How many differentially expressed genes: A perspective from the comparison of genotypic and phenotypic distances

Effects of primary leachates of conventional and alternative plastics in Cyprinodon variegatus fish larvae: Endocrine disruption and toxicological responses

The inclusion of hazardous substances in the formulation of plastics raises significant concerns, particularly, if those substances are released as primary leachates during plastic degradation and/or fragmentation. In this sense, the production of degradable plastics holding deleterious additives can increase the release of harmful substances into the environment. Additionally, the effects of primary leachates of "eco-friendly" materials remain unexplored. To address this, we performed exposures to primary leachates of alternative polymers, and commercial bags to verify possible responses associated with endocrine disruption and/or activation of the detoxification pathway in larvae of the marine fish model Cyprinodon variegatus. The chemical characterization evidenced a great number of additives in the formulation of the materials analyzed in this study. Those include, except for the PLA sample, relevant levels of the hazardous phthalates DEHP and DiBP. Regarding the effects on marine fish larvae, exposure to leachates from alternative polymers (10 g/L) PHB and PHBV produced remarkable mortality (100%). While the exposure to bag leachates of all tested materials (1 and 10 g/L) produced alterations in biomarkers for steroidogenic and detoxification pathways. To a lesser extent (10 g/L), three materials produced significant alterations in estrogenic biomarkers (Home-compostable bag 1, LDPE and Recycled PE bags). Although the alterations in gene expression were not directly correlated to the amount of DEHP or DiBP, we can conclude that primary leachates of "eco-friendly" bags are harmful to marine vertebrates.

Exogenous methylglyoxal alleviates drought-induced 'plant diabetes' and leaf senescence in maize

Drought-induced leaf senescence is associated with high sugar levels, which bears some resemblance to the syndrome of diabetes in humans; however, the underlying mechanisms of such 'plant diabetes' on carbon imbalance and the corresponding detoxification strategy are not well understood. Here, we investigated the regulatory mechanism of exogenous methylglyoxal (MG) on 'plant diabetes' in maize plants under drought stress applied via foliar spraying during the grain-filling stage. Exogenous MG delayed leaf senescence and promoted photoassimilation, thereby reducing the yield loss induced by drought by 14%. Transcriptome and metabolite analyses revealed that drought increased sugar accumulation in leaves through inhibition of sugar transporters that facilitate phloem loading. This led to disequilibrium of glycolysis and overaccumulation of endogenous MG. Application of exogenous MG up-regulated glycolytic flux and the glyoxalase system that catabolyses endogenous MG and glycation end-products, ultimately alleviating 'plant diabetes'. In addition, the expression of genes facilitating anabolism and catabolism of trehalose-6-phosphate was promoted and suppressed by drought, respectively, and exogenous MG reversed this effect, implying that trehalose-6-phosphate signaling in the mediation of 'plant diabetes'. Furthermore, exogenous MG activated the phenylpropanoid biosynthetic pathway, promoting the production of lignin and phenolic compounds, which are associated with drought tolerance. Overall, our findings indicate that exogenous MG activates defense-related pathways to alleviate the toxicity derived from 'plant diabetes', thereby helping to maintain leaf function and yield production under drought.

scDrugPrio: a framework for the analysis of single-cell transcriptomics to address multiple problems in precision medicine in immune-mediated inflammatory diseases

BACKGROUND

Ineffective drug treatment is a major problem for many patients with immune-mediated inflammatory diseases (IMIDs). Important reasons are the lack of systematic solutions for drug prioritisation and repurposing based on characterisation of the complex and heterogeneous cellular and molecular changes in IMIDs.

METHODS

Here, we propose a computational framework, scDrugPrio, which constructs network models of inflammatory disease based on single-cell RNA sequencing (scRNA-seq) data. scDrugPrio constructs detailed network models of inflammatory diseases that integrate information on cell type-specific expression changes, altered cellular crosstalk and pharmacological properties for the selection and ranking of thousands of drugs.

RESULTS

scDrugPrio was developed using a mouse model of antigen-induced arthritis and validated by improved precision/recall for approved drugs, as well as extensive in vitro, in vivo, and in silico studies of drugs that were predicted, but not approved, for the studied diseases. Next, scDrugPrio was applied to multiple sclerosis, Crohn's disease, and psoriatic arthritis, further supporting scDrugPrio through prioritisation of relevant and approved drugs. However, in contrast to the mouse model of arthritis, great interindividual cellular and gene expression differences were found in patients with the same diagnosis. Such differences could explain why some patients did or did not respond to treatment. This explanation was supported by the application of scDrugPrio to scRNA-seq data from eleven individual Crohn's disease patients. The analysis showed great variations in drug predictions between patients, for example, assigning a high rank to anti-TNF treatment in a responder and a low rank in a nonresponder to that treatment.

CONCLUSIONS

We propose a computational framework, scDrugPrio, for drug prioritisation based on scRNA-seq of IMID disease. Application to individual patients indicates scDrugPrio's potential for personalised network-based drug screening on cellulome-, genome-, and drugome-wide scales. For this purpose, we made scDrugPrio into an easy-to-use R package ( https://github.com/SDTC-CPMed/scDrugPrio ).

Metabolomics-based study of chemical compositions in cellulase additives derived from a tobacco-origin Bacillus subtilis and their impact on tobacco sensory attributes

To enhance the quality of tobacco leaves and optimize the smoking experience, diverse strains of functional bacteria and their associated metabolites have been used in tobacco aging. Exogenous cellulase additives are frequently employed to facilitate the degradation of cellulose and other macromolecular matrices and enhance the quality of the tobacco product. However, little is known about how microbial metabolites present in exogenous enzyme additives affect tobacco quality. In this study, crude cellulase solutions, produced by a tobacco-originating bacterium Bacillus subtilis FX-1 were employed on flue-cured tobacco. The incorporation of cellulase solutions resulted in the reduction of cellulose crystallinity in tobacco and the enhancement of the overall sensory quality of tobacco. Notably, tobacco treated with cellulase obtained from laboratory flask fermentation demonstrated superior scent and flavor attributes in comparison to tobacco treated with enzymes derived from industrial bioreactor fermentation. The targeted and untargeted metabolomic analysis revealed the presence of diverse flavor-related precursors and components in the cellulase additives, encompassing sugars, alcohols, amino acids, organic acids, and others. The majority of these metabolites exhibited significantly higher levels in the flask group compared to the bioreactor group, probably contributing to a pronounced enhancement in the sensory quality of tobacco. Our findings suggest that the utilization of metabolic products derived from B. subtilis FX-1 as additives in flue-cured tobacco holds promise as a viable approach for enhancing sensory attributes, establishing a solid theoretical foundation for the potential development of innovative tobacco aging additives.

Transcriptomic profiling of the acute mucosal response to local food injections in adults with eosinophilic esophagitis

BACKGROUND

Exposure of the esophageal mucosa to food allergens can cause acute mucosal responses in patients with eosinophilic esophagitis (EoE), but the underlying local immune mechanisms driving these acute responses are not well understood.

OBJECTIVE

We sought to gain insight into the early transcriptomic changes that occur during an acute mucosal response to food allergens in EoE.

METHODS

Bulk RNA sequencing was performed on esophageal biopsy specimens from adult patients with EoE (n = 5) collected before and 20 minutes after intramucosal injection of various food extracts in the esophagus. Baseline biopsy specimens from control subjects without EoE (n = 5) were also included.

RESULTS

At baseline, the transcriptome of the patients with EoE showed increased expression of genes related to an EoE signature. After local food injection, we identified 40 genes with a potential role in the early immune response to food allergens (most notably CEBPB, IL1B, TNFSF18, PHLDA2, and SLC15A3). These 40 genes were enriched in processes related to immune activation, such as the acute-phase response, cellular responses to external stimuli, and cell population proliferation. TNFSF18 (also called GITRL), a member of the TNF superfamily that is best studied for its costimulatory effect on T cells, was the most dysregulated early EoE gene, showing a 12-fold increase compared with baseline and an 18-fold increase compared with a negative visual response. Further experiments showed that the esophageal epithelium may be an important source of TNFSF18 in EoE, which was rapidly induced by costimulating esophageal epithelial cells with the EoE-relevant cytokines IL-13 and TNF-α.

CONCLUSIONS

Our data provide unprecedented insight into the transcriptomic changes that mediate the acute mucosal immune response to food allergens in EoE and suggest that TNFSF18 may be an important effector molecule in this response.

