for a dualistic concept of desmosomes in which the properties of Pkp2a differ from those of the other, more stable proteins. This body of data provides an invaluable resource for the analysis of desmosome function.

Protein homeostasis imprinting across evolution

Protein homeostasis (a.k.a. proteostasis) is associated with the primary functions of life, and therefore with evolution. However, it is unclear how cellular proteostasis machines have evolved to adjust protein biogenesis needs to environmental constraints. Herein, we describe a novel computational approach, based on semantic network analysis, to evaluate proteostasis plasticity during evolution. We show that the molecular components of the proteostasis network (PN) are reliable metrics to deconvolute the life forms into Archaea, Bacteria and Eukarya and to assess the evolution rates among species. Semantic graphs were used as new criteria to evaluate PN complexity in 93 Eukarya, 250 Bacteria and 62 Archaea, thus representing a novel strategy for taxonomic classification, which provided information about species divergence. Kingdom-specific PN components were identified, suggesting that PN complexity may correlate with evolution. We found that the gains that occurred throughout PN evolution revealed a dichotomy within both the PN conserved modules and within kingdom-specific modules. Additionally, many of these components contribute to the evolutionary imprinting of other conserved mechanisms. Finally, the current study suggests a new way to exploit the genomic annotation of biomedical ontologies, deriving new knowledge from the semantic comparison of different biological systems.

Construction of a novel lower-extremity peripheral artery disease subtype prediction model using unsupervised machine learning and neutrophil-related biomarkers

Lower-extremity peripheral artery disease (LE-PAD) is a prevalent circulatory disorder with risks of critical limb ischemia and amputation. This study aimed to develop a prediction model for a novel LE-PAD subtype to predict the severity of the disease and guide personalized interventions. Additionally, LE-PAD pathogenesis involves altered immune microenvironment, we examined the immune differences to elucidate LE-PAD pathogenesis. A total of 460 patients with LE-PAD were enrolled and clustered using unsupervised machine learning algorithms (UMLAs). Logistic regression analyses were performed to screen and identify predictive factors for the novel subtype of LE-PAD and a prediction model was built. We performed a comparative analysis regarding neutrophil levels in different subgroups of patients and an immune cell infiltration analysis to explore the associations between neutrophil levels and LE-PAD. Through hematoxylin and eosin (H&E) staining of lower-extremity arteries, neutrophil infiltration in patients with and without LE-PAD was compared. We found that UMLAs can helped in constructing a prediction model for patients with novel LE-PAD subtypes which enabled risk stratification for patients with LE-PAD using routinely available clinical data to assist clinical decision-making and improve personalized management for patients with LE-PAD. Additionally, the results indicated the critical role of neutrophil infiltration in LE-PAD pathogenesis.

Overlap in synaptic neurological condition susceptibility pathways and the neural pannexin 1 interactome revealed by bioinformatics analyses

Many neurological conditions exhibit synaptic impairments, suggesting mechanistic convergence. Additionally, the pannexin 1 (PANX1) channel and signaling scaffold is linked to several of these neurological conditions and is an emerging regulator of synaptic development and plasticity; however, its synaptic pathogenic contributions are relatively unexplored. To this end, we explored connections between synaptic neurodevelopmental disorder and neurodegenerative disease susceptibility genes discovered by genome-wide association studies (GWASs), and the neural PANX1 interactome (483 proteins) identified from mouse Neuro2a (N2a) cells. To identify shared susceptibility genes, we compared synaptic suggestive GWAS candidate genes amongst autism spectrum disorders, schizophrenia, Parkinson's disease, and Alzheimer's disease. To further probe PANX1 signaling pathways at the synapse, we used bioinformatics tools to identify PANX1 interactome signaling pathways and protein-protein interaction clusters. To shed light on synaptic disease mechanisms potentially linking PANX1 and these four neurological conditions, we performed additional cross-analyses between gene ontologies enriched for the PANX1 synaptic and disease-susceptibility gene sets. Finally, to explore the regional specificity of synaptic PANX1-neurological condition connections, we identified brain region-specific elevations of synaptic PANX1 interactome and GWAS candidate gene set transcripts. Our results confirm considerable overlap in risk genes for autism spectrum disorders and schizophrenia and identify potential commonalities in genetic susceptibility for neurodevelopmental disorders and neurodegenerative diseases. Our findings also pinpointed novel putative PANX1 links to synaptic disease-associated pathways, such as regulation of vesicular trafficking and proteostasis, warranting further validation.

