Interpretation of biological experiments changes with evolution of the Gene Ontology and its annotations

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.

Unveiling Wolbachia transcriptomic signature in the arboviral vector Aedes aegypti

Introduction

The mosquito Aedes aegypti is the main vector of arboviral diseases such as dengue and imposes a global health burden. A promising control strategy is to infect A. aegypti populations with Wolbachia, a genus of intracellular bacteria capable of blocking arboviral infections. Enhancing and preserving the efficacy of this method will depend on a solid mechanistic knowledge of the A. aegypti-Wolbachia symbiosis. By identifying differences between Wolbachia-infected and uninfected A. aegypti, previous transcriptomic studies proposed a wide range of symbiotic interactions, but a systematic identification of consistent effects across datasets is still missing.

Methods

To identify A. aegypti genes and functions consistently affected by Wolbachia, we performed differential expression and functional enrichment analysis on published transcriptomic datasets, followed by a meta-analysis of the obtained p-values using the maxP method. Six datasets were retrieved from Gene Expression Omnibus, Sequence Read Archive and ArrayExpress (last searched in July 2024, considering lack of replication as the exclusion criteria). After discarding one dataset from wAlbB-infected cell line due to poor mapping to the A. aegypti genome, the data comprised adult female A. aegypti heads, muscles, carcasses, midguts and bodies, and Wolbachia strains wMel and wMelPop.

Results and Discussion

Meta-analysis revealed 10 and 21 consistently down- and upregulated host genes, some of which have escaped the focus of previous research, including the consistently downregulated exonuclease AAEL009650 which has a pro-dengue virus homolog in Drosophila. At the function level, we found consistent upregulation of electron transport chain (ETC), carbohydrate transport and serine-type peptidase activity and inhibition, and downregulation of DNA replication. ETC upregulation suggests an alternative mechanism for Wolbachia's induction of antiviral oxidative stress, previously attributed to dual- and NADPH-oxidases which here showed downregulation or no regulation. Through analysis of previously published datasets, this work identifies promising molecular and functional targets for future studies aimed at elucidating the most fundamental mechanisms of the A. aegypti-Wolbachia symbiosis.

A thematic analysis of prognostic, diagnostic, and therapeutic of circulating miRNA biomarkers in bortezomib-resistant multiple myeloma

Objective

The increasing demand for precision medicine has spurred molecular diagnostic investigations to emphasize the utility of miRNA as significant biomarkers. Recent studies have underscored miRNA's role as prognostic, diagnostic, and therapeutic biomarkers in managing and monitoring multiple myeloma patients. This review aims to present the latest insights on the potential of circulating miRNA as prognostic, diagnostic, and therapeutic biomarkers in bortezomib-resistant multiple myeloma.

Methods

For this purpose, a comprehensive thematic literature review from January 2014 and August 2024 was conducted utilizing the databases CINAHL, Pubmed, and Google Scholar. Twenty pertinent studies were meticulously analyzed and categorized into the following sections: Bortezomib (BTZ) resistance in multiple myeloma, the predictive role of miRNAs in BTZ resistance, the impact of circulating miRNAs in multiple myeloma, and the potential of circulating miRNA as prognostic, diagnostic, and therapeutic biomarkers.

Results

Of note, eight studies identified circulating miRNAs as diagnostic miRNA biomarkers (i.e., miR-744, miR-130a, let-7d, let-7e, miR-34a, etc.). In comparison, nine studies identified several circulating miRNAs that can be used as prognostic biomarkers (i.e., miR-20a, miR-483-5p, mir-1246, let-7a, let-7e, etc.). Moreover, five studies identified circulating miRNAs as promising therapeutic biomarkers (i.e., mir-15a, mir-92a, mir-19a, etc.). This discovery can significantly enhance early detection, accurate diagnosis, prognosis, overall survival rates, and quality of life for patients with multiple myeloma.

Conclusion

Based on this evidence, exploring circulating miRNAs as a potential noninvasive biomarker for multiple myeloma represents a noteworthy advancement. This is attributed to the abundance of miRNAs in plasma or serum, which exhibits remarkable stability against enzymatic degradation.

Unravelling the developmental toxicity of heavy metals using zebrafish as a model: a narrative review

Developmental toxicity is the disruption of an organism's normal development which may occur in either the parent before conception or in the growing creature itself. Zebrafish (Danio rerio) are being employed as effective vertebrate models to evaluate the safety and toxicity of chemicals because they can breed multiple times in a year so we can observe the toxic effects in the next generation and their development mental stages can be observed and define clearly because their 1 cell stage to prime stage is transparent so we can observe the development of every organ also they have nearly about 80% genetic similarity with humans and shares the similar neuromodulatory structure along with multiple neurotransmitter. The recent research endeavours to examine the harmful outcome of various heavy metals such as cadmium, chromium, nickel, arsenic, lead, mercury, bismuth, iron, manganese, and thallium along with microplastics on zebrafish embryos when subjected to environmentally acceptable levels of every single metal in addition to co-exposure at various points in time. These heavy metals can alter the mRNA expression levels, increase the reactive oxygen species (ROS) generation, decrease antioxidant expression, damage neuronal function, alter neurotransmitter release, alter the expression of several apoptotic proteins, interfere with the different signalling pathways, decrease heat rates, increase malformations like - pericardial oedema, heart oedema, reduce in length tail bending abnormal formation in fins. Thereafter we concluded that due to its involvement in the food chain, it also causes severe effects on human beings.

Limited Diversity of Thermal Adaptation to a Critical Temperature in Zymomonas mobilis: Evidence from Multiple-Parallel Laboratory Evolution Experiments

Laboratory evolution is an effective means of understanding microbial adaptation to the environment. We previously isolated four thermoadapted Zymomonas mobilis mutants, which showed a 2 °C rise in the critical high temperature (CHT), by performing multiple-parallel adaptation experiments. In the present study, the individual mutations in these mutants were intensively analyzed. Two mutations in each adapted mutant were found to primarily contribute to the increase in the upper temperature limit. RNA sequencing (RNA-seq) analysis revealed that the two mutations led to the upregulation of 79-185 genes and the downregulation of 242-311 genes. The findings from transcriptomic and physiological experiments suggest two common and primary mechanisms for thermal resistance: a decrease in the activity of diacylglycerol kinase, which may change the structure of lipopolysaccharide (LPS) probably to strengthen the membrane structure, and an increase in the expression of genes for GroEL/GroES or cell wall hydrolase to repair the protein or membrane damage that occurs at such critical temperatures. Additionally, transporters including efflux pumps may contribute to intracellular homeostasis by expelling toxic compounds such as ethanol and acetate or by maintaining the K+ concentration. The results of this study on four independently thermoadapted mutants led to the conclusion that the mutants have almost the same thermal adaptation strategies and thus their molecular diversity is limited.

Molecular investigation of how drought stress affects chlorophyll metabolism and photosynthesis in leaves of C3 and C4 plant species: A transcriptome meta-analysis

Drought stress has a significant impact on photosynthesis in plants, leading to reduced photosynthesis rates and affecting plant growth and yield. Understanding the effects of drought stress on photosynthetic pathways, particularly in C3 and C4 plants, is crucial for maximizing agricultural productivity and maintaining food security. In this study, we analyzed RNA-seq data from leaves of common wheat (Triticum aestivum) and sorghum (Sorghum bicolor), as representatives of C3 and C4, using a meta-analysis approach to investigate the photosynthesis-related genes involved in the response to drought stress. We identified specific genes and components of the photosynthesis pathway that are affected by drought stress. The findings suggest that wheat and sorghum respond differently to drought stress, with sorghum showing a more effective defense system against photoinhibition and damage to photosystems. On the other hand, it seems that in wheat, in order to deal with oxidative stress, the expression of homologous genes of C4 enzyme and genes involved in heme and siroheme synthesis pathway has increased under stress. This is probably due to the higher photoinhibition in C3 photosynthetic system compared to C4. Furthermore, drought stress affected chlorophyll biosynthesis and degradation pathways in both wheat and sorghum, but compared with sorghum, drought stress had a greater inhibitory effect on chlorophyll biosynthesis in wheat, which indicates the difference in their ability to cope with photoinhibition.