In vitro inflammatory multi-cellular model of osteoarthritis

Objective

Osteoarthritis (OA) is a chronic joint disease, with limited treatment options, characterized by inflammation and matrix degradation, and resulting in severe pain or disability. Progressive inflammatory interaction among key cell types, including chondrocytes and macrophages, leads to a cascade of intra- and inter-cellular events which culminate in OA induction. In order to investigate these interactions, we developed a multi-cellular in vitro OA model, to characterize OA progression, and identify and evaluate potential OA therapeutics in response to mediators representing graded levels of inflammatory severity.

Methods

We compared macrophages, chondrocytes and their co-culture responses to "low" Interleukin-1 (IL-1) or "high" IL-1/tumor necrosis factor (IL-1/TNF) levels of inflammation. We also investigated response changes following the administration of dexamethasone (DEX) or mesenchymal stromal cell (MSC) treatment via a combination of gene expression and secretory changes, reflecting not only inflammation, but also chondrocyte function.

Results

Inflamed chondrocytes presented an osteoarthritic-like phenotype characterized by high gene expression of pro-inflammatory cytokines and chemokines, up-regulation of ECM degrading proteases, and down-regulation of chondrogenic genes. Our results indicate that while MSC treatment attenuates macrophage inflammation directly, it does not reduce chondrocyte inflammatory responses, unless macrophages are present as well. DEX however, can directly attenuate chondrocyte inflammation.

Conclusions

Our results highlight the importance of considering multi-cellular interactions when studying complex systems such as the articular joint. In addition, our approach, using a panel of both inflammatory and chondrocyte functional genes, provides a more comprehensive approach to investigate disease biomarkers, and responses to treatment.

Enabling personalised disease diagnosis by combining a patient's time-specific gene expression profile with a biomedical knowledge base

BACKGROUND

Recent developments in the domain of biomedical knowledge bases (KBs) open up new ways to exploit biomedical knowledge that is available in the form of KBs. Significant work has been done in the direction of biomedical KB creation and KB completion, specifically, those having gene-disease associations and other related entities. However, the use of such biomedical KBs in combination with patients' temporal clinical data still largely remains unexplored, but has the potential to immensely benefit medical diagnostic decision support systems.

RESULTS

We propose two new algorithms, LOADDx and SCADDx, to combine a patient's gene expression data with gene-disease association and other related information available in the form of a KB, to assist personalized disease diagnosis. We have tested both of the algorithms on two KBs and on four real-world gene expression datasets of respiratory viral infection caused by Influenza-like viruses of 19 subtypes. We also compare the performance of proposed algorithms with that of five existing state-of-the-art machine learning algorithms (k-NN, Random Forest, XGBoost, Linear SVM, and SVM with RBF Kernel) using two validation approaches: LOOCV and a single internal validation set. Both SCADDx and LOADDx outperform the existing algorithms when evaluated with both validation approaches. SCADDx is able to detect infections with up to 100% accuracy in the cases of Datasets 2 and 3. Overall, SCADDx and LOADDx are able to detect an infection within 72 h of infection with 91.38% and 92.66% average accuracy respectively considering all four datasets, whereas XGBoost, which performed best among the existing machine learning algorithms, can detect the infection with only 86.43% accuracy on an average.

CONCLUSIONS

We demonstrate how our novel idea of using the most and least differentially expressed genes in combination with a KB can enable identification of the diseases that a patient is most likely to have at a particular time, from a KB with thousands of diseases. Moreover, the proposed algorithms can provide a short ranked list of the most likely diseases for each patient along with their most affected genes, and other entities linked with them in the KB, which can support health care professionals in their decision-making.

Transcriptome and machine learning analysis of the impact of COVID-19 on mitochondria and multiorgan damage

The effects of coronavirus disease 2019 (COVID-19) primarily concern the respiratory tract and lungs; however, studies have shown that all organs are susceptible to infection by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). COVID-19 may involve multiorgan damage from direct viral invasion through angiotensin-converting enzyme 2 (ACE2), through inflammatory cytokine storms, or through other secondary pathways. This study involved the analysis of publicly accessible transcriptome data from the Gene Expression Omnibus (GEO) database for identifying significant differentially expressed genes related to COVID-19 and an investigation relating to the pathways associated with mitochondrial, cardiac, hepatic, and renal toxicity in COVID-19. Significant differentially expressed genes were identified and ranked by statistical approaches, and the genes derived by biological meaning were ranked by feature importance; both were utilized as machine learning features for verification. Sample set selection for machine learning was based on the performance, sample size, imbalanced data state, and overfitting assessment. Machine learning served as a verification tool by facilitating the testing of biological hypotheses by incorporating gene list adjustment. A subsequent in-depth study for gene and pathway network analysis was conducted to explore whether COVID-19 is associated with cardiac, hepatic, and renal impairments via mitochondrial infection. The analysis showed that potential cardiac, hepatic, and renal impairments in COVID-19 are associated with ACE2, inflammatory cytokine storms, and mitochondrial pathways, suggesting potential medical interventions for COVID-19-induced multiorgan damage.

Reduction of SARS-CoV-2 viral load in saliva after rinsing with mouthwashes containing cetylpyridinium chloride: a randomized clinical study

Background

Symptomatic patients with COVID-19 typically have a high SARS-CoV-2 viral load in their saliva. Procedures to reduce the viral load in their oral cavity are important for mitigating the viral transmission.

Methods

This randomized clinical trial investigated the impact of two mouthwashes (0.075% cetylpyridinium chloride plus 0.28% zinc lactate (CPC+Zn) (n = 32), and 0.075% cetylpyridinium chloride (CPC) (n = 31)) on the viral load of SARS-CoV-2 in saliva when compared to the distilled water negative control (n = 32). Saliva was collected before (T0) and after (5 min, T1; 30 min, T2; and 60 min, T3) the intervention. Viral load in saliva was measured by qRT-PCR assays. The data in both groups was normalized for T0 and Negative Control, resulting in fold change values.

Results

CPC+Zn oral solution reduced the viral load in saliva by 6.34-fold at T1, 3.6-fold at T2 and 1.9-fold at T3. Rinsing with the CPC mouthwash reduced the viral load in saliva by 2.5-fold at T1, 1.9-fold at T2 and 2.0-fold at T3.

Conclusion

CPC+Zn mouthwash or with the CPC mouthwash reduced the viral load in saliva of COVID-19 patients immediately after rinsing. These reductions extended up to 60 min.

scDrugPrio: A framework for the analysis of single-cell transcriptomics to address multiple problems in precision medicine in immune-mediated inflammatory diseases

Background

Ineffective drug treatment is a major problem for many patients with immune-mediated inflammatory diseases (IMIDs). Important reasons are the lack of systematic solutions for drug prioritisation and repurposing based on characterisation of the complex and heterogeneous cellular and molecular changes in IMIDs.

Methods

Here, we propose a computational framework, scDrugPrio, which constructs network models of inflammatory disease based on single-cell RNA sequencing (scRNA-seq) data. scDrugPrio constructs detailed network models of inflammatory diseases that integrate information on cell type-specific expression changes, altered cellular crosstalk and pharmacological properties for the selection and ranking of thousands of drugs.

Results

scDrugPrio was developed using a mouse model of antigen-induced arthritis and validated by improved precision/recall for approved drugs, as well as extensive in vitro, in vivo, and in silico studies of drugs that were predicted, but not approved, for the studied diseases. Next, scDrugPrio was applied to multiple sclerosis, Crohn's disease, and psoriatic arthritis, further supporting scDrugPrio through prioritisation of relevant and approved drugs. However, in contrast to the mouse model of arthritis, great interindividual cellular and gene expression differences were found in patients with the same diagnosis. Such differences could explain why some patients did or did not respond to treatment. This explanation was supported by the application of scDrugPrio to scRNA-seq data from eleven individual Crohn's disease patients. The analysis showed great variations in drug predictions between patients, for example, assigning a high rank to anti-TNF treatment in a responder and a low rank in a nonresponder to that treatment.

Conclusion

We propose a computational framework, scDrugPrio, for drug prioritisation based on scRNA-seq of IMID disease. Application to individual patients indicates scDrugPrio's potential for personalised network-based drug screening on cellulome-, genome-, and drugome-wide scales. For this purpose, we made scDrugPrio into an easy-to-use R package (https://github.com/SDTC-CPMed/scDrugPrio).