Immune-dysregulated neutrophils characterized by upregulation of CXCL1 may be a potential factor in the pathogenesis of abdominal aortic aneurysm and systemic lupus erythematosus

Background

The abdominal aortic aneurysm (AAA) incidence is closely related to systemic lupus erythematosus (SLE). However, the common mechanisms between AAA and SLE are still unknown. The purpose of this research was to examine the main molecules and pathways involved in the immunization process that lead to the co-occurrence of AAA and SLE through the utilization of quantitative bioinformatics analysis of publicly available RNA sequencing databases. Moreover, routine blood test information was gathered from 460 patients to validate the findings.

Materials and methods

Datasets of both AAA (GSE57691 and GSE205071) and SLE (GSE50772 and GSE154851) were downloaded from the Gene Expression Omnibus (GEO) database, and differentially expressed genes (DEGs) were analyzed using bioinformatic tools. To determine the functions of the common differentially expressed genes (DEGs), Gene Ontology (GO) and Kyoto Encyclopedia analyses were conducted. Subsequently, the hub gene was identified through cytoHubba, and its validation was carried out in GSE47472 for AAA and GSE81622 for SLE. Immune cell infiltration analysis was performed to identify the key immune cells correlated with AAA and SLE, and to evaluate the correlation between key immune cells and the hub gene. Subsequently, the routine blood test data of 460 patients were collected, and the result of the immune cell infiltration analysis was further validated by univariate and multivariate logistic regression analysis.

Results

A total of 25 common DEGs were obtained, and three genes were screened by cytoHubba algorithms. Upon validation of the datasets, CXCL1 emerged as the hub gene with strong predictive capabilities, as evidenced by an area under the curve (AUC) > 0.7 for both AAA and SLE. The infiltration of immune cells was also validated, revealing a significant upregulation of neutrophils in the AAA and SLE datasets, along with a correlation between neutrophil infiltration and CXCL1 upregulation. Clinical data analysis revealed a significant increase in neutrophils in both AAA and SLE patients (p < 0.05). Neutrophils were found to be an independent factor in the diagnosis of AAA and SLE, exhibiting good diagnostic accuracy with AUC >0.7.

Conclusion

This study elucidates CXCL1 as a hub gene for the co-occurrence of AAA and SLE. Neutrophil infiltration plays a central role in the development of AAA and SLE and may serve to be a potential diagnostic and therapeutic target.

Establishing plausibility of cardiovascular adverse effects of immunotherapies using Mendelian randomisation

Immune checkpoint inhibitors (ICIs) and Janus kinase inhibitors (JAKis) have raised concerns over serious unexpected cardiovascular adverse events. The widespread pleiotropy in genome-wide association studies offers an opportunity to identify cardiovascular risks from in-development drugs to help inform appropriate trial design and pharmacovigilance strategies. This study uses the Mendelian randomization (MR) approach to study the causal effects of 9 cardiovascular risk factors on ischemic stroke risk both independently and by mediation, followed by an interrogation of the implicated expression quantitative trait loci (eQTLs) to determine if the enriched pathways can explain the adverse stroke events observed with ICI or JAKi treatment. Genetic predisposition to higher systolic blood pressure (SBP), diastolic blood pressure (DBP), body mass index (BMI), waist-to-hip ratio (WHR), low-density lipoprotein cholesterol (LDL), triglycerides (TG), type 2 diabetes (T2DM), and smoking index were associated with higher ischemic stroke risk. The associations of genetically predicted BMI, WHR, and TG on the outcome were attenuated after adjusting for genetically predicted T2DM [BMI: 53.15% mediated, 95% CI 17.21%-89.10%; WHR: 42.92% (4.17%-81.67%); TG: 72.05% (10.63%-133.46%)]. JAKis, programmed cell death protein 1 and programmed death ligand 1 inhibitors were implicated in the pathways enriched by the genes related to the instruments for each of SBP, DBP, WHR, T2DM, and LDL. Overall, MR mediation analyses support the role of T2DM in mediating the effects of BMI, WHR, and TG on ischemic stroke risk and follow-up pathway enrichment analysis highlights the utility of this approach in the early identification of potential harm from drugs.

MMP9 and STAT1 are biomarkers of the change in immune infiltration after anti-tuberculosis therapy, and the immune status can identify patients with spinal tuberculosis

BACKGROUND

Due to a lack of studies on immune-related pathogenesis and a clinical diagnostic model, the diagnosis of Spinal Tuberculosis (STB) remains uncertain. Our study aimed to investigate the possible pathogenesis of STB and to develop a clinical diagnostic model for STB based on immune cell infiltration.

