Application of Graph Models to the Identification of Transcriptomic Oncometabolic Pathways in Human Hepatocellular Carcinoma.

Whole-tissue transcriptomic analyses have been helpful to characterize molecular subtypes of hepatocellular carcinoma (HCC). Metabolic subtypes of human HCC have been defined, yet whether these different metabolic classes are clinically relevant or derive in actionable cancer vulnerabilities is still an unanswered question. Publicly available gene sets or gene signatures have been used to infer functional changes through gene set enrichment methods. However, metabolism-related gene signatures are poorly co-expressed when applied to a biological context. Here, we apply a simple method to infer highly consistent signatures using graph-based statistics. Using the Cancer Genome Atlas Liver Hepatocellular cohort (LIHC), we describe the main metabolic clusters and their relationship with commonly used molecular classes, and with the presence of TP53 or CTNNB1 driver mutations. We find similar results in our validation cohort, the LIRI-JP cohort. We describe how previously described metabolic subtypes could not have therapeutic relevance due to their overall downregulation when compared to non-tumoral liver, and identify N-glycan, mevalonate and sphingolipid biosynthetic pathways as the hallmark of the oncogenic shift of the use of acetyl-coenzyme A in HCC metabolism. Finally, using DepMap data, we demonstrate metabolic vulnerabilities in HCC cell lines.

Adrenal pheochromocytoma impacts three main pathways: cysteine-methionine, pyrimidine, and tyrosine metabolism. / è‚¾ä¸Šè ºå—œé“¬ç»†èƒžç˜¤å½±å“ä¸‰æ¡ä¸»è¦ä»£è°¢é€šè·¯ï¼šåŠèƒ±æ°¨é ¸-è›‹æ°¨é ¸ä»£è°¢ã€å˜§å•¶ä»£è°¢å’Œé ªæ°¨é ¸ä»£è°¢é€šè·¯.

Pheochromocytomas and paragangliomas (PPGLs) cause symptoms by altering the circulation levels of catecholamines and peptide hormones. Currently, the diagnosis of PPGLs relies on diagnostic imaging and the detection of catecholamines. In this study, we used ultra-performance liquid chromatography (UPLC)/quadrupole time-of-flight mass spectrometry (Q-TOF MS) analysis to identify and measure the perioperative differential metabolites in the plasma of adrenal pheochromocytoma patients. We identified differentially expressed genes by comparing the transcriptomic data of pheochromocytoma with the normal adrenal medulla. Through conducting two steps of metabolomics analysis, we identified 111 differential metabolites between the healthy group and the patient group, among which 53 metabolites were validated. By integrating the information of differential metabolites and differentially expressed genes, we inferred that the cysteine-methionine, pyrimidine, and tyrosine metabolism pathways were the three main metabolic pathways altered by the neoplasm. The analysis of transcription levels revealed that the tyrosine and cysteine-methionine metabolism pathways were downregulated in pheochromocytoma, whereas the pyrimidine pathway showed no significant difference. Finally, we developed an optimized diagnostic model of two metabolites, L-dihydroorotic acid and vanylglycol. Our results for these metabolites suggest that they may serve as potential clinical biomarkers and can be used to supplement and improve the diagnosis of pheochromocytoma.

Screening candidate genes for fruit size based on QTL-seq in Chinese jujube.

Introduction: Fruit size is an important economic trait affecting jujube fruit quality, which has always been the focus of marker-assisted breeding of jujube traits. However, despite a large number of studies have been carried out, the mechanism and key genes regulating jujube fruit size are mostly unknown. Methods: In this study, we used a new analysis method Quantitative Trait Loci sequencing (QTL-seq) (bulked segregant analysis) to screen the parents 'Yuhong' and 'Jiaocheng 5' with significant phenotypic differences and mixed offspring group with extreme traits of large fruit and small fruit, respectively, and, then, DNA mixed pool sequencing was carried out to further shortening the QTL candidate interval for fruit size trait and excavated candidate genes for controlling fruit size. Results: The candidate intervals related to jujube fruit size were mainly located on chromosomes 1, 5, and 10, and the frequency of chromosome 1 was the highest. Based on the QTL-seq results, the annotation results of ANNOVAR were extracted from 424 SNPs (single-nucleotide polymorphisms) and 164 InDels (insertion-deletion), from which 40 candidate genes were selected, and 37 annotated candidate genes were found in the jujube genome. Four genes (LOC107428904, LOC107415626, LOC125420708, and LOC107418290) that are associated with fruit size growth and development were identified by functional annotation of the genes in NCBI (National Center for Biotechnology Information). The genes can provide a basis for further exploration and identification on genes regulating jujube fruit size. Discussion: In summary, the data obtained in this study revealed that QTL intervals and candidate genes for fruit size at the genomic level provide valuable resources for future functional studies and jujube breeding.