A novel f-divergence based generative adversarial imputation method for scRNA-seq data analysis

Comprehensive analysis of single-cell RNA sequencing (scRNA-seq) data can enhance our understanding of cellular diversity and aid in the development of personalized therapies for individuals. The abundance of missing values, known as dropouts, makes the analysis of scRNA-seq data a challenging task. Most traditional methods made assumptions about specific distributions for missing values, which limit their capability to capture the intricacy of high-dimensional scRNA-seq data. Moreover, the imputation performance of traditional methods decreases with higher missing rates. We propose a novel f-divergence based generative adversarial imputation method, called sc-fGAIN, for the scRNA-seq data imputation. Our studies identify four f-divergence functions, namely cross-entropy, Kullback-Leibler (KL), reverse KL, and Jensen-Shannon, that can be effectively integrated with the generative adversarial imputation network to generate imputed values without any assumptions, and mathematically prove that the distribution of imputed data using sc-fGAIN algorithm is same as the distribution of original data. Real scRNA-seq data analysis has shown that, compared to many traditional methods, the imputed values generated by sc-fGAIN algorithm have a smaller root-mean-square error, and it is robust to varying missing rates, moreover, it can reduce imputation variability. The flexibility offered by the f-divergence allows the sc-fGAIN method to accommodate various types of data, making it a more universal approach for imputing missing values of scRNA-seq data.

Transcriptomic characterization of Trichoderma harzianum T34 primed tomato plants: assessment of biocontrol agent induced host specific gene expression and plant growth promotion

In this study, we investigated the intricate interplay between Trichoderma and the tomato genome, focusing on the transcriptional and metabolic changes triggered during the late colonization event. Microarray probe set (GSE76332) was utilized to analyze the gene expression profiles changes of the un-inoculated control (tomato) and Trichoderma-tomato interactions for identification of the differentially expressed significant genes. Based on principal component analysis and R-based correlation, we observed a positive correlation between the two cross-comaparable groups, corroborating the existence of transcriptional responses in the host triggered by Trichoderma priming. The statistically significant genes based on different p-value cut-off scores [(padj-values or q-value); padj-value < 0.05], [(pcal-values); pcal-value < 0.05; pcal < 0.01; pcal < 0.001)] were cross compared. Through cross-comparison, we identified 156 common genes that were consistently significant across all probability thresholds, and showing a strong positive corelation between p-value and q-value in the selected probe sets. We reported TD2, CPT1, pectin synthase, EXT-3 (extensin-3), Lox C, and pyruvate kinase (PK), which exhibited upregulated expression, and Glb1 and nitrate reductase (nii), which demonstrated downregulated expression during Trichoderma-tomato interaction. In addition, microbial priming with Trichoderma resulted into differential expression of transcription factors related to systemic defense and flowering including MYB13, MYB78, ERF2, ERF3, ERF5, ERF-1B, NAC, MADS box, ZF3, ZAT10, A20/AN1, polyol sugar transporter like zinc finger proteins, and a novel plant defensin protein. The potential bottleneck and hub genes involved in this dynamic response were also identified. The protein-protein interaction (PPI) network analysis based on 25 topmost DEGS (pcal-value < 0.05) and the Weighted Correlation Gene Network Analysis (WGCNA) of the 1786 significant DEGs (pcal-value < 0.05) we reported the hits associated with carbohydrate metabolism, secondary metabolite biosynthesis, and the nitrogen metabolism. We conclude that the Trichoderma-induced microbial priming re-programmed the host genome for transcriptional response during the late colonization event and were characterized by metabolic shifting and biochemical changes specific to plant growth and development. The work also highlights the relevance of statistical parameters in understanding the gene regulatory dynamics and complex regulatory networks based on differential expression, co-expression, and protein interaction networks orchestrating the host responses to beneficial microbial interactions.

Epigenome-wide studies of antipsychotics: a systematic review and pathway meta-analysis

Background & methods: Researchers have aimed to understand the mechanisms of antipsychotics through epigenetics to inform interindividual response rates. However, findings have widely varied across studies, making advancement in the field difficult. Materials & methods: A systematic review was performed to include all epigenome-wide studies of antipsychotic treatment in humans. Methylation sites were used for a pathway and enrichment map analysis was conducted. Results & conclusion: Seven studies were included and 82 methylation sites were used for the exploratory pathway meta-analysis that identified six pathway clusters. The findings here demonstrate that studies of the epigenome and antipsychotic treatment are highly heterogeneous in nature and could inform future work to target cross-cutting gene sets and pathways.

Screening for chemicals with thyroid hormone-disrupting effects using zebrafish embryo

Thyroid disruption is an increasingly recognized issue in the use and development of chemicals and new drugs, especially to help toxicologist to complement the reproductive and developmental toxicology information of chemicals. Still, adequate assessment methods are scarce and often suffer a trade-off between physiological relevance and labor- and cost-intensive assays. Here, we present a tiered approach for a medium-throughput screening of chemicals to identify their thyroid disrupting potential in zebrafish embryos as a New Approach Methodology (NAM). After identifying the maximum tolerated concentrations, we exposed zebrafish larvae to sub-adverse effect levels of the reference compounds benzophenone-2, bisphenol A, phenylthiourea, potassium perchlorate, propylthiouracil, and phloroglucinol to exclude any systemic toxicity. Applying the transgenic zebrafish line that carries a gene for the red fluorescence protein (Tg(tg:mCherry)) under the thyroglobulin promoter, we could identify the thyroid disrupting effects of the chemicals by a time and cost-effective image analysis measuring the fluorescence levels in the thyroid glands. Our observations could be confirmed by altered expression patterns of genes involved in the hypothalamus-pituitary-thyroid (HPT) axis. Finally, to anchor the observed thyroid disruption, we determined some changes in the Thyroid hormone levels of triiodothyronine (T3) and Thyroxine (T4) using a newly developed liquid chromatography mass spectrometric (LCMS) method. The presented approach carries the potential to extend the toolbox for legislative authorities and chemical producers for the assessment of thyroid-specific endocrine disruption and to overcome current challenges in the evaluation of endocrine disruptors.

A novel f -divergence based generative adversarial imputation method for scRNA-seq data analysis

Comprehensive analysis of single-cell RNA sequencing (scRNA-seq) data can enhance our understanding of cellular diversity and aid in the development of personalized therapies for individuals. The abundance of missing values, known as dropouts, makes the analysis of scRNA-seq data a challenging task. Most traditional methods made assumptions about specific distributions for missing values, which limit their capability to capture the intricacy of high-dimensional scRNA-seq data. Moreover, the imputation performance of traditional methods decreases with higher missing rates. We propose a novel f -divergence based generative adversarial imputation method, called sc- f GAIN, for the scRNA-seq data imputation. Our studies identify four f -divergence functions, namely cross-entropy, Kullback-Leibler (KL), reverse KL, and Jensen-Shannon, that can be effectively integrated with the generative adversarial imputation network to generate imputed values without any assumptions, and mathematically prove that the distribution of imputed data using sc- f GAIN algorithm is same as the distribution of original data. Real scRNA-seq data analysis has shown that, compared to many traditional methods, the imputed values generated by sc- f GAIN algorithm have a smaller root-mean-square error, and it is robust to varying missing rates, moreover, it can reduce imputation bias. The flexibility offered by the f -divergence allows the sc- f GAIN method to accommodate various types of data, making it a more universal approach for imputing missing values of scRNA-seq data.

Inflammatory Gene Profile and Particle Presence in Peri-Implant Mucosa: a Pilot Study on 9 Patients

Objectives

The purpose of this pilot study is to compare gene expression in mucosa around dental implants with zirconia abutment to titanium and investigate presence of particles in mucosa samples and on implant heads.

Material and Methods

Ten patients with a single implant supported prosthesis connected to zirconia or titanium abutments were invited at the five-year control. A clinical examination and a survey on experience of function and appearance were conducted. A mucosa biopsy taken in close vicinity to the implant were analysed by real-time polymerase chain reaction (qPCR) and presence of particles in a scanning electron microscope/energy-dispersive X-ray spectroscope (SEM/EDX). Cytological smear samples were collected and analysed through inductively coupled plasma mass spectrometry (ICP-MS) to investigate presence of particles on implant heads.

Results

In total, 9 patients participated in the study, five with titanium abutments and four with zirconia abutments. All patients were satisfied with function and aesthetics. Titanium and iron particles were detected in mucosa biopsies. The ICP - MS analysis demonstrated presence of zirconia and titanium. Several proinflammatory genes were upregulated in the zirconia abutment group.

Conclusions

Around zirconia abutments a slight increase in proinflammatory response and amount of wear particles was seen as compared to titanium. Wear particles of titanium were present in all soft tissue samples, however zirconia particles only in the samples from implants heads/mucosa with zirconia abutments.

CCND1 Overexpression in Idiopathic Dilated Cardiomyopathy: A Promising Biomarker?