Transcriptional variation and RNA polymorphism among different Lentinula edodes (Berk.) Pegler strains

BACKGROUND

Lentinula edodes is a commercially important mushroom known for its nutritional and therapeutic values. However, the molecular mechanisms underlying the distinct nutritional and physiological attributes of various L. edodes strains are not well understood. This study focused on three Lentinula strains (DMRO-356, DMRO-623, and DMRO-388s) with different nutritional and productivity profiles. Illumina sequencing was used to perform a whole-transcriptome analysis, conducting 100-base pair paired-end sequencing of total messenger RNA (mRNA) in duplicate, resulting in 28-48 million sequencing reads per strain. After rigorous data filtering, over 99% of high-quality reads were retained, and more than 95% were aligned to the Lentinula genome.

RESULTS

Differential gene expression analyses identified 2210 differentially expressed genes between DMRO-356 and DMRO-623, 862 between DMRO-356 and DMRO-388s, and 2212 between DMRO-623 and DMRO-388s. Significant genetic variations were found among the strains, including 7753 single nucleotide polymorphisms (SNPs) in DMRO-356 versus DMRO-623 and 4080 SNPs in DMRO-356 versus DMRO-388s. Additionally, 349 insertions/deletions (InDels) were found in DMRO-356/DMRO-623 and 218 in DMRO-356/DMRO-388 s. Non-synonymous SNPs, which alter amino acid compositions, were analyzed, showing a preference for polar over charged amino acids.

CONCLUSION

These differentially expressed genes were associated with various nutritional and developmental processes, highlighting the importance of genetic variations in shaping amino acid composition and potentially affecting protein function. This study is the first comprehensive exploration of transcriptional differences among Lentinula strains available for its cultivation, providing valuable insights to enhance mushroom quality and productivity. © 2024 Society of Chemical Industry.

FUNCTIONAL IMMUNOPHENOTYPING FOR PRECISION THERAPIES IN SEPSIS

ABSTRACT

Sepsis remains a significant cause of morbidity and mortality worldwide. Although many more patients are surviving the acute event, a substantial number enters a state of persistent inflammation and immunosuppression, rendering them more vulnerable to infections. Modulating the host immune response has been a focus of sepsis research for the past 50 years, yet novel therapies have been few and far between. Although many septic patients have similar clinical phenotypes, pathways affected by the septic event differ not only between individuals but also within an individual over the course of illness. These differences ultimately impact overall immune function and response to treatment. Defining the immune state, or endotype, of an individual is critical to understanding which patients will respond to a particular therapy. In this review, we highlight current approaches to define the immune endotype and propose that these technologies may be used to "prescreen" individuals to determine which therapies are most likely to be beneficial.

ENQUIRE automatically reconstructs, expands, and drives enrichment analysis of gene and Mesh co-occurrence networks from context-specific biomedical literature

The accelerating growth of scientific literature overwhelms our capacity to manually distil complex phenomena like molecular networks linked to diseases. Moreover, biases in biomedical research and database annotation limit our interpretation of facts and generation of hypotheses. ENQUIRE (Expanding Networks by Querying Unexpectedly Inter-Related Entities) offers a time- and resource-efficient alternative to manual literature curation and database mining. ENQUIRE reconstructs and expands co-occurrence networks of genes and biomedical ontologies from user-selected input corpora and network-inferred PubMed queries. Its modest resource usage and the integration of text mining, automatic querying, and network-based statistics mitigating literature biases makes ENQUIRE unique in its broad-scope applications. For example, ENQUIRE can generate co-occurrence gene networks that reflect high-confidence, functional networks. When tested on case studies spanning cancer, cell differentiation, and immunity, ENQUIRE identified interlinked genes and enriched pathways unique to each topic, thereby preserving their underlying context specificity. ENQUIRE supports biomedical researchers by easing literature annotation, boosting hypothesis formulation, and facilitating the identification of molecular targets for subsequent experimentation.

Micropillar-induced changes in cell nucleus morphology enhance bone regeneration by modulating the secretome

Nuclear morphology, which modulates chromatin architecture, plays a critical role in regulating gene expression and cell functions. While most research has focused on the direct effects of nuclear morphology on cell fate, its impact on the cell secretome and surrounding cells remains largely unexplored, yet is especially crucial for cell-based therapies. In this study, we fabricated implants with a micropillar topography using methacrylated poly(octamethylene citrate)/hydroxyapatite (mPOC/HA) composites to investigate how micropillar-induced nuclear deformation influences cell paracrine signaling for osteogenesis and cranial bone regeneration. In vitro, cells with deformed nuclei showed enhanced secretion of proteins that support extracellular matrix (ECM) organization, which promoted osteogenic differentiation in neighboring human mesenchymal stromal cells (hMSCs). In a mouse model with critical-size cranial defects, nuclear-deformed hMSCs on micropillar mPOC/HA implants elevated Col1a2 expression, contributing to bone matrix formation, and drove cell differentiation toward osteogenic progenitor cells. These findings indicate that micropillars not only enhance the osteogenic differentiation of human mesenchymal stromal cells (hMSCs) but also modulate the secretome, thereby influencing the fate of surrounding cells through paracrine effects.

Phosphotungstic acid-enhanced micro-computed tomography and RNA sequencing provide a new perspective on temporomandibular joint arthritis induced by complete Freund's adjuvant and collagen-induced arthritis in rat models

Background/Purpose

Temporomandibular joint (TMJ) arthritis causes inflammation and degradation of the mandibular condylar cartilage and subchondral bone. Complete Freund's adjuvant (CFA) and collagen-induced arthritis (CIA) are models for studying TMJ arthritis. While micro-computed tomography (micro-CT) is crucial for three-dimensional (3D) bone analysis, it has limitations in imaging nonmineralized tissues. Phosphotungstic acid (PTA) enhances soft tissue contrast. However, research on the 3D imaging of mandibular condylar cartilage and the molecular mechanisms of CFA- and CIA-induced arthritis remains unclear. This study aimed to investigate the bone and PTA-stained cartilage in the mandibular condyle using 3D reconstruction and explore the characteristics of enriched gene ontology terms underlying CFA- and CIA-induced TMJ arthritis in rat models.

Materials and methods

Rat mandibular condyles were collected from control, CFA, and CIA groups. Live micro-CT created 3D bone structures, and PTA-enhanced micro-CT constructed 3D mandibular condylar cartilage. Gene ontology enrichment analysis identified enriched gene ontology terms from differentially expressed genes through RNA sequencing.

Results

Major deformities in cartilage volume and bone morphology were observed in the arthritis-induced groups. The CIA group exhibited significant correlations between cartilage volume and bone parameters changes. Gene ontology enrichment analysis indicated fewer terms with upregulated differentially expressed genes related to inflammation and immune response in the CIA group than in the CFA group.

Conclusion

This study reveals distinct responses between CFA- and CIA-induced TMJ arthritis models. The CIA group exhibited strong correlations between cartilage volume and bone parameter changes and had less pronounced inflammation and immune response than the CFA group.

Leveraging DNA methylation to create Epigenetic Biomarker Proxies that inform clinical care: A new framework for Precision Medicine

The lack of accurate, cost-effective, and clinically relevant biomarkers remains a major barrier to incorporating omic data into clinical practice. Previous studies have shown that DNA methylation algorithms have utility as surrogate measures for selected proteins and metabolites. We expand upon this work by creating DNAm surrogates, termed epigenetic biomarker proxies (EBPs), across clinical laboratories, the metabolome, and the proteome. After screening >2,500 biomarkers, we trained and tested 1,694 EBP models and assessed their incident relationship with 12 chronic diseases and mortality, followed up to 15 years. We observe broad clinical relevance: 1) there are 1,292 and 4,863 FDR significant incident and prevalent associations, respectively; 2) most of these associations are replicated when looking at the lab-based counterpart, and > 62% of the shared associations have higher odds and hazard ratios to disease outcomes than their respective observed measurements; 3) EBPs of current clinical biochemistries detect deviations from normal with high sensitivity and specificity. Longitudinal EBPs also demonstrate significant changes corresponding to the changes observed in lab-based counterparts. Using two cohorts and > 30,000 individuals, we found that EBPs validate across healthy and sick populations. While further study is needed, these findings highlight the potential of implementing EBPs in a simple, low-cost, high-yield framework that benefits clinical medicine.

Differential gene expression analysis pipelines and bioinformatic tools for the identification of specific biomarkers: A review

In recent years, the role of bioinformatics and computational biology together with omics techniques and transcriptomics has gained tremendous importance in biomedicine and healthcare, particularly for the identification of biomarkers for precision medicine and drug discovery. Differential gene expression (DGE) analysis is one of the most used techniques for RNA-sequencing (RNA-seq) data analysis. This tool, which is typically used in various RNA-seq data processing applications, allows the identification of differentially expressed genes across two or more sample sets. Functional enrichment analyses can then be performed to annotate and contextualize the resulting gene lists. These studies provide valuable information about disease-causing biological processes and can help in identifying molecular targets for novel therapies. This review focuses on differential gene expression (DGE) analysis pipelines and bioinformatic techniques commonly used to identify specific biomarkers and discuss the advantages and disadvantages of these techniques.