Variations and inequities in access to cardiac diagnostic services in Ontario Canada.

OBJECTIVES: Echocardiography is an essential diagnostic modality known to have wide regional utilization variations. This study's objectives were to quantify regional variations and to examine the extent to which they are explained by differences in population age, sex, cardiac disease prevalence (CDP), and social determinants of health (SDH) risk. METHODS: This is an observational study of all echocardiography exams performed in Ontario in 2019/20 (n = 695,622). We measured regional variations in echocardiography crude rates and progressively standardized rates for population age, sex, CDP, and SDH risk. RESULTS: After controlling for differences in population age, sex, and CDP, Ontario's highest rate regions had echocardiography rates 57% higher than its lowest rate regions. Forty eight percent of total variation was not explained by differences in age, sex, and CDP. CDP increased with SDH risk. Access to most cardiac diagnostics was negatively correlated with SDH risk, while cardiac catheterization rates were positively correlated with SDH risk. CONCLUSION: Variations analysis that adjusts for age and sex only without including clinical measures of need are likely to overestimate the unwarranted portion of total variation. Substantial variations persisted despite a mandatory provider accreditation policy aimed at curtailing them. The associations between variations and SDH risks imply a need to redress access and outcome inequities.

Successful Multimodal Treatment of Intracranial Growing Teratoma Syndrome with Malignant Features.

Molecular analysis of the growing teratoma syndrome has not been extensively studied. Here, we report a 14-year-old boy with a growing mass during treatment for a mixed germ cell tumor of the pineal region. Tumor markers were negative; thus, growing teratoma syndrome was suspected. A radical resection via the occipital transtentorial approach was performed, and histopathological examination revealed a teratoma with malignant features. Methylation classifier analysis confirmed the diagnosis of teratoma, and DMRT1 loss and 12p gain were identified by copy number variation analysis, potentially elucidating the cause of growth and malignant transformation of the teratoma. The patient remains in remission after intense chemoradiation treatment as a high-risk germ cell tumor.

Adrenal pheochromocytoma impacts three main pathways: cysteine-methionine, pyrimidine, and tyrosine metabolism. / è‚¾ä¸Šè ºå—œé“¬ç»†èƒžç˜¤å½±å“ä¸‰æ¡ä¸»è¦ä»£è°¢é€šè·¯ï¼šåŠèƒ±æ°¨é ¸-è›‹æ°¨é ¸ä»£è°¢ã€å˜§å•¶ä»£è°¢å’Œé ªæ°¨é ¸ä»£è°¢é€šè·¯.

Pheochromocytomas and paragangliomas (PPGLs) cause symptoms by altering the circulation levels of catecholamines and peptide hormones. Currently, the diagnosis of PPGLs relies on diagnostic imaging and the detection of catecholamines. In this study, we used ultra-performance liquid chromatography (UPLC)/quadrupole time-of-flight mass spectrometry (Q-TOF MS) analysis to identify and measure the perioperative differential metabolites in the plasma of adrenal pheochromocytoma patients. We identified differentially expressed genes by comparing the transcriptomic data of pheochromocytoma with the normal adrenal medulla. Through conducting two steps of metabolomics analysis, we identified 111 differential metabolites between the healthy group and the patient group, among which 53 metabolites were validated. By integrating the information of differential metabolites and differentially expressed genes, we inferred that the cysteine-methionine, pyrimidine, and tyrosine metabolism pathways were the three main metabolic pathways altered by the neoplasm. The analysis of transcription levels revealed that the tyrosine and cysteine-methionine metabolism pathways were downregulated in pheochromocytoma, whereas the pyrimidine pathway showed no significant difference. Finally, we developed an optimized diagnostic model of two metabolites, L-dihydroorotic acid and vanylglycol. Our results for these metabolites suggest that they may serve as potential clinical biomarkers and can be used to supplement and improve the diagnosis of pheochromocytoma.