Cardiomyopathy, a disorder of electrical or heart muscle function, represents a type of cardiac muscle failure and culminates in severe heart conditions. The prevalence of dilated cardiomyopathy (DCM) is higher than that of other types (hypertrophic cardiomyopathy and restrictive cardiomyopathy) and causes many deaths. Idiopathic dilated cardiomyopathy (IDCM) is a type of DCM with an unknown underlying cause. This study aims to analyze the gene network of IDCM patients to identify disease biomarkers. Data were first extracted from the Gene Expression Omnibus (GEO) dataset and normalized based on the RMA algorithm (Bioconductor package), and differentially expressed genes were identified. The gene network was mapped on the STRING website, and the data were transferred to Cytoscape software to determine the top 100 genes. In the following, several genes, including VEGFA, IGF1, APP, STAT1, CCND1, MYH10, and MYH11, were selected for clinical studies. Peripheral blood samples were taken from 14 identified IDCM patients and 14 controls. The RT-PCR results revealed no significant differences in the expression of the genes APP, MYH10, and MYH11 between the two groups. By contrast, the STAT1, IGF1, CCND1, and VEGFA genes were overexpressed in patients more than in controls. The highest expression was found for VEGFA, followed by CCND1 (p < 0.001). Overexpression of these genes may contribute to disease progression in patients with IDCM. However, more patients and genes need to be analyzed in order to achieve more robust results.

Methodology to identify a gene expression signature by merging microarray datasets

A vast number of microarray datasets have been produced as a way to identify differentially expressed genes and gene expression signatures. A better understanding of these biological processes can help in the diagnosis and prognosis of diseases, as well as in the therapeutic response to drugs. However, most of the available datasets are composed of a reduced number of samples, leading to low statistical, predictive and generalization power. One way to overcome this problem is by merging several microarray datasets into a single dataset, which is typically a challenging task. Statistical methods or supervised machine learning algorithms are usually used to determine gene expression signatures. Nevertheless, statistical methods require an arbitrary threshold to be defined, and supervised machine learning methods can be ineffective when applied to high-dimensional datasets like microarrays. We propose a methodology to identify gene expression signatures by merging microarray datasets. This methodology uses statistical methods to obtain several sets of differentially expressed genes and uses supervised machine learning algorithms to select the gene expression signature. This methodology was validated using two distinct research applications: one using heart failure and the other using autism spectrum disorder microarray datasets. For the first, we obtained a gene expression signature composed of 117 genes, with a classification accuracy of approximately 98%. For the second use case, we obtained a gene expression signature composed of 79 genes, with a classification accuracy of approximately 82%. This methodology was implemented in R language and is available, under the MIT licence, at https://github.com/bioinformatics-ua/MicroGES.

Differentially expressed genes in systemic sclerosis: Towards predictive medicine with new molecular tools for clinicians

Systemic sclerosis (SSc) is a rare and chronic autoimmune disease characterized by a pathogenic triad of immune dysregulation, vasculopathy, and progressive fibrosis. Clinical tools commonly used to assess patients, such as the modified Rodnan skin score, difference between limited or diffuse forms of skin involvement, presence of lung, heart or kidney involvement, or of various autoantibodies, are important prognostic factors, but still fail to reflect the large heterogeneity of the disease. SSc treatment options are diverse, ranging from conventional drugs to autologous hematopoietic stem cell transplantation, and predicting response is challenging. Genome-wide technologies, such as high throughput microarray analyses and RNA sequencing, allow accurate, unbiased, and broad assessment of alterations in expression levels of multiple genes. In recent years, many studies have shown robust changes in the gene expression profiles of SSc patients compared to healthy controls, mainly in skin tissues and peripheral blood cells. The objective analysis of molecular patterns in SSc is a powerful tool that can further classify SSc patients with similar clinical phenotypes and help predict response to therapy. In this review, we describe the journey from the first discovery of differentially expressed genes to the identification of enriched pathways and intrinsic subsets identified in SSc, using machine learning algorithms. Finally, we discuss the use of these new tools to predict the efficacy of various treatments, including stem cell transplantation. We suggest that the use of RNA gene expression-based classifications according to molecular subsets may bring us one step closer to precision medicine in Systemic Sclerosis.

Symbiont-specific responses to environmental cues in a threesome lichen symbiosis

Photosymbiodemes are a special case of lichen symbiosis where one lichenized fungus engages in symbiosis with two different photosynthetic partners, a cyanobacterium and a green alga, to develop two distinctly looking photomorphs. We compared gene expression of thallus sectors of the photosymbiodeme-forming lichen Peltigera britannica containing cyanobacterial photobionts with thallus sectors with both green algal and cyanobacterial photobionts and investigated differential gene expression at different temperatures representing mild and putatively stressful conditions. First, we quantified photobiont-mediated differences in fungal gene expression. Second, because of known ecological differences between photomorphs, we investigated symbiont-specific responses in gene expression to temperature increases. Photobiont-mediated differences in fungal gene expression could be identified, with upregulation of distinct biological processes in the different morphs, showing that interaction with specific symbiosis partners profoundly impacts fungal gene expression. Furthermore, high temperatures expectedly led to an upregulation of genes involved in heat shock responses in all organisms in whole transcriptome data and to an increased expression of genes involved in photosynthesis in both photobiont types at 15 and 25°C. The fungus and the cyanobacteria exhibited thermal stress responses already at 15°C, the green algae mainly at 25°C, demonstrating symbiont-specific responses to environmental cues and symbiont-specific ecological optima.

How does SARS-CoV-2 infection impact on immunity, procession and treatment of pan cancers

We identified 14 immune-related differentially Expressed Genes (DEGs) between COVID-19 patients and normal controls and the receiver operator characteristic curve results showed that they could be used to discriminate COVID-19 patients from healthy controls. Single-sample gene set enrichment analysis and CIBERSORT analysis displayed immune landscape of COVID-19 patients that the fraction of immune cells (like B cell subtypes and T cell subtypes) decreased distinctly in the first SARS-CoV-2 infection which may further weaken immunity of cancer patients and increasing inflammatory cells (Neutrophils and Macrophages) may further promote inflammatory response of cancer patients. Based on expression levels of 14 DEGs we found that first SARS-CoV-2 infection may accelerate progression of cancer patients by Kaplan-Meier survival, immune subtypes and tumor microenvironment analyses, and may weaken anti-PD-1 monoclonal antibody treatment effect of cancer patients by weighted gene co-expression network, tumor mutation burden and microsatellite instability analysis. The second SARS-CoV-2 infection was beneficial to control development of tumor seemingly, but it may be difficult for cancer patients to experience destroy successfully from first SARS-CoV-2 infection, let alone benefits from second SARS-CoV-2 infection. In addition, this study also emphasized significance of multi-factor analysis when analyzing impacts of SARS-CoV-2 infection on cancer patients.

Single-cell profile of tumor and immune cells in primary breast cancer, sentinel lymph node, and metastatic lymph node

PURPOSE

Little is known about the host-tumor interaction in the lymph-node basin at a single cell level. This study examines single cell sequences in breast cancer nodal metastases of a patient with triple-negative breast cancer.

METHODS

The primary breast tumor, sentinel lymph node, an adjacent lymph node with metastatic involvement and a clinically normal-appearing lymph node were collected during surgery. Single-cell sequencing was performed on all four specimens.

RESULTS

14,016 cells were clustered into 6 cell subpopulations. Cancer cells demonstrated the molecular characteristics of TNBC basal B subtype and highly expressed genes in the MAPK signaling cascade. Tumor-associated macrophages regulated antigen processing and presentation and other immune-related pathways to promote tumor invasion. CD8 + and CD4 + T lymphocytes concentrated more in sentinel lymph node and mainly stratified into two transcriptional states. The immune-cell amount variation among primary tumor, sentinel and normal lymph nodes showed a similar tendency between the sc-RNA-seq profile of TNBC samples and a previous reported bulk RNA-seq profile of a breast cancer cohort, including all four breast cancer subtype samples.

DISCUSSION

Single-cell sequencing analysis suggested that the sentinel lymph node was the initial meeting site of tumor infiltration and immune response, where partial T lymphocytes perform anti-tumor activity, while other T cells exhibit an exhausted state. We proposed a molecular explanation to the well-established clinical principle that the 5-year and 10-year survival outcomes were noninferior between SLND and ALND.