Enhanced antiviral defense against begomoviral infection in Nicotiana benthamiana through strategic utilization of fluorescent carbon quantum dots to activate plant immunity

BACKGROUND

Owing to their unique physiochemical properties, low toxicity, antipathogenic effects and tunability, fluorescent carbon quantum dots (CQDs) represent a new generation of carbon-based nanomaterials. Despite the mounting research on the efficacy of CQDs against resilient plant pathogens, their potential ability to mitigate viral pathogens and the underlying molecular mechanism(s) remain understudied. In this study, we optimized the CQDs to maximize their antiviral effects against a highly pathogenic Begomovirus (cotton leaf curl Multan virus, CLCuMuV) and elucidated the mechanistic pathways associated with CQDs-mediated viral inhibition. To fine-tune the CQDs-induced antiviral effects against CLCuMuV and investigate the underlying molecular mechanisms,we used HR-TEM, XRD, FT-IR, XPS, and UV‒Vis spectrophotometry to characterize the CQDs. SPAD and FluorCam were used for physiological and photosynthetic performance analysis. Transcriptome, RT‒qPCR, integrated bioinformatics and molecular biology were employed to investigate gene expression, viral quantification and data validation.

RESULTS

The application of fluorescent, hexagonal crystalline, UV-absorptive and water-soluble CQDs (0.01 mg/ml) significantly reduced the CLCuMuV titer and mitigated viral symptoms in N. benthamiana at the early (5 dpi) and late (20 dpi) stages of infection. CQDs significantly increased the morphophysiological properties, relative chlorophyll contents and photosynthetic (Fv/Fm, QY_max, NPQ and Rfd) performance of the CLCuMuV-infected plants. While CLCuMuV infection disrupted plant immunity, the CQDs improved the antiviral defense response by regulating important immunity-related genes involved in endocytosis/necroptosis, Tam3-transposase, the ABC transporter/sphingolipid signaling pathway and serine/threonine protein kinase activities. CQDs potentially triggered TSS and TTS alternative splicing events in CLCuMuV-infected plants.

CONCLUSIONS

Overall, these findings underscore the antiviral potential of CQDs, their impact on plant resilience, and their ability to modulate gene expression in response to viral stress. This study's molecular insights provide a foundation for further research on nanomaterial applications in plant virology and crop protection, emphasizing the promising role of CQDs in enhancing plant health and combating viral infections.

Cancer subtype identification by multi-omics clustering based on interpretable feature and latent subspace learning

In recent years, multi-omics clustering has become a powerful tool in cancer research, offering a comprehensive perspective on the diverse molecular characteristics inherent to various cancer subtypes. However, most existing multi-omics clustering methods directly integrate heterogeneous features from different omics, which may struggle to deal with the noise or redundancy of multi-omics data and lead to poor clustering results. Therefore, we propose a novel multi-omics clustering method to extract interpretable and discriminative features from various omics before data integration. The clinical information is used to supervise the process of feature extraction based on SHAP (SHapley Additive exPlanation) values. Singular value decomposition (SVD) is then applied to integrate the extracted features of different omics by constructing a latent subspace. Finally, we utilize shared nearest neighbor-based spectral clustering on the latent representation to obtain the clustering result. The proposed method is evaluated on several cancer datasets across three levels of omics, in comparison to several state-of-the-art multi-omics clustering methods. The comparison results demonstrate the superior performance of the proposed method in multi-omics data analysis for cancer subtyping. Additionally, experiments reveal the efficacy of utilizing clinical information based on SHAP values for feature extraction, enhancing the performance of clustering analyses. Moreover, enrichment analysis of the identified gene signatures in different subtypes is also performed to further demonstrate the effectiveness of the proposed method. Availability: The proposed method can be freely accessible at https://github.com/Tianyi-Shi-Tsukuba/Multi-omics-clustering-based-on-SHAP. Data will be made available on request.

Translation of genome-wide association study: from genomic signals to biological insights

Since the turn of the 21st century, genome-wide association study (GWAS) have successfully identified genetic signals associated with a myriad of common complex traits and diseases. As we transition from establishing robust genetic associations with diverse phenotypes, the central challenge is now focused on characterizing the underlying functional mechanisms driving these signals. Previous GWAS efforts have revealed multiple variants, each conferring relatively subtle susceptibility, collectively contributing to the pathogenesis of various common diseases. Such variants can further exhibit associations with multiple other traits and differ across ancestries, plus disentangling causal variants from non-causal due to linkage disequilibrium complexities can lead to challenges in drawing direct biological conclusions. Combined with cellular context considerations, such challenges can reduce the capacity to definitively elucidate the biological significance of GWAS signals, limiting the potential to define mechanistic insights. This review will detail current and anticipated approaches for functional interpretation of GWAS signals, both in terms of characterizing the underlying causal variants and the corresponding effector genes.

Drug Mechanism: A bioinformatic update

A drug Mechanism of Action (MoA) is a complex biological phenomenon that describes how a bioactive compound produces a pharmacological effect. The complete knowledge of MoA is fundamental to fully understanding the drug activity. Over the years, many experimental methods have been developed and a huge quantity of data has been produced. Nowadays, considering the increasing omics data availability and the improvement of the accessible computational resources, the study of a drug MoA is conducted by integrating experimental and bioinformatics approaches. The development of new in silico solutions for this type of analysis is continuously ongoing; herein, an updating review on such bioinformatic methods is presented. The methodologies cited are based on multi-omics data integration in biochemical networks and Machine Learning (ML). The multiple types of usable input data and the advantages and disadvantages of each method have been analyzed, with a focus on their applications. Three specific research areas (i.e. cancer drug development, antibiotics discovery, and drug repurposing) have been chosen for their importance in the drug discovery fields in which the study of drug MoA, through novel bioinformatics approaches, is particularly productive.

Decellularized extracellular matrix derived from dental pulp stem cells promotes gingival fibroblast adhesion and migration

BACKGROUND

Decellularized extracellular matrix (dECM) has been proposed as a useful source of biomimetic materials for regenerative medicine due to its biological properties that regulate cell behaviors. The present study aimed to investigate the influence of decellularized ECM derived from dental pulp stem cells (DPSCs) on gingival fibroblast (GF) cell behaviors. Cells were isolated from dental pulp and gingival tissues. ECM was derived from culturing dental pulp stem cells in growth medium supplemented with ascorbic acid. A bioinformatic database of the extracellular matrix was constructed using Metascape. GFs were reseeded onto dECM, and their adhesion, spreading, and organization were subsequently observed. The migration ability of the cells was determined using a scratch assay. Protein expression was evaluated using immunofluorescence staining.

RESULTS

Type 1 collagen and fibronectin were detected on the ECM and dECM derived from DPSCs. Negative phalloidin and nuclei were noted in the dECM. The proteomic database revealed enrichment of several proteins involved in ECM organization, ECM-receptor interaction, and focal adhesion. Compared with those on the controls, the GFs on the dECM exhibited more organized stress fibers. Furthermore, cultured GFs on dECM exhibited significantly enhanced migration and proliferation abilities. Interestingly, GFs seeded on dECM showed upregulation of FN1, ITGB3, and CTNNB1 mRNA levels.

CONCLUSIONS

ECM derived from DSPCs generates a crucial microenvironment for regulating GF adhesion, migration and proliferation. Therefore, decellularized ECM from DPSCs could serve as a matrix for oral tissue repair.

Divergent iron regulatory states contribute to heterogeneity in breast cancer aggressiveness

Contact with dense collagen I (Col1) can induce collective invasion of triple negative breast cancer (TNBC) cells and transcriptional signatures linked to poor patient prognosis. However, this response is heterogeneous and not well understood. Using phenotype-guided sequencing analysis of invasive vs. noninvasive subpopulations, we show that these two phenotypes represent opposite sides of the iron response protein 1 (IRP1)-mediated response to cytoplasmic labile iron pool (cLIP) levels. Invasive cells upregulate iron uptake and utilization machinery characteristic of a low cLIP response, which includes contractility regulating genes that drive migration. Non-invasive cells upregulate iron sequestration machinery characteristic of a high cLIP response, which is accompanied by upregulation of actin sequestration genes. These divergent IRP1 responses result from Col1-induced transient expression of heme oxygenase I (HO-1), which cleaves heme and releases iron. These findings lend insight into the emerging theory that heme and iron fluxes regulate TNBC aggressiveness.