Developing and validating the model of tumor-infiltrating immune cell to predict survival in patients receiving radiation therapy for head and neck squamous cell carcinoma.

Background: Radiotherapy (RT) is a mainstay of head and neck squamous cell carcinoma (HNSCC) treatment. Due to the influence of RT on tumor cells and immune/stromal cells in microenvironment, some studies suggest that immunologic landscape could shape treatment response. To better predict the survival based on genomic data, we developed a prognostic model using tumor-infiltrating immune cell (TIIC) signature to predict survival in patients undergoing RT for HNSCC. Methods: Gene expression data and clinical information were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Data from HNSCC patients undergoing RT were extracted for analysis. TIICs prevalence in HNSCC patients was quantified by gene set variation analysis (GSVA) algorithm. TIICs and post-RT survival were analyzed using univariate Cox regression analysis and used to construct and validate a tumor-infiltrating cells score (TICS). Results: Five of 26 immune cells were significantly associated with HNSCC prognosis in the training cohort (all P<0.05). Kaplan-Meier (KM) survival curves showed that patients in the high TICS group had better survival outcomes (log-rank test, P<0.05). Univariate analyses demonstrated that the TICS had independent prognostic predictive ability for RT outcomes (P<0.05). Patients with high TICS scores showed significantly higher expression of immune-related genes. Functional pathway analyses further showed that the TICS was significantly related to immune-related biological process. Stratified analyses supported integrating TICS and tumor mutation burden (TMB) into individualized treatment planning, as an adjunct to classification by clinical stage and human papillomavirus (HPV) infection. Conclusions: The TICS model supports a personalized medicine approach to RT for HNSCC. Increased prevalence of TIIC within the tumor microenvironment (TME) confers a better prognosis for patients undergoing treatment for HNSCC.

Identification of renal ischemia reperfusion injury-characteristic genes, pathways and immunological micro-environment features through bioinformatics approaches.

BACKGROUND: Biomarkers and pathways associated with renal ischemia reperfusion injury (IRI) had not been well unveiled. This study was intended to investigate and summarize the regulatory networks for related hub genes. Besides, the immunological micro-environment features were evaluated and the correlations between immune cells and hub genes were also explored. METHODS: GSE98622 containing mouse samples with multiple IRI stages and controls was collected from the GEO database. Differentially expressed genes (DEGs) were recognized by the R package limma, and the GO and KEGG analyses were conducted by DAVID. Gene set variation analysis (GSVA) and weighted gene coexpression network analysis (WGCNA) had been implemented to uncover changed pathways and gene modules related to IRI. Besides the known pathways such as apoptosis pathway, metabolic pathway, and cell cycle pathways, some novel pathways were also discovered to be critical in IRI. A series of novel genes associated with IRI was also dug out. An IRI mouse model was constructed to validate the results. RESULTS: The well-known IRI marker genes (Kim1 and Lcn2) and novel hub genes (Hbegf, Serpine2, Apbb1ip, Trip13, Atf3, and Ncaph) had been proved by the quantitative real-time polymerase chain reaction (qRT-PCR). Thereafter, miRNAs targeted to the dysregulated genes were predicted and the miRNA-target network was constructed. Furthermore, the immune infiltration for these samples was predicted and the results showed that macrophages infiltrated to the injured kidney to affect the tissue repair or fibrosis. Hub genes were significantly positively or negatively correlated with the macrophage abundance indicating they played a crucial role in macrophage infiltration. CONCLUSIONS: Consequently, the pathways, hub genes, miRNAs, and the immune microenvironment may explain the mechanism of IRI and might be the potential targets for IRI treatments.

A polyamine metabolism risk signature for predicting the prognosis and immune therapeutic response of kidney cancer.