Fixed Orthodontic Treatment Increases Cariogenicity and Virulence Gene Expression in Dental Biofilm

Background: Dental caries commonly occurs during orthodontic treatment because fixed appliances can impede effective oral hygiene practices. This study investigated the effects of fixed orthodontic treatment on dental biofilm maturity and virulence gene (gtfB, ldh, brpA, spaP, luxS, and gbpB) expression. Methods: Dental biofilms and virulence gene expression were determined in 24 orthodontic patients before and after treatment of ≥6 months. A three-tone disclosing gel was used to stain dental biofilm and assess its maturity by its color change—pink (new dental biofilm), purple (mature dental biofilm), and light blue (cariogenic dental biofilm). Gene expression levels were determined using real-time PCR. Results: After fixed orthodontic appliance insertion, the percentage of new dental biofilm decreased, whereas that of cariogenic dental biofilm significantly increased (p < 0.05). There was no significant difference in the percentage of mature dental biofilm (p > 0.05). Fixed orthodontic appliances increased gtfB, ldh, brpA, and gbpB gene expression above 1.5-fold in dental biofilm. In contrast, there was no change in spaP or luxS gene expression after treatment. Conclusions: Fixed orthodontic appliance insertion induced ecological changes and cariogenic virulence gene expression in dental biofilm.

Comprehensive Computational Analysis of Honokiol Targets for Cell Cycle Inhibition and Immunotherapy in Metastatic Breast Cancer Stem Cells

Breast cancer stem cells (BCSCs) play a critical role in chemoresistance, metastasis, and poor prognosis of breast cancer. BCSCs are mostly dormant, and therefore, activating them and modulating the cell cycle are important for successful therapy against BCSCs. The tumor microenvironment (TME) promotes BCSC survival and cancer progression, and targeting the TME can aid in successful immunotherapy. Honokiol (HNK), a bioactive polyphenol isolated from the bark and seed pods of Magnolia spp., is known to exert anticancer effects, such as inducing cell cycle arrest, inhibiting metastasis, and overcoming immunotherapy resistance in breast cancer cells. However, the molecular mechanisms of action of HNK in BCSCs, as well as its effects on the cell cycle, remain unclear. This study aimed to explore the potential targets and molecular mechanisms of HNK on metastatic BCSC (mBCSC)-cell cycle arrest and the impact of the TME. Using bioinformatics analyses, we predicted HNK protein targets from several databases and retrieved the genes differentially expressed in mBCSCs from the GEO database. The intersection between the differentially expressed genes (DEGs) and the HNK-targets was determined using a Venn diagram, and the results were analyzed using a protein-protein interaction network, hub gene selection, gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses, genetic alteration analysis, survival rate, and immune cell infiltration levels. Finally, the interaction between HNK and two HNK-targets regulating the cell cycle was analyzed using molecular docking analysis. The identified potential therapeutic targets of HNK (PTTH) included CCND1, SIRT2, AURKB, VEGFA, HDAC1, CASP9, HSP90AA1, and HSP90AB1, which can potentially inhibit the cell cycle of mBCSCs. Moreover, our results showed that PTTH could modulate the PI3K/Akt/mTOR and HIF1/NFkB/pathways. Overall, these findings highlight the potential of HNK as an immunotherapeutic agent for mBCSCs by modulating the tumor immune environment.

Differential and divergent activity of insulin‑like growth factor binding protein 6 in platinum‑sensitive versus platinum‑resistant high‑grade serous ovarian carcinoma cell lines

Insulin-like growth factor binding protein 6 (IGFBP6) is a secreted protein with a controversial role in human malignancies, being downregulated in most types of human cancer, but upregulated in selected tumors. Ovarian cancer (OC) is a human malignancy characterized by IGFBP6 downregulation; however, the significance of its low expression during ovarian carcinogenesis is still poorly understood. In the present study, IGFBP6 expression and activation of its associated signaling pathway were evaluated in two matched OC cell lines derived from a high-grade serous OC before and after platinum resistance (PEA1 and PEA2 cells, respectively). A whole genome gene expression analysis was comparatively performed in both cell lines upon IGFBP6 stimulation using Illumina technology. IGFBP6 gene expression data from human OC cases were obtained from public datasets. Gene expression data from public datasets confirmed the downregulation of IGFBP6 in primary and metastatic OC tissues compared with in normal ovarian tissues. The comparative analysis of platinum-sensitive (PEA1) and platinum-resistant (PEA2) cell lines showed quantitative and qualitative differences in the activation of IGFBP6 signaling. Notably, IGFBP6 enhanced ERK1/2 phosphorylation only in PEA1 cells, and induced more evident and significant gene expression reprogramming in PEA1 cells compared with in PEA2 cells. Furthermore, the analysis of selected genes modulated by IGFBP6 (i.e., FOS, JUN, TNF, IL6, IL8 and EGR1) exhibited an inverse regulation in PEA1 versus PEA2 cells. In addition, selected hallmarks (TNFA_signaling_via_NFKB, TGF_beta_signaling, P53_pathway) and IL-6 signaling were positively regulated in PEA1 cells, whereas they were inhibited in PEA2 cells in response to IGFBP6. These data suggested that dysregulation of IGFBP6 signaling may serve a role in the progression of OC, and is likely associated with the development of platinum resistance.

Molecular Biomarker of Drug Resistance Developed From Patient-Derived Organoids Predicts Survival of Colorectal Cancer Patients

The drug 5-fluorouracil (5-Fu) is the critical composition of colorectal cancer (CRC) treatments. Prognostic and predictive molecular biomarkers for CRC patients (CRCpts) treated with 5-Fu-based chemotherapy can provide assistance for tailoring treatment approach. Here, we established a molecular biomarker of 5-Fu resistance derived from colorectal cancer organoids (CRCOs) for predicting the survival of CRCpts. Forty-one CRCO cultures were generated from 50 CRC tumor tissues after surgery (82%). The following experiments revealed a great diversity in drug sensitivity for 10 μM 5-Fu treatment tested by using organoid size change. Fourteen cases (34.1%) were 5-Fu sensitive and the other 27 (65.9%) were resistant. Then, differentially expressed genes (DEGs) associated with 5-Fu resistance were outputted by transcriptome sequencing. In particular, DEGs were generated in two comparison groups: 1) 5-Fu sensitive and resistant untreated CRCOs; 2) CRCOs before 5-Fu treatment and surviving CRCOs after 5-Fu treatment. Some molecules and most of the pathways that have been reported to be involved in 5-Fu resistance were identified in the current research. By using DEGs correlated with 5-Fu resistance and survival of CRCpts, the gene signature and drug-resistant score model (DRSM) containing five molecules were established in The Cancer Genome Atlas (TCGA)-CRC cohort by least absolute shrinkage and selection operator (LASSO) regression analysis and 5-fold cross-validation. Multivariate analysis revealed that drug-resistant score (DRS) was an independent prognostic factor for overall survival (OS) in CRCpts in TCGA-CRC cohort (P < 0.001). Further validation results from four Gene Expression Omnibus (GEO) cohorts elucidated that the DRSM based on five genes related to 5-Fu chemosensitivity and developed from patient-derived organoids can predict survival of CRCpts. Meanwhile, our model could predict the survival of CRCpts in different subgroups. Besides, the difference of molecular pathways, tumor mutational burden (TMB), immune response-related pathways, immune score, stromal score, and immune cell proportion were dissected between DRS-high and DRS-low patients in TCGA-CRC cohort.

Attractor-Specific and Common Expression Values in Random Boolean Network Models (with a Preliminary Look at Single-Cell Data)

Random Boolean Networks (RBNs for short) are strongly simplified models of gene regulatory networks (GRNs), which have also been widely studied as abstract models of complex systems and have been used to simulate different phenomena. We define the “common sea” (CS) as the set of nodes that take the same value in all the attractors of a given network realization, and the “specific part” (SP) as the set of all the other nodes, and we study their properties in different ensembles, generated with different parameter values. Both the CS and of the SP can be composed of one or more weakly connected components, which are emergent intermediate-level structures. We show that the study of these sets provides very important information about the behavior of the model. The distribution of distances between attractors is also examined. Moreover, we show how the notion of a “common sea” of genes can be used to analyze data from single-cell experiments.

The collagenase of the bacterium Clostridium histolyticum does not favor metastasis of breast cancer

BACKGROUND

Breast cancer is the most common malignancy among women worldwide. As survival rates increase, breast reconstruction and quality of life gain importance. Of all women undergoing breast reconstruction, approximately, 70% opt for silicone implants and 50% of those develop capsular contracture, the most prevalent long-term complication. The collagenase of the bacterium Clostridium histolyticum (CCH) showed promising results in the therapy of capsule contracture; however, its influence on residual cancer cells is unknown. The aim of this study was to investigate whether CCH-treatment negatively impacts breast cancer cells in vitro and in vivo.