METHODS

Label-free quantification protein analysis of five pairs of specimens was used to determine the protein expression of the intervertebral disc in STB and non-STB. GO enrichment analysis, and KEGG pathway analysis were used to investigate the pathogenesis of STB. The Hub proteins were then eliminated. Four datasets were downloaded from the GEO database to analyze immune cell infiltration, and the results were validated using blood routine test data from 8535TB and 7337 non-TB patients. Following that, clinical data from 164 STB and 162 non-STB patients were collected. The Random-Forest algorithm was used to screen out clinical predictors of STB and build a diagnostic model. The differential expression of MMP9 and STAT1 in STB and controls was confirmed using immunohistochemistry.

RESULTS

MMP9 and STAT1 were STB Hub proteins that were linked to disc destruction in STB. MMP9 and STAT1 were found to be associated with Monocytes, Neutrophils, and Lymphocytes in immune cell infiltration studies. Data from 15,872 blood routine tests revealed that the Monocytes ratio and Neutrophils ratio was significantly higher in TB patients than in non-TB patients (p < 0.001), while the Lymphocytes ratio was significantly lower in TB patients than in non-TB patients (p < 0.001). MMP9 and STAT1 expression were downregulated following the anti-TB therapy. For STB, a clinical diagnostic model was built using six clinical predictors: MR, NR, LR, ESR, BMI, and PLT. The model was evaluated using a ROC curve, which yielded an AUC of 0.816.

CONCLUSIONS

MMP9 and STAT1, immune-related hub proteins, were correlated with immune cell infiltration in STB patients. MR, NR, LR ESR, BMI, and PLT were clinical predictors of STB. Thus, the immune cell Infiltration-related clinical diagnostic model can predict STB effectively.

Qiangguyin inhibited fat accumulation in OVX mice through the p38 MAPK signaling pathway to achieve anti-osteoporosis effects

Postmenopausal osteoporosis (PMOP) is a common bone disease characterized by decreased bone density and increased bone fragility due to decreased estrogen levels. Qiangguyin (QGY) is transformed from the famous traditional Chinese medicine BuShen Invigorating Blood Decoction. In this study, we used QGY to treat PMOP. We observed that QGY significantly reduced fat accumulation in the chondro-osseous junction. However, its specific mechanism of action remains unclear. To determine the specific molecular mechanism of QGY, we explored the pharmacological mechanism by which QGY reduces fat accumulation in the chondro-osseous junction through network pharmacological analysis. The active components and targets related to PMOP and QGY were screened from different databases, forming a composition-target-disease network. Next, a comprehensive analysis platform including protein-protein interaction (PPI) network, Gene Ontology (GO) enrichment analysis, and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were established. The results revealed that QGY inhibits adipogenic differentiation by activating the mitogen-activated protein kinase (MAPK) signaling pathway, thus reducing the accumulation of fat in the chondro-osseous junction. For further verification. In vitro and in vivo experiments were carried out. Our data showed that QGY significantly reversed the high expression of fatty acid binding protein 4 (FABP4) and peroxisome proliferator-activated receptor γ (PPARγ). Further, QGY prevents fat accumulation by inhibiting the expression of p38. In summary, the results of this study suggested that QGY-induced phenotypic changes are related to the activation of the p38 MAPK signaling pathway.

Common microgeographical selection patterns revealed in four European conifers

Microgeographical adaptation occurs when the effects of directional selection persist despite gene flow. Traits and genetic loci under selection can then show adaptive divergence, against the backdrop of little differentiation at other traits or loci. How common such events are and how strong the selection is that underlies them remain open questions. Here, we discovered and analysed microgeographical patterns of genomic divergence in four European and Mediterranean conifers with widely differing life-history traits and ecological requirements (Abies alba MIll., Cedrus atlantica [Endl.] Manetti, Pinus halepensis Mill. and Pinus pinaster Aiton) by screening pairs from geographically close forest stands sampled along steep ecological gradients. We inferred patterns of genomic divergence by applying a combination of divergence outlier detection methods, demographic modelling, Approximate Bayesian Computation inferences and genomic annotation to genomic data. Surprisingly for such small geographical scales, we showed that selection is strong in all species but generally affects different loci in each. A clear signature of selection was systematically detected on a fraction of the genome, of the order of 0.1%-1% of the loci depending on the species. The novel modelling method we designed for estimating selection coefficients showed that the microgeographical selection coefficient scaled by population size (Ns) was 2-30. Our results convincingly suggest that selection maintains within-population diversity at microgeographical scales in spatially heterogeneous environments. Such genetic diversity is likely to be a major reservoir of adaptive potential, helping populations to adapt under fluctuating environmental conditions.