Deciphering the Intracellular Action of the Antimicrobial Peptide A11 via an In-Depth Analysis of Its Effect on the Global Proteome of Acinetobacter baumannii

The potential antimicrobial activity and low propensity to induce the development of bacterial resistance have rendered antimicrobial peptides (AMPs) as novel and ideal candidate therapeutic agents for the treatment of infections caused by drug-resistant pathogenic bacteria. The targeting of bacterial membranes by AMPs has been typically considered their sole mode of action; however, increasing evidence supports the existence of multiple and complementary functions of AMPs that result in bacterial death. An in-depth characterization of their mechanism of action could facilitate further research and development of AMPs with higher potency. The current study employs biophysics and proteomics approaches to unveil the mechanisms underlying the antibacterial activity of A11, a potential candidate AMP, against Acinetobacter baumannii, a leading cause of hospital-acquired infections (HAIs) and consequently, a serious global threat. A11 peptide was found to induce membrane depolarization to a high extent, as revealed by flow cytometry and electron microscopy analyses. The prompt intracellular penetration of A11 peptide, observed using confocal microscopy, was found to occur concomitantly with a very low degree of membrane lysis, suggesting that its mode of action predominantly involves a nonlytic killing mechanism. Quantitative proteomics analysis employed for obtaining insights into the mechanisms underlying the antimicrobial activity of A11 peptide revealed that it disrupted energy metabolism, interfered with protein homeostasis, and inhibited fatty acid synthesis that is essential for cell membrane integrity; all these impacted the cellular functions of A. baumannii. A11 treatment also impacted signal transduction associated with the regulation of biofilm formation, hindered the stress response, and influenced DNA repair processes; these are all crucial survival mechanisms of A. baumannii. Additionally, robust antibacterial activity was exhibited by A11 peptide against multidrug-resistant (MDR) and extensively drug-resistant (XDR) clinical isolates of A. baumannii; moreover, A11 peptide exhibited synergy with levofloxacin and minocycline as well as low propensity for inducing resistance. Taken together, the findings emphasize the therapeutic potential of A11 peptide as an antibacterial agent against drug-resistant A. baumannii and underscore the need for further investigation.

Transcriptomic changes in the posterior pallium of male zebra finches associated with social niche conformance

Animals plastically adjust their physiological and behavioural phenotypes to conform to their social environment-social niche conformance. The degree of sexual competition is a critical part of the social environment to which animals adjust their phenotypes, but the underlying genetic mechanisms are poorly understood. We conducted a study to investigate how differences in sperm competition risk affect the gene expression profiles of the testes and two brain areas (posterior pallium and optic tectum) in breeding male zebra finches (Taeniopygia castanotis). In this pre-registered study, we investigated a large sample of 59 individual transcriptomes. We compared two experimental groups: males held in single breeding pairs (low sexual competition) versus those held in two pairs (elevated sexual competition) per breeding cage. Using weighted gene co-expression network analysis (WGCNA), we observed significant effects of the social treatment in all three tissues. However, only the treatment effects found in the pallium were confirmed by an additional randomisation test for statistical robustness. Likewise, the differential gene expression analysis revealed treatment effects only in the posterior pallium (ten genes) and optic tectum (six genes). No treatment effects were found in the testis at the single gene level. Thus, our experiments do not provide strong evidence for transcriptomic adjustment specific to manipulated sperm competition risk. However, we did observe transcriptomic adjustments to the manipulated social environment in the posterior pallium. These effects were polygenic rather than based on few individual genes with strong effects. Our findings are discussed in relation to an accompanying paper using the same animals, which reports behavioural results consistent with the results presented here.

Investigation of the Circular Transcriptome in Alzheimer's Disease Brain

Circular RNAs (circRNAs) are a subclass of non-coding RNAs which have demonstrated potential as biomarkers for Alzheimer's disease (AD). In this study, we conducted a comprehensive exploration of the circRNA transcriptome within AD brain tissues. Specifically, we assessed circRNA expression patterns in the dorsolateral prefrontal cortex collected from nine AD-afflicted individuals and eight healthy controls. Utilising two circRNA detection tools, CIRI2 and CIRCexplorer2, we detected thousands of circRNAs and performed a differential expression analysis. CircRNAs which exhibited statistically significantly differential expression were identified as AD-specific differentially expressed circRNAs. Notably, our investigation revealed 120 circRNAs with significant upregulation and 1325 circRNAs displaying significant downregulation in AD brains when compared to healthy brain tissue. Additionally, we explored the expression profiles of the linear RNA counterparts corresponding to differentially expressed circRNAs in AD-afflicted brains and discovered that the linear RNA counterparts exhibited no significant changes in the levels of expression. We used CRAFT tool to predict that circUBE4B had potential to target miRNA named as hsa-miR-325-5p, ultimately regulated CD44 gene. This study provides a comprehensive overview of differentially expressed circRNAs in the context of AD brains, underscoring their potential as molecular biomarkers for AD. These findings significantly enhance our comprehension of AD's underlying pathophysiological mechanisms, offering promising avenues for future diagnostic and therapeutic developments.

Gene Set Summarization Using Large Language Models

Molecular biologists frequently interpret gene lists derived from high-throughput experiments and computational analysis. This is typically done as a statistical enrichment analysis that measures the over- or under-representation of biological function terms associated with genes or their properties, based on curated assertions from a knowledge base (KB) such as the Gene Ontology (GO). Interpreting gene lists can also be framed as a textual summarization task, enabling Large Language Models (LLMs) to use scientific texts directly and avoid reliance on a KB. TALISMAN (Terminological ArtificiaL Intelligence SuMmarization of Annotation and Narratives) uses generative AI to perform gene set function summarization as a complement to standard enrichment analysis. This method can use different sources of gene functional information: (1) structured text derived from curated ontological KB annotations, (2) ontology-free narrative gene summaries, or (3) direct retrieval from the model. We demonstrate that these methods are able to generate plausible and biologically valid summary GO term lists for an input gene set. However, LLM-based approaches are unable to deliver reliable scores or p-values and often return terms that are not statistically significant. Crucially, in our experiments these methods were rarely able to recapitulate the most precise and informative term from standard enrichment analysis. We also observe minor differences depending on prompt input information, with GO term descriptions leading to higher recall but lower precision. However, newer LLM models perform statistically significantly better than the oldest model across all performance metrics, suggesting that future models may lead to further improvements. Overall, the results are nondeterministic, with minor variations in prompt resulting in radically different term lists, true to the stochastic nature of LLMs. Our results show that at this point, LLM-based methods are unsuitable as a replacement for standard term enrichment analysis, however they may provide summarization benefits for implicit knowledge integration across extant but unstandardized knowledge, for large sets of features, and where the amount of information is difficult for humans to process.

Feature engineering from meta-data for prediction of differentially expressed genes: An investigation of Mus musculus exposed to space-conditions

Transcription profiling is a key process that can reveal those biological mechanisms driving the response to various exposure conditions or gene perturbations. In this work, we investigate the prediction of differentially expressed genes (DEGs) when exposed to conditions in space from a set of diverse engineered features. To do this, we collected DEGs and non-differentially expressed genes (NDEGs) of Mus musculus-based experiments on the GeneLab database. We engineered a diverse set of features from factors reported in the literature to affect gene expression. An extreme gradient boosting (XGBoost) model was trained to predict if a given gene would be differentially expressed at various levels of differential expression. The test results on a separate holdout dataset showed an area under the receiver operating characteristics curves (AUCs) of 0.90±0.07, averaged across the five selected percentages of the most and least differentially expressed genes. Subsequently, we investigated the impact of selection of features, both individually with a correlation-based feature-selection procedure and in groups with a combination procedure, on the prediction performance. The feature selection confirmed some known drivers of adaptation to radiation and highlighted some new transcription factors and micro RNAs (miRNAs). Finally, gene ontology (GO) analysis revealed biological processes that tend to have expression patterns most suitable for this approach. This work highlights the potential of detection of differentially expressed genes using a machine learning (ML) approach, and provides some evidence of gene expression changes being captured by a diverse feature set not related to the condition under study.