METHODS

MDA-MB-231 and MCF-7 cells were used in this study. In vitro, we tested the influence of CCH on proliferation, wound healing, migration and cell cycle by MTT-assay, scratch-assay, transwell-migration-assay, and flow cytometry. In vivo, solid tumors were induced in immune-deficient mice. CCH was injected into the tumors and tumor growth and metastasis formation was monitored by caliper measurement, in vivo bioluminescence imaging and histology. Gene expression analysis was performed by microarray including 27,190 genes.

RESULTS

CCH-incubation led to a dose-dependent reduction in proliferation for both cell lines, while wound healing was reduced only in MDA-MB-231 cells. No morphological alterations were monitored in cell cycle or apoptosis. In vivo, bioluminescence imaging and histology did not show any evidence of metastasis. Although CCH led to changes in gene expression of breast cancer cells, no relevant alterations in metastasis-related genes were monitored.

CONCLUSION

CCH has no impact on tumor growth or metastasis formation in vitro and in vivo. This paves the way for first clinical trials.

OUP accepted manuscript

Preeclampsia is a pregnancy-specific disease that can have serious effects on the health of both mothers and their offspring. Predicting which women will develop preeclampsia in early pregnancy with high accuracy will allow for improved management. The clinical symptoms of preeclampsia are well recognized, however, the precise molecular mechanisms leading to the disorder are poorly understood. This is compounded by the heterogeneous nature of preeclampsia onset, timing and severity. Indeed a multitude of poorly defined causes including genetic components implicates etiologic factors, such as immune maladaptation, placental ischemia and increased oxidative stress. Large datasets generated by microarray and next-generation sequencing have enabled the comprehensive study of preeclampsia at the molecular level. However, computational approaches to simultaneously analyze the preeclampsia transcriptomic and network data and identify clinically relevant information are currently limited. In this paper, we proposed a control theory method to identify potential preeclampsia-associated genes based on both transcriptomic and network data. First, we built a preeclampsia gene regulatory network and analyzed its controllability. We then defined two types of critical preeclampsia-associated genes that play important roles in the constructed preeclampsia-specific network. Benchmarking against differential expression, betweenness centrality and hub analysis we demonstrated that the proposed method may offer novel insights compared with other standard approaches. Next, we investigated subtype specific genes for early and late onset preeclampsia. This control theory approach could contribute to a further understanding of the molecular mechanisms contributing to preeclampsia.

Clinical significance of FAT1 gene mutation and mRNA expression in patients with head and neck squamous cell carcinoma

The FAT1 gene functions as a tumor suppressor or promoter and remains incompletely understood. We examined the clinical significance of FAT1 in head and neck squamous cell carcinoma (HNSCC) using four publicly available HNSCC cohorts and one HNSCC cohort enrolled at a tertiary medical center. We developed FAT1 signatures reflecting FAT1 mutations and mRNA expression using one cohort. Patients with HNSCC were classified into FAT1‐associated low risk (FAT1‐LR; n = 195) and FAT1‐associated high risk (FAT1‐HR; n = 371) subgroups. The five‐year overall survival and recurrence‐free survival rates were significantly lower in the FAT1‐HR subgroup than in the FAT1‐LR subgroup (P = 0.01 and 0.003, respectively). The clinical significance of FAT1 was validated using four independent cohorts. Cox proportional hazards models showed that the FAT1 signature was an independent prognostic factor for HNSCC patients. In addition, FAT1 signature was associated with the response to radiotherapy, advanced stage, and human papilloma virus (HPV) status in HNSCC patients. In conclusion, the FAT1 gene signature was associated with prognosis of HNSCC and may help to provide personalized treatments for HNSCC patients.

Reproductive health and endocrine disruption in smallmouth bass (Micropterus dolomieu) from the Lake Erie drainage, Pennsylvania, USA

Smallmouth bass Micropterus dolomieu were sampled from three sites within the Lake Erie drainage (Elk Creek, Twentymile Creek, and Misery Bay, an embayment in Presque Isle Bay). Plasma, tissues for histopathological analyses, and liver and testes preserved in RNALater® were sampled from 30 smallmouth bass (of both sexes) at each site. Liver and testes samples were analyzed for transcript abundance with Nanostring nCounter® technology. Evidence of estrogenic endocrine disruption was assessed by the presence and severity of intersex (testicular oocytes; TO) and concentrations of plasma vitellogenin in male fish. Abundance of 17 liver transcripts associated with reproductive function, endocrine activity, and contaminant detoxification pathways and 40 testes transcripts associated with male and female reproductive function, germ cell development, and steroid biosynthesis were also measured. Males with a high rate of TO (87-100%) and plasma vitellogenin were noted at all sites; however, TO severity was greatest at the site with the highest agricultural land cover. Numerous transcripts were differentially regulated among the sites and patterns of transcript abundance were used to better understand potential risk factors for estrogenic endocrine disruption. The results of this study suggest endocrine disruption is prevalent in this region and further research would benefit to identify the types of contaminants that may be associated with the observed biological effects.

Investigating the Transcriptomic and Expression Presence-Absence Variation Exist in Japanese Eel (Anguilla japonica), a Primitive Teleost

The pan-genome was defined as the complete gene set across strains, and it is built upon genes displaying presence-absence variations (PAVs); the pan-transcriptome is defined by recalling the pan-genome. Indeed, a PAV is reflected from the expression presence-absence variation (ePAV). In this study, treated with androgen, eels, which are a primitive fish from the basal lineage of Teleost, with different ovarian developments were chosen and submitted to RAN-sequencing. Transcriptomes were the assembly against eel genome scaffolds; a pair was the unit (the same eel before and after treatment) to analyze DEGs (differentially expressed genes); the core, unique, or accessory genes were identified, and the list of DEGs was analyzed to investigate ePAV. The results suggest that there was ePAV in Japanese eel, and the ePAV of eel was analyzed by pathway enrichment. These results signify the importance of genetic differential expression on the variations of phenotypes by androgen, and a transcriptomic approach appears to enable extracting multiple layers of genomic data.

[PCR analysis of the expression of chromosome 18 genes in human liver tissue: interindividual variability]

Using human chromosome 18 (Ch18) genes as an example, a PCR analysis of the interindividual variability of gene expression in liver tissue was performed. Although the quantitative profiles of the Ch18 transcriptome, expressed in the number of cDNA copies per single cell, showed a high degree of correlation between donors (Pearson correlation coefficients ranged from 0.963 to 0.966), the expression of the significant number of genes (from 13% to 19%, depending on the method of experimental data normalization) varied by more than 4-fold when comparing donors pairwise. At the same time, the proportion of differentially expressed genes increased with a decrease in the level of their expression. It is shown that the higher quantitative variability of low-abundance transcripts is mainly not technical, but biological. Bioinformatic analysis of the interindividual variability of the differential expression of chromosome 18 genes in human liver tissue did not reveal any statistically significant groups of genes related to certain biological processes that indicated a rather transient nature of the interindividual variability of their expression, probably reflecting the response of cells of an individual to specific external stimuli.

Circulating MicroRNAs in the Second Trimester From Pregnant Women Who Subsequently Developed Preeclampsia: Potential Candidates as Predictive Biomarkers and Pathway Analysis for Target Genes of miR-204-5p

MicroRNAs (miRNAs) play an important role in the pathophysiology of preeclampsia (PE). However, the expression of circulating miRNAs was not analyzed in the second trimester of pregnancy, a period of major relevance to identify predictive biomarkers for PE. Therefore, we examined the expression profiles of 84 circulating miRNAs using a PCR array in plasma collected between 20 and 25 weeks of gestation from pregnant women, who subsequently developed PE and those who remained healthy during pregnancy, randomly selected from a prospective cohort. Overall, 23 miRNAs had a fold change > 2.0 and were considered to be upregulated in plasma from pregnant women who subsequently developed PE, even before the onset of clinical symptoms of PE. However, only miR-204-5p was statistically significant (P = 0.0082). Experimentally validated interactions for the target genes of miR-204-5p extracted from miRTarBase were used in the gene set functional enrichment analysis to identify Reactome pathways. The network connecting the 37 target genes for miR-204-5p revealed pathways of known pathophysiological relevance during the early development of PE and included key genes related to PE, such as BDNF, MMP-9, MALAT1, TGFBR2, and SIRT1. We further depicted downstream targets of SIRT1 that are related to the vascular endothelial function or implicated in the pathophysiology of PE, namely, FOXO1, NFκB, HIF-1α, NOS3, and PPAR-γ. Our novel findings provide for circulating miRNAs upregulated in the second trimester on plasma from pregnant women who subsequently developed PE that is potentially related to the early development of PE, which may guide further studies focused on the validation of potential predictive biomarkers in PE.