Subcellular location of source proteins improves prediction of neoantigens for immunotherapy

Antigen presentation via the major histocompatibility complex (MHC) is essential for anti-tumor immunity. However, the rules that determine which tumor-derived peptides will be immunogenic are still incompletely understood. Here, we investigated whether constraints on peptide accessibility to the MHC due to protein subcellular location are associated with peptide immunogenicity potential. Analyzing over 380,000 peptides from studies of MHC presentation and peptide immunogenicity, we find clear spatial biases in both eluted and immunogenic peptides. We find that including parent protein location improves the prediction of peptide immunogenicity in multiple datasets. In human immunotherapy cohorts, the location was associated with a neoantigen vaccination response, and immune checkpoint blockade responders generally had a higher burden of neopeptides from accessible locations. We conclude that protein subcellular location adds important information for optimizing cancer immunotherapies.

A Multigraph-Based Representation of Hi-C Data

Chromatin-chromatin interactions and three-dimensional (3D) spatial structures are involved in transcriptional regulation and have a decisive role in DNA replication and repair. To understand how individual genes and their regulatory elements function within the larger genomic context, and how the genome reacts to environmental stimuli, the linear sequence information needs to be interpreted in three-dimensional space, which is still a challenging task. Here, we propose a novel, heuristic approach to represent Hi-C datasets by a whole-genomic pseudo-structure in 3D space. The baseline of our approach is the construction of a multigraph from genomic-sequence data and Hi-C interaction data, then applying a modified force-directed layout algorithm. The resulting layout is a pseudo-structure. While pseudo-structures are not based on direct observation and their details are inherent to settings, surprisingly, they demonstrate interesting, overall similarities of known genome structures of both barley and rice, namely, the Rabl and Rosette-like conformation. It has an exciting potential to be extended by additional omics data (RNA-seq, Chip-seq, etc.), allowing to visualize the dynamics of the pseudo-structures across various tissues or developmental stages. Furthermore, this novel method would make it possible to revisit most Hi-C data accumulated in the public domain in the last decade.

Identification of a novel ceRNA network related to prognosis and immunity in HNSCC based on integrated bioinformatic investigation

Head and neck squamous cell carcinoma (HNSCC) is characterized by an immunosuppression environment and necessitates the development of new immunotherapy response predictors. The study aimed to build a prognosis-related competing endogenous RNA (ceRNA) network based on immune-related genes (IRGs) and analyze its immunological signatures. Differentially expressed IRGs were identified by bioinformatics analysis with Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA) and ImmPort databases. Finally, via upstream prognosis-related microRNAs (miRNAs) and long noncoding RNAs (lncRNAs) prediction and co-expression analysis, we built an immune-related ceRNA network (LINC00052/hsa-miR-148a-3p/PLAU) related to HNSCC patient prognosis. CIBERSORT analysis demonstrated that there were substantial differences in 11 infiltrating immune cells in HNSCC, and PLAU was closely correlated with 10 type cells, including T cells CD8+ (R =  - 0.329), T cells follicular helper (R = - 0.342) and macrophage M0 (R = 0.278). Methylation and Tumor Immune Dysfunction and Exclusion (TIDE) analyses revealed that PLAU upregulation was most likely caused by hypomethylation and that high PLAU expression may be associated with tumor immune evasion in HNSCC, respectively.

Integrative analyses of immune-related biomarkers and associated mechanisms in coronary heart disease

Various studies showed that the effect of immune activation is pro-atherogenic and coronary heart disease (CHD) should therefore be considered an autoimmune disease. This study aimed to identify potential immune-related biomarkers, pathways, and the potential regulatory networks underlying CHD. Differentially expressed genes (DEGs) between CHD and control samples were determined by analyzing GSE71226 and GSE9128. The overlapping differential expression immune-related genes (DE-IRGs) for CHD were identified by analyzing the ImmPort database and two GEO databases. A total of 384 DE-IRGs were identified. Subsequently, comprehensive enrichment analyses suggested that DE-IRGs were enriched in immune-related pathways, including autoimmune thyroid disease, the intestinal immune network for IGA production, and downstream signaling events of B cell receptors. The signature of DE-IRGs was validated using an external independent dataset GSE20681 (AUC = 0.875). Furthermore, we conducted protein–protein interaction network analysis and identified eight hub genes, which were most enriched in regulation of defense response, NF-κB signaling pathway, regulation of JNK cascade, and regulation of cytokine production. Moreover, networks of miRNAs-mRNAs and transcription factors (TFs)-mRNA underlying the integrated data were established, involving eight miRNAs and 76 TF-targeting hub genes. Ultimately, 17 SNPs in miRNA-mediated gene networks were identified. We screened potential immune-related genes in CHD and constructed miRNA-mRNA-TF and SNP-miRNA networks, which not only provide inspired insights into the occurrence and the molecular mechanisms of CHD but also lay a foundation for targeting potential biomarkers using immunotherapy and for understanding the molecular mechanisms of CHD.