Eight structurally diverse components with anti-acetylcholinesterase activity from Daphne bholua

Eight structurally diverse components, including six undescribed ones, (±)-daphuarin A (1a/1b), daphuarin B (2), daphuarin D-E (4-6), together with a pair of new natural products (±)-daphuarin C (3a/3b) were isolated from the herb of Daphne bholua Buch.-Ham. ex D. Don. Their planar structures were elucidated by extensive spectroscopic analyses. The configurations were established with the assistance of quantum chemical calculations, together with the Custom DP4+ method. The inhibitory potentials of all isolates against acetylcholinesterase were evaluated.

Gene expression variation underlying tissue-specific responses to copper stress in Drosophila melanogaster

Copper is one of a handful of biologically necessary heavy metals that is also a common environmental pollutant. Under normal conditions, copper ions are required for many key physiological processes. However, in excess, copper results in cell and tissue damage ranging in severity from temporary injury to permanent neurological damage. Because of its biological relevance, and because many conserved copper-responsive genes respond to nonessential heavy metal pollutants, copper resistance in Drosophila melanogaster is a useful model system with which to investigate the genetic control of the heavy metal stress response. Because heavy metal toxicity has the potential to differently impact specific tissues, we genetically characterized the control of the gene expression response to copper stress in a tissue-specific manner in this study. We assessed the copper stress response in head and gut tissue of 96 inbred strains from the Drosophila Synthetic Population Resource using a combination of differential expression analysis and expression quantitative trait locus mapping. Differential expression analysis revealed clear patterns of tissue-specific expression. Tissue and treatment specific responses to copper stress were also detected using expression quantitative trait locus mapping. Expression quantitative trait locus associated with MtnA, Mdr49, Mdr50, and Sod3 exhibited both genotype-by-tissue and genotype-by-treatment effects on gene expression under copper stress, illuminating tissue- and treatment-specific patterns of gene expression control. Together, our data build a nuanced description of the roles and interactions between allelic and expression variation in copper-responsive genes, provide valuable insight into the genomic architecture of susceptibility to metal toxicity, and highlight candidate genes for future functional characterization.

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.

Screening the critical protein subnetwork to delineate potential mechanisms and protective agents associated with arsenic-induced cutaneous squamous cell carcinoma: A toxicogenomic study

Recent studies show that complex mechanisms are involved in arsenic-induced malignant transformation of cells. This study aimed to decipher molecular mechanisms associated with arsenic-induced cutaneous squamous cell carcinoma (cSCC) and suggest potential protective factors. RNA-seq-based differentially expressed genes between arsenic-exposed human keratinocytes (HaCaT) and controls were used to construct a protein-protein interaction (PPI) network and discover critical subnetwork-based mechanisms. Protective compounds against arsenic toxicity were determined and their target interactions in the core sub-network were identified by the comparative toxicogenomic database (CTD). The binding affinity between the effective factor and target was calculated by molecular docking. A total of 15 key proteins were screened out as critical arsenic-responsive subnetwork (FN1, IL-1A, CCN2, PECAM1, FGF5, EDN1, FGF1, PXDN, DNAJB9, XBP1, ERN1, PDIA4, DNAJB11, FOS, PDIA6) and 7 effective protective agents were identified (folic acid, quercetin, zinc, acetylcysteine, methionine, catechin, selenium). The GeneMANIA predicted detailed interactions of the subnetwork and revealed terms related to unfolded protein response as the main processes. FN1, IL1A and CCN2, as top significant genes, had good docking affinity with folic acid and quercetin, as selected key compounds. Integration of gene expression and protein-protein interaction related to arsenic exposure in cSCC explored the potential mechanisms and protective agents.

Divergent iron-regulatory states contribute to heterogeneity in breast cancer aggressiveness

Primary tumors with similar mutational profiles can progress to vastly different outcomes where transcriptional state, rather than mutational profile, predicts prognosis. A key challenge is to understand how distinct tumor cell states are induced and maintained. In triple negative breast cancer cells, invasive behaviors and aggressive transcriptional signatures linked to poor patient prognosis can emerge in response to contact with collagen type I. Herein, collagen-induced migration heterogeneity within a TNBC cell line was leveraged to identify transcriptional programs associated with invasive versus non-invasive phenotypes and implicate molecular switches. Phenotype-guided sequencing revealed that invasive cells upregulate iron uptake and utilization machinery, anapleurotic TCA cycle genes, actin polymerization promoters, and a distinct signature of Rho GTPase activity and contractility regulating genes. The non-invasive cell state is characterized by actin and iron sequestration modules along with glycolysis gene expression. These unique tumor cell states are evident in patient tumors and predict divergent outcomes for TNBC patients. Glucose tracing confirmed that non-invasive cells are more glycolytic than invasive cells, and functional studies in cell lines and PDO models demonstrated a causal relationship between phenotype and metabolic state. Mechanistically, the OXPHOS dependent invasive state resulted from transient HO-1 upregulation triggered by contact with dense collagen that reduced heme levels and mitochondrial chelatable iron levels. This induced expression of low cytoplasmic iron response genes regulated by ACO1/IRP1. Knockdown or inhibition of HO-1, ACO1/IRP1, MRCK, or OXPHOS abrogated invasion. These findings support an emerging theory that heme and iron flux serve as important regulators of TNBC aggressiveness.

Expanded Application of Piper nigrum: Guided Isolation of Alkaloids with Inhibitory Activities of AChE/BuChE and Aβ Aggregation

Piper nigrum is a popular crop that can be used as seasoning or as an additive but its active ingredients also have an effect on the nervous system. Nineteen new amide alkaloids (1a/1b, 2-5, 6a/6b, 7, 8a/8b, 9, 10a/10b, 11a-11b, 12-14) were isolated from P. nigrum, guided by inhibitory activity of AChE and LC-MS/MS based on GNPS. The configurations were determined by extensive spectral analysis, Bulkiness rule, and NMR calculations. The inhibitory activities of AChE/BuChE and Aβ aggregation were tested, and the results showed compounds 2, 7, and 12 had significant inhibitory activities. These components were identified in the crude fraction and their relative quantities were tested, which suggested that compound 2 was the index component in the active site from P. nigrum.

A novel feature selection algorithm for identifying hub genes in lung cancer

Lung cancer, a life-threatening disease primarily affecting lung tissue, remains a significant contributor to mortality in both developed and developing nations. Accurate biomarker identification is imperative for effective cancer diagnosis and therapeutic strategies. This study introduces the Voting-Based Enhanced Binary Ebola Optimization Search Algorithm (VBEOSA), an innovative ensemble-based approach combining binary optimization and the Ebola optimization search algorithm. VBEOSA harnesses the collective power of the state-of-the-art classification models through soft voting. Moreover, our research applies VBEOSA to an extensive lung cancer gene expression dataset obtained from TCGA, following essential preprocessing steps including outlier detection and removal, data normalization, and filtration. VBEOSA aids in feature selection, leading to the discovery of key hub genes closely associated with lung cancer, validated through comprehensive protein-protein interaction analysis. Notably, our investigation reveals ten significant hub genes-ADRB2, ACTB, ARRB2, GNGT2, ADRB1, ACTG1, ACACA, ATP5A1, ADCY9, and ADRA1B-each demonstrating substantial involvement in the domain of lung cancer. Furthermore, our pathway analysis sheds light on the prominence of strategic pathways such as salivary secretion and the calcium signaling pathway, providing invaluable insights into the intricate molecular mechanisms underpinning lung cancer. We also utilize the weighted gene co-expression network analysis (WGCNA) method to identify gene modules exhibiting strong correlations with clinical attributes associated with lung cancer. Our findings underscore the efficacy of VBEOSA in feature selection and offer profound insights into the multifaceted molecular landscape of lung cancer. Finally, we are confident that this research has the potential to improve diagnostic capabilities and further enrich our understanding of the disease, thus setting the stage for future advancements in the clinical management of lung cancer. The VBEOSA source codes is publicly available at https://github.com/TEHNAN/VBEOSA-A-Novel-Feature-Selection-Algorithm-for-Identifying-hub-Genes-in-Lung-Cancer .

The two-stage molecular scenery of SARS-CoV-2 infection with implications to disease severity: An in-silico quest

Introduction

The two-stage molecular profile of the progression of SARS-CoV-2 (SCOV2) infection is explored in terms of five key biological/clinical questions: (a) does SCOV2 exhibits a two-stage infection profile? (b) SARS-CoV-1 (SCOV1) vs. SCOV2: do they differ? (c) does and how SCOV2 differs from Influenza/INFL infection? (d) does low viral-load and (e) does COVID-19 early host response relate to the two-stage SCOV2 infection profile? We provide positive answers to the above questions by analyzing the time-series gene-expression profiles of preserved cell-lines infected with SCOV1/2 or, the gene-expression profiles of infected individuals with different viral-loads levels and different host-response phenotypes.