Background: Polyamine metabolism is critically involved in the proliferation and metastasis of tumor cells, including in kidney renal clear cell (KIRC) cancer. However, the molecular mechanisms underlying the effect of polyamines in KIRC cancer remain largely unknown. Methods: The messenger RNA (mRNA) expression profile of KIRC was downloaded from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and ArrayExpress database. Differential expression analysis was performed with the "limma" package in R. Univariate Cox regression and multivariable Cox regression were used to estimate correlation between variables and prognosis. Least absolute shrinkage and selection operator (LASSO) Cox regression analysis was employed to screen variables and construct a risk signature. A nomogram model was established using the risk signature and clinical variables. Receiver operating characteristic (ROC), calibration curve, and decision curve analysis (DCA) were used to assess the predicted accuracy and clinical benefit of the model. Results: We identified nine differentially expressed polyamine metabolism-related genes (PMRGs) in TCGA-KIRC. Of these, six were closely associated with patients' outcomes. These six genes participated in different pathways and originated from different cell types within the tumor microenvironment (TME). Using the mRNA expression values of these genes, we constructed a 4-gene PMRG risk signature. Patients with high PMRG risk exhibited worse outcomes, and our analysis showed that the PMRG risk signature was an independent prognostic factor when clinical information was used as a covariate. We also found that multiple immune- or metabolism-related pathways were differentially enriched in high or low PMRG risk groups, suggesting that altering these pathways could lead to different clinical outcomes. Finally, in two external datasets, we found that the PMRG risk signature could predict the response of patients to immune therapy. Conclusions: In summary, our study identified several potentially important PMRGs in KIRC and constructed a practical risk signature, which could serve as a foundation for further development of polyamine metabolism-based targeted therapies for KIRC.

Gene signatures in U-BIOPRED severe asthma for molecular phenotyping and precision medicine: time for clinical use.

INTRODUCTION: The use and generation of gene signatures have been established as a method to define molecular endotypes in complex diseases such as severe asthma. Bioinformatic approaches have now been applied to large omics datasets to define the various co-existing inflammatory and cellular functional pathways driving or characterizing a particular molecular endotype. AREAS COVERED: Molecular phenotypes and endotypes of Type 2 inflammatory pathways and also of non-Type 2 inflammatory pathways, such as IL-6 trans-signaling, IL-17 activation, and IL-22 activation, have been defined in the Unbiased Biomarkers for the Prediction of Respiratory Disease Outcomes dataset. There has also been the identification of the role of mast cell activation and of macrophage dysfunction in various phenotypes of severe asthma. EXPERT OPINION: Phenotyping on the basis of clinical treatable traits is not sufficient for understanding of mechanisms driving the disease in severe asthma. It is time to consider whether certain patients with severe asthma, such as those non-responsive to current therapies, including Type 2 biologics, would be better served using an approach of molecular endotyping using gene signatures for management purposes rather than the current sole reliance on blood eosinophil counts or exhaled nitric oxide measurements.

Whole-genome resequencing and transcriptome analyses of four generation mutants to reveal spur-type and skin-color related genes in apple (Malus domestica Borkh. Cv. Red delicious).

BACKGROUND: Bud sport is a kind of somatic mutation that usually occurred in apple. 'Red Delicious' is considered to be a special plant material of bud sport, whereas the genetic basis of plant mutants is still unknown. In this study, we used whole-genome resequencing and transcriptome sequencing to identify genes related to spur-type and skin-color in the 'Red Delicious' (G0) and its four generation mutants including 'Starking Red' (G1), 'Starkrimson' (G2), 'Campbell Redchief' (G3) and 'Vallee Spur' (G4). RESULTS: The number of single nucleotide polymorphisms (SNPs), insertions and deletions (InDels) and structural variations (SVs) were decreased in four generation mutants compared to G0, and the number of unique SNPs and InDels were over 9-fold and 4-fold higher in G1 versus (vs.) G2 and G2 vs. G3, respectively. Chromosomes 2, 5, 11 and 15 carried the most SNPs, InDels and SVs, while chromosomes 1 and 6 carried the least. Meanwhile, we identified 4,356 variation genes by whole-genome resequencing and transcriptome, and obtained 13 and 16 differentially expressed genes (DEGs) related to spur-type and skin-color by gene expression levels. Among them, DELLA and 4CL7 were the potential genes that regulate the difference of spur-type and skin-color characters, respectively. CONCLUSIONS: Our study identified potential genes associated with spur-type and skin-color differences in 'Red Delicious' and its four generation mutants, which provides a theoretical foundation for the mechanism of the apple bud sport.