Acute Effects of Intratumor DNA Electrotransfer

Intratumor therapeutic DNA electroporation or electrotransfer is in clinical trials in the United States and is under development in many other countries. Acute changes in endogenous gene expression in response to DNA or to pulse application may significantly modulate the therapeutic efficacy of the expressed proteins. Oligonucleotide arrays were used in this study to quantify changes in mRNA expression in B16-F10 mouse melanoma tumors four hours after DNA electrotransfer. The data were subjected to the DAVID v6.8 web server for functional annotation to reveal regulated genes and genetic pathways. Gene ontology analysis revealed several molecular functions related to cytoskeletal remodeling and inflammatory signaling. In B16-F10 cells, F-actin remodeling was confirmed by phalloidin staining in cells that received pulse application alone or in the presence of DNA. Chemokine secretion was confirmed in cells receiving DNA electrotransfer. These results indicate that pulse application alone or in the presence of DNA may modulate the therapeutic efficacy of therapeutic DNA electrotransfer.

Systematic assessment of pathway databases, based on a diverse collection of user-submitted experiments

A knowledge-based grouping of genes into pathways or functional units is essential for describing and understanding cellular complexity. However, it is not always clear a priori how and at what level of specificity functionally interconnected genes should be partitioned into pathways, for a given application. Here, we assess and compare nine existing and two conceptually novel functional classification systems, with respect to their discovery power and generality in gene set enrichment testing. We base our assessment on a collection of nearly 2000 functional genomics datasets provided by users of the STRING database. With these real-life and diverse queries, we assess which systems typically provide the most specific and complete enrichment results. We find many structural and performance differences between classification systems. Overall, the well-established, hierarchically organized pathway annotation systems yield the best enrichment performance, despite covering substantial parts of the human genome in general terms only. On the other hand, the more recent unsupervised annotation systems perform strongest in understudied areas and organisms, and in detecting more specific pathways, albeit with less informative labels.

Immune cell infiltration-related clinical diagnostic model for Ankylosing Spondylitis

Background: The pathogenesis and diagnosis of Ankylosing Spondylitis (AS) has remained uncertain due to several reasons, including the lack of studies on the local and systemic immune response in AS. To construct a clinical diagnostic model, this study identified the micro RNA-messenger RNA (miRNA-mRNA) interaction network and immune cell infiltration-related hub genes associated with AS.

Materials and Methods: Total RNA was extracted and purified from the interspinous ligament tissue samples of three patients with AS and three patients without AS; miRNA and mRNA microarrays were constructed using the extracted RNA. Bioinformatic tools were used to construct an miRNA-mRNA network, identify hub genes, and analyze immune infiltration associated with AS. Next, we collected the blood samples and clinical characteristics of 359 patients (197 with AS and 162 without AS). On the basis of the clinical characteristics and results of the routine blood tests, we selected immune-related cells whose numbers were significantly different in patients with AS and patients without AS. Univariate and multivariate logistic regression analysis was performed to construct a nomogram. Immunohistochemistry staining analysis was utilized to verify the differentially expression of LYN in AS and controls.

Results: A total of 225 differentially expressed miRNAs (DE miRNAs) and 406 differentially expressed mRNAs (DE mRNAs) were identified from the microarray. We selected 15 DE miRNAs and 38 DE mRNAs to construct a miRNA-mRNA network. The expression of LYN, an immune-related gene, correlated with the counts of monocytes, neutrophils, and dendritic cells. Based on the independent predictive factors of sex, age, and counts of monocytes, neutrophils, and white blood cells, a nomogram was established. Receiver operating characteristic (ROC) analysis was performed to evaluate the nomogram, with a C-index of 0.835 and AUC of 0.855.

Conclusion:LYN, an immune-related hub gene, correlated with immune cell infiltration in patients with AS. In addition, the counts of monocytes and neutrophils were the independent diagnostic factors for AS. If verified in future studies, a diagnostic model based on these findings may be used to predict AS effectively.

Tissue Specificity Based Isoform Function Prediction

Alternative splicing enables a gene spliced into different isoforms and hence protein variants. Identifying individual functions of these isoforms help deciphering the functional diversity of proteins. Although much efforts have been made for automatic gene function prediction, few efforts have been moved toward computational isoform function prediction, mainly due to the unavailable (or scanty) functional annotations of isoforms. Existing efforts directly combine multiple RNA-seq datasets without account of the important tissue specificity of alternative splicing. To bridge this gap, we introduce a novel approach called TS-Isofun to predict the functions of isoforms by integrating multiple functional association networks with respect to tissue specificity. TS-Isofun first constructs tissue-specific isoform functional association networks using multiple RNA-seq datasets from tissue-wise. Next, TS-Isofun assigns weights to these networks and models the tissue specificity by selectively integrating them with adaptive weights. It then introduces a joint matrix factorization-based data fusion model to leverage the integrated network, gene-level data and functional annotations of genes to infer the functions of isoforms. To achieve coherent weight assignment and isoform function prediction, TS-Isofun jointly optimizes the weights of individual networks and the isoform function prediction in a unified objective function. Experimental results show that TS-Isofun significantly outperforms state-of-the-art methods and the account of tissue specificity contributes to more accurate isoform function prediction.