Methods

Our analytical methodology follows an in-silico quest organized around an elaborate multi-step analysis pipeline including: (a) utilization of fifteen gene-expression datasets from NCBI's gene expression omnibus/GEO repository; (b) thorough designation of SCOV1/2 and INFL progression stages and COVID-19 phenotypes; (c) identification of differentially expressed genes (DEGs) and enriched biological processes and pathways that contrast and differentiate between different infection stages and phenotypes; (d) employment of a graph-based clustering process for the induction of coherent groups of networked genes as the representative core molecular fingerprints that characterize the different SCOV2 progression stages and the different COVID-19 phenotypes. In addition, relying on a sensibly selected set of induced fingerprint genes and following a Machine Learning approach, we devised and assessed the performance of different classifier models for the differentiation of acute respiratory illness/ARI caused by SCOV2 or other infections (diagnostic classifiers), as well as for the prediction of COVID-19 disease severity (prognostic classifiers), with quite encouraging results.

Results

The central finding of our experiments demonstrates the down-regulation of type-I interferon genes (IFN-1), interferon induced genes (ISGs) and fundamental innate immune and defense biological processes and molecular pathways during the early SCOV2 infection stages, with the inverse to hold during the later ones. It is highlighted that upregulation of these genes and pathways early after infection may prove beneficial in preventing subsequent uncontrolled hyperinflammatory and potentially lethal events.

Discussion

The basic aim of our study was to utilize in an intuitive, efficient and productive way the most relevant and state-of-the-art bioinformatics methods to reveal the core molecular mechanisms which govern the progression of SCOV2 infection and the different COVID-19 phenotypes.

Promotion of Th1 and Th2 responses over Th17 in Riemerella anatipestifer stimulation in chicken splenocytes: Correlation of gga-miR-456-3p and gga-miR-16-5p with NOS2 and CCL5 expression

Riemerella (R.) anatipestifer poses a significant threat to ducks, resulting in mortality rates ranging from 5-75%. This disease is highly infectious and economically consequential for domestic ducks. Although other avian species, such as chickens, also display susceptibility, the impact is comparatively less severe than in ducks. IL-17A has a pronounced correlation with R. anatipestifer infection in ducks, which is less in chickens. This study performed an in vitro transcriptome analysis using chicken splenic lymphocytes collected at 4-, 8-, and 24-hour intervals following R. anatipestifer stimulation. The primary objective was to discern the differentially expressed genes, with a specific focus on IL-17A and IL-17F expression. Moreover, an association between specific miRNAs with NOS2 and CCL5 was identified. The manifestation of riemerellosis in chickens was linked to heightened expression of Th1- and Th2-associated cells, while Th17 cells exhibited minimal involvement. This study elucidated the mechanism behind the absence of a Th17 immune response, shedding light on its role throughout disease progression. Additionally, through small RNA sequencing, we identified a connection between miRNAs, specifically miR-456-3p and miR-16-5p, and their respective target genes NOS2 and CCL5. These miRNAs are potential regulators of the inflammatory process during riemerellosis in chickens.

The atrial and ventricular myocardial proteome of end-stage lamin heart disease

Lamins A/C (encoded by LMNA gene) can lead to dilated cardiomyopathy (DCM). This pilot study sought to explore the postgenomic phenotype of end-stage lamin heart disease. Consecutive patients with end-stage lamin heart disease (LMNA-group, n = 7) and ischaemic DCM (ICM-group, n = 7) undergoing heart transplantation were prospectively enrolled. Samples were obtained from left atrium (LA), left ventricle (LV), right atrium (RA), right ventricle (RV) and interventricular septum (IVS), avoiding the infarcted myocardial segments in the ICM-group. Samples were analysed using a discovery 'shotgun' proteomics approach. We found that 990 proteins were differentially abundant between LMNA and ICM samples with the LA being most perturbed (16-fold more than the LV). Abundance of lamin A/C protein was reduced, but lamin B increased in LMNA LA/RA tissue compared to ICM, but not in LV/RV. Carbonic anhydrase 3 (CA3) was over-abundant across all LMNA tissue samples (LA, LV, RA, RV, and IVS) when compared to ICM. Transthyretin was more abundant in the LV/RV of LMNA compared to ICM, while sarcomeric proteins such as titin and cardiac alpha-cardiac myosin heavy chain were generally less abundant in RA/LA of LMNA. Protein expression profiling and enrichment analysis pointed towards sarcopenia, extracellular matrix remodeling, deficient myocardial energetics, redox imbalances, and abnormal calcium handling in LMNA samples. Compared to ICM, end-stage lamin heart disease is a biventricular but especially a biatrial disease appearing to have an abundance of lamin B, CA3 and transthyretin, potentially hinting to compensatory responses.

Responses to predation pressure involve similar sets of genes in two divergent species of Daphnia

Species that are not closely related can express similar inducible traits, but molecular mechanisms underlying the observed responses are often unknown, nor is it known if these mechanisms are shared between such species. Here, we compared transcriptional profiles of two Daphnia species (D. mitsukuri and D. sinensis) from different subgenera, at both juvenile and adult developmental stages. Both species were exposed to the same predation threat (fish kairomones), and both showed similar induced morphological changes (reduced body length). At the early developmental stage, response to predation risk resulted in similar changes in expression levels of 23 orthologues in both species. These orthologues, involved in 107 GO categories, changed in the same direction in both species (over- or underexpressed), in comparison to non-exposed controls. Several of these orthologues were associated with DNA replication, structural constituents of cuticle or innate immune response. In both species, the differentially expressed (DE) genes on average had higher ω (dN /dS ) values than non-DE genes, suggesting that these genes had experienced greater positive selection or lower purifying selection than non-DE genes. Overall, our results suggest that similar suites of genes, responding in similar ways to predation pressure, have been retained in Daphnia for many millions of years.

Momordica charantia Enhances Tendon Healing in Rats: An Experimental Study

Momordica charantia (MC) is a traditional plant widely used since ancient times for wound healing. This study evaluated its potential effects on tendon healing. Adult male Wistar albino rats (n = 32, 8 rats in each group) were anesthetized, and their Achilles tendons were prepared for surgical procedures. Group 1 (Cont = control group) was not subjected to any surgery and was used as a control group for baseline values. Group 2 (PR = primary repair group) underwent primary repair (PR) with a monofilament suture after a full-thickness incision of the Achilles tendon. A full-thickness incision was also made to the Achilles tendon of group 3 (CT = collagen tube-administered group), followed by PR and collagen tube insertion. In group 4 (MC = M. charantia-administered group), 1 mL of MC extract was applied locally on the collagen tube in addition to the surgical procedure applied to group 3. The Achilles tendons were excised on the postoperative 40th day and examined stereologically, histologically, and bioinformatically. Data showed that the total volume of the collagen fibers was higher in MC and CT groups than in the PR group. The total volume of the tendon was decreased in MC and CT groups than in the Cont group. The ratios between the volumes of the collagen fibers and total tendon in the MC and CT groups were significantly different from PR, but not different from the Cont group. Additionally, MC improved tenoblastic activity, collagen production, and neovascularization. Bioinformatic interactions showed that the proteases of MC could trigger the signals playing a role on vasculogenesis, reducing inflammation, and contributing to tenoblast activation and collagen remodeling. MC extract ameliorates the healing of injured tendon and can provide satisfactory tendon repair. Further works are recommended to explore the healing capacity of MC.

Claudin-3 facilitates the progression and mediates the tumorigenic effects of TGF-β in glioblastoma multiforme

Glioblastoma multiforme (GBM) is a significantly malignant and lethal brain tumor with an average survival time of less than 12 months. Several researches had shown that Claudin-3 (CLDN3) is overexpressed in various cancers and might be important in their growth and spread. In this study, we used qRT-PCR, western blotting, immunohistochemistry, and immunofluorescence staining assays to investigate the expression levels of various proteins. To explore the proliferation abilities of GBM cells, we conducted the CCK-8 and EdU-DNA formation assays. Wound healing and transwell assays were used to investigate the capacities of invasion and migration of GBM cells. Additionally, we constructed an intracranial xenograft model of GBM to study the in vivo role of CLDN3. Our study devoted to investigate the function of CLDN3 in the pathogenesis and progression of GBM. Our study revealed that CLDN3 was upregulated in GBM and could stimulate tumor cell growth and epithelial-mesenchymal transition (EMT) in both laboratory and animal models. We also discovered that CLDN3 expression could be triggered by transforming growth factor-β (TGF-β) and reduced by specific inhibitors of the TGF-β signaling pathway, such as ITD-1. Further analysis revealed that increased CLDN3 levels enhanced TGF-β-induced growth and EMT in GBM cells, while reducing CLDN3 levels weakened these effects. Our study demonstrated the function of CLDN3 in facilitating GBM growth and metastasis and indicated its involvement in the tumorigenic effects of TGF-β. Developing specific inhibitors of CLDN3 might, therefore, represent a promising new approach for treating this devastating disease.