Differential Gene Expression of Brachypodium distachyon Roots Colonized by Gluconacetobacter diazotrophicus and the Role of BdCESA8 in the Colonization

Alternatives to synthetic nitrogen fertilizer are needed to reduce the costs of crop production and offset environmental damage. Nitrogen-fixing bacterium Gluconacetobacter diazotrophicus has been proposed as a possible biofertilizer for monocot crop production. However, the colonization of G. diazotrophicus in most monocot crops is limited and deep understanding of the response of host plants to G. diazotrophicus colonization is still lacking. In this study, the molecular response of the monocot plant model Brachypodium distachyon was studied during G. diazotrophicus root colonization. The gene expression profiles of B. distachyon root tissues colonized by G. diazotrophicus were generated via next-generation RNA sequencing, and investigated through gene ontology and metabolic pathway analysis. The RNA sequencing results indicated that Brachypodium is actively involved in G. diazotrophicus colonization via cell wall synthesis. Jasmonic acid, ethylene, gibberellin biosynthesis. nitrogen assimilation, and primary and secondary metabolite pathways are also modulated to accommodate and control the extent of G. diazotrophicus colonization. Cellulose synthesis is significantly downregulated during colonization. The loss of function mutant for Brachypodium cellulose synthase 8 (BdCESA8) showed decreased cellulose content in xylem and increased resistance to G. diazotrophicus colonization. This result suggested that the cellulose synthesis of the secondary cell wall is involved in G. diazotrophicus colonization. The results of this study provide insights for future research in regard to gene manipulation for efficient colonization of nitrogen-fixing bacteria in Brachypodium and monocot crops.[Formula: see text] Copyright © 2021 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Integrated multiplex network based approach for hub gene identification in oral cancer

Background: The incidence of Oral Cancer (OC) is high in Asian countries, which goes undetected at its early stage. The study of genetics, especially genetic networks holds great promise in this endeavor. Hub genes in a genetic network are prominent in regulating the whole network structure of genes. Thus identification of such genes related to specific cancer types can help in reducing the gap in OC prognosis. Methods: Traditional study of network biology is unable to decipher the inter-dependencies within and across diverse biological networks. Multiplex network provides a powerful representation of such systems and encodes much richer information than isolated networks. In this work, we focused on the entire multiplex structure of the genetic network integrating the gene expression profile and DNA methylation profile for OC. Further, hub genes were identified by considering their connectivity in the multiplex structure and the respective protein-protein interaction (PPI) network as well. Results: 46 hub genes were inferred in our approach with a high prediction accuracy (96%), outstanding Matthews coefficient correlation value (93%) and significant biological implications. Among them, genes PIK3CG, PIK3R5, MYH7, CDC20 and CCL4 were differentially expressed and predominantly enriched in molecular cascades specific to OC. Conclusions: The identified hub genes in this work carry ontological signatures specific to cancer, which may further facilitate improved understanding of the tumorigenesis process and the underlying molecular events. Result indicates the effectiveness of our integrated multiplex network approach for hub gene identification. This work puts an innovative research route for multi-omics biological data analysis.

Salivary SARS-CoV-2 load reduction with mouthwash use: A randomized pilot clinical trial

The saliva of patients with COVID-19 has a high SARS-CoV-2 viral load. The risk of spreading the virus is high, and procedures for viral load reduction in the oral cavity are important. Little research to date has been performed on the effect of mouthwashes on the salivary SARS-CoV-2 viral load. This pilot randomized single-center clinical trial investigated whether three types of mouthwash with solutions containing either 0.075% cetylpyridinium chloride plus 0.28% zinc lactate (CPC + Zn), 1.5% hydrogen peroxide (HP), or 0.12% chlorhexidine gluconate (CHX) reduce the SARS-CoV-2 viral load in saliva at different time points. Sixty SARS-CoV-2-positive patients were recruited and randomly partitioned into a placebo (oral rinsing with distilled water) group and other groups according to the type of mouthwash. Saliva samples were collected from the participants before rinsing (T0), immediately after rinsing (T1), 30 min after rinsing (T2), and 60 min after rinsing (T3). The salivary SARS-CoV-2 viral load was measured by qRT-PCR assays. Rinsing with HP and CPC + Zn resulted in better reductions in viral load, with 15.8 ± 0.08- and 20.4 ± 3.7-fold reductions at T1, respectively. Although the CPC + Zn group maintained a 2.6 ± 0.1-fold reduction at T3, this trend was not observed for HP. HP mouthwash resulted in a significant reduction in the SARS-CoV-2 viral load up to 30 min after rinsing (6.5 ± 3.4). The CHX mouthwash significantly reduced the viral load at T1, T2, and T3 (2.1 ± 1.5-, 6.2 ± 3.8-, and 4.2 ± 2.4-fold reductions, respectively). In conclusion, mouthwash with CPC + Zinc and CHX resulted in significant reductions of the SARS-CoV-2 viral load in saliva up to 60 mins after rinsing, while HP mouthwash resulted in a significant reduction up to 30 mins after rinsing. Despite this transitory effect, these results encourage further studies and suggest that these products could be considered as risk-mitigation strategies for patients infected with SARS-CoV-2.

Use of scREAD to explore and analyze single-cell and single-nucleus RNA-seq data for Alzheimer's disease

Single-cell RNA-sequencing (scRNA-seq) and single-nucleus RNA-sequencing (snRNA-seq) studies have provided remarkable insights into understanding the molecular pathogenesis of Alzheimer's disease. We recently developed scREAD, a database to provide comprehensive analyses of all the existing AD scRNA-seq and snRNA-seq data from the public domain. Here, we report protocols for using the scREAD web interface and running the backend workflow locally. Our protocols enable custom analyses of AD single-cell and single-nucleus gene expression profiles.

For complete details on the use and execution of this protocol, please refer to Jiang et al. (2020).

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scREAD protocol for Alzheimer's disease sc/snRNA-seq analysis and interpretation

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Step-by-step tutorial to run the scREAD workflow on users’ local computers

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Comprehensive demonstrations of cell type annotation and other downstream analysis

NusG is an intrinsic transcription termination factor that stimulates motility and coordinates gene expression with NusA

NusA and NusG are transcription factors that stimulate RNA polymerase pausing in Bacillus subtilis. While NusA was known to function as an intrinsic termination factor in B. subtilis, the role of NusG in this process was unknown. To examine the individual and combinatorial roles that NusA and NusG play in intrinsic termination, Term-seq was conducted in wild type, NusA depletion, ΔnusG, and NusA depletion ΔnusG strains. We determined that NusG functions as an intrinsic termination factor that works alone and cooperatively with NusA to facilitate termination at 88% of the 1400 identified intrinsic terminators. Our results indicate that NusG stimulates a sequence-specific pause that assists in the completion of suboptimal terminator hairpins with weak terminal A-U and G-U base pairs at the bottom of the stem. Loss of NusA and NusG leads to global misregulation of gene expression and loss of NusG results in flagella and swimming motility defects.

Genetic controls of Tas1r3-independent sucrose consumption in mice

We have previously used crosses between C57BL/6ByJ (B6) and 129P3/J (129) inbred strains to map a quantitative trait locus (QTL) on mouse chromosome (Chr) 4 that affects behavioral and neural responses to sucrose. We have named it the sucrose consumption QTL 2 (Scon2), and shown that it corresponds to the Tas1r3 gene, which encodes a sweet taste receptor subunit TAS1R3. To discover other sucrose consumption QTLs, we have intercrossed B6 inbred and 129.B6-Tas1r3 congenic mice to produce F₂ hybrids, in which Scon2 (Tas1r3) does not segregate, and hence does not contribute to phenotypical variation. Chromosome mapping using this F₂ intercross identified two main-effect QTLs, Scon3 (Chr9) and Scon10 (Chr14), and an epistatically interacting QTL pair Scon3 (Chr9)-Scon4 (Chr1). Using serial backcrosses, congenic and consomic strains, we conducted high-resolution mapping of Scon3 and Scon4 and analyzed their epistatic interactions. We used mice with different Scon3 or Scon4 genotypes to understand whether these two QTLs influence sucrose intake via gustatory or postoral mechanisms. These studies found no evidence for involvement of the taste mechanisms, but suggested involvement of energy metabolism. Mice with the B6 Scon4 genotype drank less sucrose in two-bottle tests, and also had a higher respiratory exchange ratio and lower energy expenditure under basal conditions (when they had only chow and water available). Our results provide evidence that Scon3 and Scon4 influence mouse-to-mouse variation in sucrose intake and that both likely act through a common postoral mechanism.