ASURAT: functional annotation-driven unsupervised clustering of single-cell transcriptomes

MOTIVATION

Single-cell RNA sequencing (scRNA-seq) analysis reveals heterogeneity and dynamic cell transitions. However, conventional gene-based analyses require intensive manual curation to interpret biological implications of computational results. Hence, a theory for efficiently annotating individual cells remains warranted.

RESULTS

We present ASURAT, a computational tool for simultaneously performing unsupervised clustering and functional annotation of disease, cell type, biological process and signaling pathway activity for single-cell transcriptomic data, using a correlation graph decomposition for genes in database-derived functional terms. We validated the usability and clustering performance of ASURAT using scRNA-seq datasets for human peripheral blood mononuclear cells, which required fewer manual curations than existing methods. Moreover, we applied ASURAT to scRNA-seq and spatial transcriptome datasets for human small cell lung cancer and pancreatic ductal adenocarcinoma, respectively, identifying previously overlooked subpopulations and differentially expressed genes. ASURAT is a powerful tool for dissecting cell subpopulations and improving biological interpretability of complex and noisy transcriptomic data.

AVAILABILITY AND IMPLEMENTATION

ASURAT is published on Bioconductor (https://doi.org/10.18129/B9.bioc.ASURAT). The codes for analyzing data in this article are available at Github (https://github.com/keita-iida/ASURATBI) and figshare (https://doi.org/10.6084/m9.figshare.19200254.v4).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Identification of Critical Biomarkers and Immune Infiltration in Rheumatoid Arthritis Based on WGCNA and LASSO Algorithm

Rheumatoid arthritis(RA) is the most common inflammatory arthritis, and a significant cause of morbidity and mortality. RA patients' synovial inflammation contains a variety of genes and signalling pathways that are poorly understood. It was the goal of this research to discover the major biomarkers related to the course of RA and how they connect to immune cell infiltration. The Gene Expression Omnibus was used to download gene microarray data. Differential expression analysis, weighted gene co-expression network analysis (WGCNA), and least absolute shrinkage and selection operator (LASSO) regression were used to identify hub markers for RA. Single-sample GSEA was used to examine the infiltration levels of 28 immune cells and their connection to hub gene markers. The hub genes' expression in RA-HFLS and HFLS cells was verified by RT-PCR. The CCK-8 assay was applied to determine the roles of hub genes in RA. In this study, we identified 21 differentially expressed genes (DEGs) in RA. WGCNA yielded two co-expression modules, one of which exhibited the strongest connection with RA. Using a combination of differential genes, a total of 6 intersecting genes was discovered. Six hub genes were identified as possible biomarkers for RA after a lasso analysis was performed on the data. Three hub genes, CKS2, CSTA, and LY96, were found to have high diagnostic value using ROC curve analysis. They were shown to be closely related to the concentrations of several immune cells. RT-PCR confirmed that the expressions of CKS2, CSTA and LY96 were distinctly upregulated in RA-HFLS cells compared with HFLS cells. More importantly, knockdown of CKS2 suppressed the proliferation of RA-HFLS cells. Overall, to help diagnose and treat RA, it's expected that CKS2, CSTA, and LY96 will be available, and the aforementioned infiltration of immune cells may have a significant impact on the onset and progression of the disease.

Predicting causal genes from psychiatric genome-wide association studies using high-level etiological knowledge

Genome-wide association studies have discovered hundreds of genomic loci associated with psychiatric traits, but the causal genes underlying these associations are often unclear, a research gap that has hindered clinical translation. Here, we present a Psychiatric Omnilocus Prioritization Score (PsyOPS) derived from just three binary features encapsulating high-level assumptions about psychiatric disease etiology - namely, that causal psychiatric disease genes are likely to be mutationally constrained, be specifically expressed in the brain, and overlap with known neurodevelopmental disease genes. To our knowledge, PsyOPS is the first method specifically tailored to prioritizing causal genes at psychiatric GWAS loci. We show that, despite its extreme simplicity, PsyOPS achieves state-of-the-art performance at this task, comparable to a prior domain-agnostic approach relying on tens of thousands of features. Genes prioritized by PsyOPS are substantially more likely than other genes at the same loci to have convergent evidence of direct regulation by the GWAS variant according to both DNA looping assays and expression or splicing quantitative trait locus (QTL) maps. We provide examples of genes hundreds of kilobases away from the lead variant, like GABBR1 for schizophrenia, that are prioritized by all three of PsyOPS, DNA looping and QTLs. Our results underscore the power of incorporating high-level knowledge of trait etiology into causal gene prediction at GWAS loci, and comprise a resource for researchers interested in experimentally characterizing psychiatric gene candidates.