Isoflurane Rapidly Modifies Synaptic and Cytoskeletal Phosphoproteomes of the Supraoptic Nucleus of the Hypothalamus and the Cortex

INTRODUCTION

Despite the widespread use of general anaesthetics, the mechanisms mediating their effects are still not understood. Although suppressed in most parts of the brain, neuronal activity, as measured by FOS activation, is increased in the hypothalamic supraoptic nucleus (SON) by numerous general anaesthetics, and evidence points to this brain region being involved in the induction of general anaesthesia (GA) and natural sleep. Posttranslational modifications of proteins, including changes in phosphorylation, enable fast modulation of protein function which could be underlying the rapid effects of GA. In order to identify potential phosphorylation events in the brain-mediating GA effects, we have explored the phosphoproteome responses in the rat SON and compared these to cingulate cortex (CC) which displays no FOS activation in response to general anaesthetics.

METHODS

Adult Sprague-Dawley rats were treated with isoflurane for 15 min. Proteins from the CC and SON were extracted and processed for nano-LC mass spectrometry (LC-MS/MS). Phosphoproteomic determinations were performed by LC-MS/MS.

RESULTS

We found many changes in the phosphoproteomes of both the CC and SON in response to 15 min of isoflurane exposure. Pathway analysis indicated that proteins undergoing phosphorylation adaptations are involved in cytoskeleton remodelling and synaptic signalling events. Importantly, changes in protein phosphorylation appeared to be brain region specific suggesting that differential phosphorylation adaptations might underlie the different neuronal activity responses to GA between the CC and SON.

CONCLUSION

In summary, these data suggest that rapid posttranslational modifications in proteins involved in cytoskeleton remodelling and synaptic signalling events might mediate the central mechanisms mediating GA.

Breast Cancer Therapeutics and Hippo Signaling Pathway: Novel MicroRNA-Gene-Protein Interaction Networks

The Hippo signaling pathway is a master regulator of development, cell proliferation, and apoptosis in particular, and it plays an important role in tissue regeneration, controlling organ size, and cancer suppression. Dysregulation of the Hippo signaling pathway has been implicated in breast cancer, a highly prevalent cancer affecting 1 out of every 15 women worldwide. While the Hippo signaling pathway inhibitors are available, they are suboptimal, for example, due to chemoresistance, mutation, and signal leakage. Inadequate knowledge about the Hippo pathway connections and their regulators limits our ability to uncover novel molecular targets for drug development. We report here novel microRNA (miRNA)-gene and protein-protein interaction networks in the Hippo signaling pathway. We employed the GSE miRNA dataset for the present study. The GSE57897 dataset was normalized and searched for differentially expressed miRNAs, and their targets were searched using the miRWalk2.0 tool. From the upregulated miRNAs, we observed that the hsa-miR-205-5p forms the biggest cluster and targets four genes involved in the Hippo signaling pathway. Interestingly, we found a novel connection between two Hippo signaling pathway proteins, angiomotin (AMOT) and mothers against decapentaplegic homolog 4 (SMAD4). From the downregulated miRNAs, hsa-miR-16-5p, hsa-miR-7g-5p, hsa-miR-141-3p, hsa-miR-103a-3p, hsa-miR-21-5p, and hsa-miR-200c-3p, target genes were present in the pathway. We found that PTEN, EP300, and BTRC were important cancer-inhibiting proteins, form hubs, and their genes interact with downregulating miRNAs. We suggest that targeting proteins from these newly unraveled networks in the Hippo signaling pathway and further research on the interaction of hub-forming cancer-inhibiting proteins can open up new avenues for next-generation breast cancer therapeutics.

Ageing restructures the transcriptome of the hypothalamic supraoptic nucleus and alters the response to dehydration

Ageing is associated with altered neuroendocrine function. In the context of the hypothalamic supraoptic nucleus, which makes the antidiuretic hormone vasopressin, ageing alters acute responses to hyperosmotic cues, rendering the elderly more susceptible to dehydration. Chronically, vasopressin has been associated with numerous diseases of old age, including type 2 diabetes and metabolic syndrome. Bulk RNAseq transcriptome analysis has been used to catalogue the polyadenylated supraoptic nucleus transcriptomes of adult (3 months) and aged (18 months) rats in basal euhydrated and stimulated dehydrated conditions. Gene ontology and Weighted Correlation Network Analysis revealed that ageing is associated with alterations in the expression of extracellular matrix genes. Interestingly, whilst the transcriptomic response to dehydration is overall blunted in aged animals compared to adults, there is a specific enrichment of differentially expressed genes related to neurodegenerative processes in the aged cohort, suggesting that dehydration itself may provoke degenerative consequences in aged rats.

Xenbase: key features and resources of the Xenopus model organism knowledgebase

Xenbase (https://www.xenbase.org/), the Xenopus model organism knowledgebase, is a web-accessible resource that integrates the diverse genomic and biological data from research on the laboratory frogs Xenopus laevis and Xenopus tropicalis. The goal of Xenbase is to accelerate discovery and empower Xenopus research, to enhance the impact of Xenopus research data, and to facilitate the dissemination of these data. Xenbase also enhances the value of Xenopus data through high-quality curation, data integration, providing bioinformatics tools optimized for Xenopus experiments, and linking Xenopus data to human data, and other model organisms. Xenbase also plays an indispensable role in making Xenopus data interoperable and accessible to the broader biomedical community in accordance with FAIR principles. Xenbase provides annotated data updates to organizations such as NCBI, UniProtKB, Ensembl, the Gene Ontology consortium, and most recently, the Alliance of Genomic Resources, a common clearing house for data from humans and model organisms. This article provides a brief overview of key and recently added features of Xenbase. New features include processing of Xenopus high-throughput sequencing data from the NCBI Gene Expression Omnibus; curation of anatomical, physiological, and expression phenotypes with the newly created Xenopus Phenotype Ontology; Xenopus Gene Ontology annotations; new anatomical drawings of the Normal Table of Xenopus development; and integration of the latest Xenopus laevis v10.1 genome annotations. Finally, we highlight areas for future development at Xenbase as we continue to support the Xenopus research community.

Bioinformatics and network biology approach to identifying type 2 diabetes genes and pathways that influence the progression of breast cancer

Breast cancer is the second most prevalent malignancy affecting women. Postmenopausal women breast tumor is one of the top causes of death in women, accounting for 23% of cancer cases. Type 2 diabetes, a worldwide pandemic, has been connected to a heightened risk of several malignancies, although its association with breast cancer is still uncertain. In comparison to non-diabetic women, women with T2DM had a 23% elevated likelihood of developing breast cancer. It is difficult to determine causative or genetic susceptibility that connect T2DM and breast cancer. We created a large-scale network-based quantitative approach employing unbiased methods to discover abnormally amplified genes in both T2DM and breast cancer, to solve these issues. We performed transcriptome analysis to uncover identical genetic biomarkers and pathways to clarify the connection between T2DM and breast cancer patients. In this study, two RNA-seq datasets (GSE103001 and GSE86468) from the Gene Expression Omnibus (GEO) are used to identify mutually differentially expressed genes (DEGs) for breast cancer and T2DM, as well as common pathways and prospective medicines. Firstly, 45 shared genes (30 upregulated and 15 downregulated) between T2D and breast cancer were detected. We employed gene ontology and pathway enrichment to characterize prevalent DEGs' molecular processes and signal transduction pathways and observed that T2DM has certain connections to the progression of breast cancer. Using several computational and statistical approaches, we created a protein-protein interactions (PPI) network and revealed hub genes. These hub genes can be potential biomarkers, which may also lead to new therapeutic strategies for investigated diseases. We conducted TF-gene interactions, gene-microRNA interactions, protein-drug interactions, and gene-disease associations to find potential connections between T2DM and breast cancer pathologies. We assume that the potential drugs that emerged from this study could be useful therapeutic values. Researchers, doctors, biotechnologists, and many others may benefit from this research.