Transcriptomic Changes Associated with Loss of Cell Viability Induced by Oxysterol Treatment of a Retinal Photoreceptor-Derived Cell Line: An In Vitro Model of Smith-Lemli-Opitz Syndrome

Smith–Lemli–Opitz Syndrome (SLOS) results from mutations in the gene encoding the enzyme DHCR7, which catalyzes conversion of 7-dehydrocholesterol (7DHC) to cholesterol (CHOL). Rats treated with a DHCR7 inhibitor serve as a SLOS animal model, and exhibit progressive photoreceptor-specific cell death, with accumulation of 7DHC and oxidized sterols. To understand the basis of this cell type specificity, we performed transcriptomic analyses on a photoreceptor-derived cell line (661W), treating cells with two 7DHC-derived oxysterols, which accumulate in tissues and bodily fluids of SLOS patients and in the rat SLOS model, as well as with CHOL (negative control), and evaluated differentially expressed genes (DEGs) for each treatment. Gene enrichment analysis and compilation of DEG sets indicated that endoplasmic reticulum stress, oxidative stress, DNA damage and repair, and autophagy were all highly up-regulated pathways in oxysterol-treated cells. Detailed analysis indicated that the two oxysterols exert their effects via different molecular mechanisms. Changes in expression of key genes in highlighted pathways (Hmox1, Ddit3, Trib3, and Herpud1) were validated by immunofluorescence confocal microscopy. The results extend our understanding of the pathobiology of retinal degeneration and SLOS, identifying potential new druggable targets for therapeutic intervention into these and other related orphan diseases.

Gene expression profiling of corona virus microarray datasets to identify crucial targets in COVID-19 patients

The current outbreak of coronavirus disease (COVID-19) has been affecting millions of people and has caused devastating mortality worldwide. Moreover, it is to be noted that cytokine storm has become an important cause for the rising mortality. However, the efforts for the development of drugs, vaccines and treatment has also been intervened due to poor understanding of host's defense mechanism and also due to the development of cytokine storm against this viral infection. Thus, a deeper understanding of the mechanism behind the immune dysregulation and cytokine storm development might give us clues for the clinical management of the severe cases. Hence, we have implemented differential gene expression analysis together with protein-protein interaction and Gene Ontology (GO) studies with the help of Severe Acute respiratory syndrome coronavirus (SARS-CoV) data sets such as GSE1739 and GSE33267 to give us more knowledge on the host immune response for the pathogenic coronavirus which in turn reduces the mortality. A total of 79 differentially-expressed genes (DEGs) were identified in our data set using the filters such as P-value and log2 fold change values of less than 0.05 and 1.5 respectively. Further, network analysis and GO studies showed that differential expression of two hub genes namely ELANE and LTF which could induce higher levels of pro-inflammatory cytokines in the lungs. We are certain that differential expression of ELANE and LTF results in an excessive inflammatory reaction known as the cytokine storm and ultimately leading to death. Therefore, targeting these key drivers of cytokine storm genes appears to be the potential therapeutic targets for combating the Severe Acute respiratory syndrome coronavirus - 2 (SARS-CoV-2) infection ultimately resulting in reduced mortality. Indeed, this predictive view may open new insights for designing an immune intervention for COVID-19 in the near future resulting in the mitigation of mortality rate.

Monohaloacetic Acids and Monohaloacetamides Attack Distinct Cellular Proteome Thiols

Disinfection byproduct (DBP) exposure has been linked to multiple adverse health outcomes. However, the molecular initiating events by which DBPs induce their toxicities remain unclear. Herein, we combined reporter cell lines and activity-based protein profiling (ABPP) chemical proteomics to identify the protein targets of three monohaloacetic acids (mHAAs) and three monohaloacetamides (mHAMs), at the proteome-wide level. While mHAAs and mHAMs have similar potencies in reducing MTT activity, mHAMs induced greater Nrf2-mediated oxidative stress responses, demonstrating their distinct toxicity pathways. ABPP on crude cell lysates suggested that general proteome thiol reactivity correlates with cytotoxicity. Interestingly, live cell ABPP results revealed class-specific proteins attacked by mHAMs or mHAAs. Subsequent proteomic analysis identified >100 unique targets per DBP. mHAMs preferentially react with redox proteins including disulfide oxidoreductase enzymes, accounting for their stronger Nrf2 responses. To further probe alkylation mechanisms, we directly monitored protein adducts and identified 120 and 37 unique peptides with iodoacetamide and iodoacetic acid adducts, respectively. Of the latter, we confirmed glyceraldehyde-3-phosphate dehydrogenase as a key target of IAA, specifically attacking the catalytic Cys 152. This is the first study reporting the distinct cellular protein targets of mHAAs and mHAMs at the proteome-wide level, which highlights their different toxicity pathways despite their similar structures.

Systematic Reconstruction of the Complete Two-Component Sensorial Network in Staphylococcus aureus

Bacteria are able to sense environmental conditions and respond accordingly. Their sensorial system relies on pairs of sensory and regulatory proteins, known as two-component systems (TCSs). The majority of bacteria contain dozens of TCSs, each of them responsible for sensing and responding to a different range of signals. Traditionally, the function of each TCS has been determined by analyzing the changes in gene expression caused by the absence of individual TCSs. Here, we used a bacterial strain deprived of the complete TC sensorial system to introduce, one by one, the active form of every TCS. This gain-of-function strategy allowed us to identify the changes in gene expression conferred by each TCS without interference of other members of the family.

In bacteria, adaptation to changes in the environment is mainly controlled through two-component signal transduction systems (TCSs). Most bacteria contain dozens of TCSs, each of them responsible for sensing a different range of signals and controlling the expression of a repertoire of target genes (regulon). Over the years, identification of the regulon controlled by each individual TCS in different bacteria has been a recurrent question. However, limitations associated with the classical approaches used have left our knowledge far from complete. In this report, using a pioneering approach in which a strain devoid of the complete nonessential TCS network was systematically complemented with the constitutively active form of each response regulator, we have reconstituted the regulon of each TCS of S. aureus in the absence of interference between members of the family. Transcriptome sequencing (RNA-Seq) and proteomics allowed us to determine the size, complexity, and insulation of each regulon and to identify the genes regulated exclusively by one or many TCSs. This gain-of-function strategy provides the first description of the complete TCS regulon in a living cell, which we expect will be useful to understand the pathobiology of this important pathogen.

IMPORTANCE Bacteria are able to sense environmental conditions and respond accordingly. Their sensorial system relies on pairs of sensory and regulatory proteins, known as two-component systems (TCSs). The majority of bacteria contain dozens of TCSs, each of them responsible for sensing and responding to a different range of signals. Traditionally, the function of each TCS has been determined by analyzing the changes in gene expression caused by the absence of individual TCSs. Here, we used a bacterial strain deprived of the complete TC sensorial system to introduce, one by one, the active form of every TCS. This gain-of-function strategy allowed us to identify the changes in gene expression conferred by each TCS without interference of other members of the family.

Merging microarray studies to identify a common gene expression signature to several structural heart diseases

BACKGROUND

Heart disease is the leading cause of death worldwide. Knowing a gene expression signature in heart disease can lead to the development of more efficient diagnosis and treatments that may prevent premature deaths. A large amount of microarray data is available in public repositories and can be used to identify differentially expressed genes. However, most of the microarray datasets are composed of a reduced number of samples and to obtain more reliable results, several datasets have to be merged, which is a challenging task. The identification of differentially expressed genes is commonly done using statistical methods. Nonetheless, these methods are based on the definition of an arbitrary threshold to select the differentially expressed genes and there is no consensus on the values that should be used.

RESULTS

Nine publicly available microarray datasets from studies of different heart diseases were merged to form a dataset composed of 689 samples and 8354 features. Subsequently, the adjusted p-value and fold change were determined and by combining a set of adjusted p-values cutoffs with a list of different fold change thresholds, 12 sets of differentially expressed genes were obtained. To select the set of differentially expressed genes that has the best accuracy in classifying samples from patients with heart diseases and samples from patients with no heart condition, the random forest algorithm was used. A set of 62 differentially expressed genes having a classification accuracy of approximately 95% was identified.

CONCLUSIONS

We identified a gene expression signature common to different cardiac diseases and supported our findings by showing their involvement in the pathophysiology of the heart. The approach used in this study is suitable for the identification of gene expression signatures, and can be extended to different diseases.