Multi-omics data integration for subtype identification of Chinese lower-grade gliomas: a joint similarity network fusion approach

Lower-grade gliomas (LGG), characterized by heterogeneity and invasiveness, originate from the central nervous system. Although studies focusing on molecular subtyping and molecular characteristics have provided novel insights into improving the diagnosis and therapy of LGG, there is an urgent need to identify new molecular subtypes and biomarkers that are promising to improve patient survival outcomes. Here, we proposed a joint similarity network fusion (Joint-SNF) method to integrate different omics data types to construct a fused network using the Joint and Individual Variation Explained (JIVE) technique under the SNF framework. Focusing on the joint network structure, a spectral clustering method was employed to obtain subtypes of patients. Simulation studies show that the proposed Joint-SNF method outperforms the original SNF approach under various simulation scenarios. We further applied the method to a Chinese LGG data set including mRNA expression, DNA methylation and microRNA (miRNA). Three molecular subtypes were identified and showed statistically significant differences in patient survival outcomes. The five-year mortality rates of the three subtypes are 80.8%, 32.1%, and 34.4%, respectively. After adjusting for clinically relevant covariates, the death risk of patients in Cluster 1 was 5.06 times higher than patients in other clusters. The fused network attained by the proposed Joint-SNF method enhances strong similarities, thus greatly improves subtyping performance compared to the original SNF method. The findings in the real application may provide important clues for improving patient survival outcomes and for precision treatment for Chinese LGG patients. An R package to implement the method can be accessed in Github at https://github.com/Sameerer/Joint-SNF.

Proteomic changes associated with predator-induced morphological defenses in oysters

Inducible prey defenses occur when organisms undergo plastic changes in phenotype to reduce predation risk. When predation pressure varies persistently over space or time, such as when predator and prey co-occur over only part of their biogeographic ranges, prey populations can become locally adapted in their inducible defenses. In California estuaries, native Olympia oyster (Ostrea lurida) populations have evolved disparate phenotypic responses to an invasive predator, the Atlantic oyster drill (Urosalpinx cinerea). In this study, oysters from an estuary with drills, and oysters from an estuary without drills, were reared for two generations in a laboratory common garden, and subsequently exposed to cues from Atlantic drills. Comparative proteomics was then used to investigate molecular mechanisms underlying conserved and divergent aspects of their inducible defenses. Both populations developed smaller, thicker, and harder shells after drill exposure, and these changes in shell phenotype were associated with up-regulation of calcium transport proteins that could influence biomineralization. Inducible defenses evolve in part because defended phenotypes incur fitness costs when predation risk is low. Immune proteins were down-regulated by both oyster populations after exposure to drills, implying a trade-off between biomineralization and immune function. Following drill exposure, oysters from the population that co-occurs with drills grew smaller shells than oysters inhabiting the estuary not yet invaded by the predator. Variation in the response to drills between populations was associated with isoform-specific protein expression. This trend suggests that a stronger inducible defense response evolved in oysters that co-occur with drills through modification of an existing mechanism.

A global Anopheles gambiae gene co-expression network constructed from hundreds of experimental conditions with missing values

BACKGROUND

Gene co-expression networks (GCNs) can be used to determine gene regulation and attribute gene function to biological processes. Different high throughput technologies, including one and two-channel microarrays and RNA-sequencing, allow evaluating thousands of gene expression data simultaneously, but these methodologies provide results that cannot be directly compared. Thus, it is complex to analyze co-expression relations between genes, especially when there are missing values arising for experimental reasons. Networks are a helpful tool for studying gene co-expression, where nodes represent genes and edges represent co-expression of pairs of genes.

RESULTS

In this paper, we establish a method for constructing a gene co-expression network for the Anopheles gambiae transcriptome from 257 unique studies obtained with different methodologies and experimental designs. We introduce the sliding threshold approach to select node pairs with high Pearson correlation coefficients. The resulting network, which we name AgGCN1.0, is robust to random removal of conditions and has similar characteristics to small-world and scale-free networks. Analysis of network sub-graphs revealed that the core is largely comprised of genes that encode components of the mitochondrial respiratory chain and the ribosome, while different communities are enriched for genes involved in distinct biological processes.