Therapeutic effects of polydeoxyribonucleotide in an in vitro neuronal model of ischemia/reperfusion injury

Polydeoxyribonucleotide (PDRN) is an agonist that selectively stimulates adenosine A_2A receptor (ADORA2A), which suppresses inflammatory responses. Ischemia/reperfusion (I/R) injury plays a major role in the pathogenesis of ischemic stroke by inducing neuroinflammation. Therefore, this study aimed to investigate the therapeutic effects of PDRN in an in vitro I/R injury model. The in vitro model was established with differentiated Neuro-2a cells under oxygen and glucose deprivation condition. The cells were treated with PDRN for 24 h under reoxygenation condition. As the results of RNA-seq transcriptome analysis, CSF1, IL-6, PTPN6, RAC2, and STAT1 were identified of its relation to the effect of PDRN on inflammatory responses in the model. To further investigate therapeutic effects of PDRN, RT-qPCR, western blotting, LDH assay, and TUNEL assay were performed. PDRN significantly reversed the expression of genes and proteins related to inflammatory responses. The elevated ADORA2A expression by PDRN treatment downregulated JAK/STAT pathway in the model. Furthermore, PDRN inhibited neuronal cell death in the model. Consequently, our results suggested that PDRN alleviated inflammatory responses through inhibition of JAK/STAT pathway by mediating ADORA2A expression and inhibited neuronal cell death in the model. These results provide significant insights into potential therapeutic approaches involving PDRN treatment for I/R injury.

Interpreting omics data with pathway enrichment analysis

Pathway enrichment analysis is indispensable for interpreting omics datasets and generating hypotheses. However, the foundations of enrichment analysis remain elusive to many biologists. Here, we discuss best practices in interpreting different types of omics data using pathway enrichment analysis and highlight the importance of considering intrinsic features of various types of omics data. We further explain major components that influence the outcomes of a pathway enrichment analysis, including defining background sets and choosing reference annotation databases. To improve reproducibility, we describe how to standardize reporting methodological details in publications. This article aims to serve as a primer for biologists to leverage the wealth of omics resources and motivate bioinformatics tool developers to enhance the power of pathway enrichment analysis.

DNA-Methylation Signatures of Tobacco Smoking in a High Cardiovascular Risk Population: Modulation by the Mediterranean Diet

Biomarkers based on DNA methylation are relevant in the field of environmental health for precision health. Although tobacco smoking is one of the factors with a strong and consistent impact on DNA methylation, there are very few studies analyzing its methylation signature in southern European populations and none examining its modulation by the Mediterranean diet at the epigenome-wide level. We examined blood methylation smoking signatures on the EPIC 850 K array in this population (n = 414 high cardiovascular risk subjects). Epigenome-wide methylation studies (EWASs) were performed analyzing differential methylation CpG sites by smoking status (never, former, and current smokers) and the modulation by adherence to a Mediterranean diet score was explored. Gene-set enrichment analysis was performed for biological and functional interpretation. The predictive value of the top differentially methylated CpGs was analyzed using receiver operative curves. We characterized the DNA methylation signature of smoking in this Mediterranean population by identifying 46 differentially methylated CpGs at the EWAS level in the whole population. The strongest association was observed at the cg21566642 (p = 2.2 × 10-32) in the 2q37.1 region. We also detected other CpGs that have been consistently reported in prior research and discovered some novel differentially methylated CpG sites in subgroup analyses. In addition, we found distinct methylation profiles based on the adherence to the Mediterranean diet. Particularly, we obtained a significant interaction between smoking and diet modulating the cg5575921 methylation in the AHRR gene. In conclusion, we have characterized biomarkers of the methylation signature of tobacco smoking in this population, and suggest that the Mediterranean diet can increase methylation of certain hypomethylated sites.

Exploiting Multi-Omics Profiling and Systems Biology to Investigate Functions of TOMM34

Although modern biology is now in the post-genomic era with vastly increased access to high-quality data, the set of human genes with a known function remains far from complete. This is especially true for hundreds of mitochondria-associated genes, which are under-characterized and lack clear functional annotation. However, with the advent of multi-omics profiling methods coupled with systems biology algorithms, the cellular role of many such genes can be elucidated. Here, we report genes and pathways associated with TOMM34, Translocase of Outer Mitochondrial Membrane, which plays role in the mitochondrial protein import as a part of cytosolic complex together with Hsp70/Hsp90 and is upregulated in various cancers. We identified genes, proteins, and metabolites altered in TOMM34-/- HepG2 cells. To our knowledge, this is the first attempt to study the functional capacity of TOMM34 using a multi-omics strategy. We demonstrate that TOMM34 affects various processes including oxidative phosphorylation, citric acid cycle, metabolism of purine, and several amino acids. Besides the analysis of already known pathways, we utilized de novo network enrichment algorithm to extract novel perturbed subnetworks, thus obtaining evidence that TOMM34 potentially plays role in several other cellular processes, including NOTCH-, MAPK-, and STAT3-signaling. Collectively, our findings provide new insights into TOMM34's cellular functions.

konnect2prot: a web application to explore the protein properties in a functional protein-protein interaction network

MOTIVATION

The regulation of proteins governs the biological processes and functions and, therefore, the organisms' phenotype. So there is an unmet need for a systematic tool for identifying the proteins that play a crucial role in information processing in a protein-protein interaction (PPI) network. However, the current protein databases and web servers still lag behind to provide an end-to-end pipeline that can leverage the topological understanding of a context-specific PPI network to identify the influential spreaders. Addressing this, we developed a web application, 'konnect2prot' (k2p), which can generate context-specific directional PPI network from the input proteins and detect their biological and topological importance in the network.

RESULTS

We pooled together a large amount of ontological knowledge, parsed it down into a functional network, and gained insight into the molecular underpinnings of the disease development by creating a one-stop junction for PPI data. k2p contains both local and global information about a protein, such as protein class, disease mutations, ligands and PDB structure, enriched processes and pathways, multi-disease interactome and hubs and bottlenecks in the directional network. It also identifies spreaders in the network and maps them to disease hallmarks to determine whether they can affect the disease state or not.

AVAILABILITY AND IMPLEMENTATION

konnect2prot is freely accessible using the link https://konnect2prot.thsti.in. The code repository is https://github.com/samrat-lab/k2p_bioinfo-2022.

Unraveling the Transcriptomic Signatures of Homologous Recombination Deficiency in Ovarian Cancers

Homologous recombination deficiency (HRD) is a crucial driver of tumorigenesis by inducing impaired repair of double-stranded DNA breaks. Although HRD possibly triggers the production of numerous tumor neoantigens that sufficiently stimulate and activate various tumor-immune responses, a comprehensive understanding of the HRD-associated tumor microenvironment is elusive. To investigate the effect of HRD on the selective enrichment of transcriptomic signatures, 294 cases from The Cancer Genome Atlas-Ovarian Cancer project with both RNA-sequencing and SNP array data are analyzed. Differentially expressed gene analysis and network analysis are performed to identify HRD-specific signatures. Gene-sets associated with mitochondrial activation, including enhanced oxidative phosphorylation (OxPhos), are significantly enriched in the HRD-high group. Furthermore, a wide range of immune cell activation signatures is enriched in HRD-high cases of high-grade serous ovarian cancer (HGSOC). On further cell-type-specific analysis, M1-like macrophage genes are significantly enriched in HRD-high HGSOC cases, whereas M2-macrophage-related genes are not. The immune-response-associated genomic features, including tumor mutation rate, neoantigens, and tumor mutation burdens, correlated with HRD scores. In conclusion, the results of this study highlight the biological properties of HRD, including enhanced energy metabolism, increased tumor neoantigens and tumor mutation burdens, and consequent exacerbation of immune responses, particularly the enrichment of M1-like macrophages in HGSOC cases.

Functional genomic tools for emerging model species

Most studies in the field of ecology and evolution aiming to connect genotype to phenotype rarely validate identified loci using functional tools. Recent developments in RNA interference (RNAi) and clustered regularly interspaced palindromic repeats (CRISPR)-Cas genome editing have dramatically increased the feasibility of functional validation. However, these methods come with specific challenges when applied to emerging model organisms, including limited spatial control of gene silencing, low knock-in efficiencies, and low throughput of functional validation. Moreover, many functional studies to date do not recapitulate ecologically relevant variation, and this limits their scope for deeper insights into evolutionary processes. We therefore argue that increased use of gene editing by allelic replacement through homology-directed repair (HDR) would greatly benefit the field of ecology and evolution.