CONCLUSION

Analysis of the network reveals that both the architecture of the core sub-network and the network communities are based on gene function, supporting the power of the proposed method for GCN construction. Application of network science methodology reveals that the overall network structure is driven to maximize the integration of essential cellular functions, possibly allowing the flexibility to add novel functions.

The Mechanism of Dendrobium officinale as a Treatment for Hyperlipidemia Based on Network Pharmacology and Experimental Validation

Aim and Objective. Hyperlipidemia is a public health matter of global scale, contributing to a wide range of diseases that can result in severe complications and significant annual mortality. Dendrobium officinale (DO) is an edible plant with a long medicinal history in China. Our previous studies revealed that DO may have therapeutic benefits in lipid disorders. However, the mechanism of its active compounds is still unclear. This research aimed at uncovering the hidden anti-hyperlipidemia mechanisms of DO through network pharmacology and experimental validation. Materials and Methods. The active compounds in DO, their targets, and targets associated with hyperlipidemia were screened across various databases, and the hidden targets of DO in treating hyperlipidemia were forecast. The compound-target (C-T), protein-protein interaction (PPI), and compound-target-pathway (C-T-P) networks of DO were set up with Cytoscape software. The hub genes and core clusters of DO predicted to be active against hyperlipidemia were calculated by Cytoscape. The DAVID database was adopted for Gene Ontology (GO) analysis and KEGG pathway enrichment analysis. Next, we used the high-sucrose-fat diet and alcohol (HFDA)-induced hyperlipidemia rats to evaluate the hypolipidemic effect of DO. Results. In this study, we obtained 264 compounds from DO, revealed 11 bioactive compounds, and predicted 89 potential targets of DO. The network analysis uncovered that naringenin, isorhamnetin, and taxifolin might be the compounds in DO that are mainly in charge of its roles in hyperlipidemia and might play a role by modulating the targets (including PPARG, ADIPOQ, AKT1, TNF, and APOB). The pathway analysis showed that DO might affect diverse signaling pathways related to the pathogenesis of hyperlipidemia, including PPAR signaling pathway, insulin resistance, AMPK signaling pathway, and non-alcoholic fatty liver disease simultaneously. Meanwhile, in the HFDA-induced hyperlipidemia rat model, DO could significantly decrease the level of TC, TG, LDL-c, and ALT in serum, and increase HDL-c as well. The liver pathological section indicated that DO could ease liver damage and lipid cumulation. Conclusion. In summary, the biological targets of the main bioactive compounds in DO were found to distribute across multiple metabolic pathways. These findings suggest that a mutual regulatory system consisting of multiple components, targets, and pathways is a likely mechanism through which DO may improve hyperlipidemia. Validation experiments indicated that DO may treat hyperlipidemia by affecting NAFLD-related signaling pathways.

Novel Genetic Signatures Associated With Sporadic Amyotrophic Lateral Sclerosis

Amyotrophic Lateral Sclerosis (ALS) is a complex polygenetic neurodegenerative disorder. Establishing a diagnosis for ALS is a challenging and lengthy process. By the time a diagnosis is made, the lifespan prognosis is only about two to 5 years. Genetic testing can be critical in assessing a patient’s risk for ALS, provided they have one of the known familial genes. However, the vast majority of ALS cases are sporadic and have no known associated genetic signatures. Our analysis of the whole genome sequencing data from ALS patients and healthy controls from the Answer ALS Consortium has uncovered twenty-three novel mutations in twenty-two protein-coding genes associated with sporadic ALS cases. The results show the majority of patients with the sporadic form of ALS have at least one or more mutation(s) in the 22 genes we have identified with probabilities of developing ALS ranging from 25–99%, depending on the number of mutations a patient has among the identified genes. Moreover, we have identified a subset of the ALS cohort that has >17 mutations in the 22 identified. In this case, a patient with this mutation profile has a 99% chance of developing ALS and could be classified as being at high risk for the disease. These genetic biomarkers can be used as an early ALS disease diagnostic tool with a rapid and non-invasive technique.

Gaining confidence in inferred networks

Network inference is a notoriously challenging problem. Inferred networks are associated with high uncertainty and likely riddled with false positive and false negative interactions. Especially for biological networks we do not have good ways of judging the performance of inference methods against real networks, and instead we often rely solely on the performance against simulated data. Gaining confidence in networks inferred from real data nevertheless thus requires establishing reliable validation methods. Here, we argue that the expectation of mixing patterns in biological networks such as gene regulatory networks offers a reasonable starting point: interactions are more likely to occur between nodes with similar biological functions. We can quantify this behaviour using the assortativity coefficient, and here we show that the resulting heuristic, functional assortativity, offers a reliable and informative route for comparing different inference algorithms.