Identification and validation of risk score model based on gene set activity as a diagnostic biomarker for endometriosis.

Objective: The enigmatic nature of Endometriosis (EMS) pathogenesis necessitates investigating alterations in signaling pathway activity to enhance our comprehension of the disease's characteristics. Methods: Three published gene expression profiles (GSE11691, GSE25628, and GSE7305 datasets) were downloaded, and the "combat" algorithm was employed for batch correction, gene expression difference analysis, and pathway enrichment difference analysis. The protein-protein interaction (PPI) network was constructed to identify core genes, and the relative enrichment degree of gene sets was evaluated. The Lasso regression model identified candidate gene sets with diagnostic value, and a risk scoring diagnostic model was constructed for further validation on the GSE86534 and GSE5108 datasets. CIBERSORT was used to assess the composition of immune cells in EMS, and the correlation between EMS diagnostic value gene sets and immune cells was evaluated. Results: A total of 568 differentially expressed genes were identified between eutopic and ectopic endometrium, with 10 core genes in the PPI network associated with cell cycle regulation. Inflammation-related pathways, including cytokine-receptor signaling and chemokine signaling pathways, were significantly more active in ectopic endometrium compared to eutopic endometrium. Diagnostic gene sets for EMS, such as homologous recombination, base excision repair, DNA replication, P53 signaling pathway, adherens junction, and SNARE interactions in vesicular transport, were identified. The risk score's area under the curve (AUC) was 0.854, as indicated by the receiver operating characteristic (ROC) curve, and the risk score's diagnostic value was validated by the validation cohort. Immune cell infiltration analysis revealed correlations between the risk score and Macrophages M2, Plasma cells, resting NK cells, activated NK cells, and regulatory T cells. Conclusion: The risk scoring diagnostic model, based on pathway activity, demonstrates high diagnostic value and offers novel insights and strategies for the clinical diagnosis and treatment of Endometriosis.

A phospho-deficient α3 glycine receptor mutation alters synaptic glycine and GABA release in mouse spinal dorsal horn neurons.

Glycine receptors (GlyRs), together with GABAA receptors, mediate postsynaptic inhibition in most spinal cord and hindbrain neurons. In several CNS regions, GlyRs are also expressed in presynaptic terminals. Here, we analysed the effects of a phospho-deficient mutation (S346A) in GlyR α3 subunits on inhibitory synaptic transmission in superficial spinal dorsal horn neurons, where this subunit is abundantly expressed. Unexpectedly, we found that not only were the amplitudes of evoked glycinergic inhibitory postsynaptic currents (IPSCs) significantly larger in GlyRα3(S346A) mice than in mice expressing wild-type α3GlyRs (GlyRα3(WT) mice), but so were those of GABAergic IPSCs. Decreased frequencies of spontaneously occurring glycinergic and GABAergic miniature IPSCs (mIPSCs) with no accompanying change in mIPSC amplitudes suggested a change in presynaptic transmitter release. Paired-pulse experiments on glycinergic IPSCs revealed an increased paired-pulse ratio and a smaller coefficient of variation in GlyRα3(S346A) mice, which together indicate a reduction in transmitter release probability and an increase in the number of releasable vesicles. Paired-pulse ratios of GABAergic IPSCs recorded in the presence of strychnine were not different between genotypes, while the coefficient of variation was smaller in GlyRα3(S346A) mice, demonstrating that the decrease in release probability was readily reversible by GlyR blockade, while the difference in the size of the pool of releasable vesicles remained. Taken together, our results suggest that presynaptic α3 GlyRs regulate synaptic glycine and GABA release in superficial dorsal horn neurons, and that this effect is potentially regulated by their phosphorylation status. KEY POINTS: A serine-to-alanine point mutation was introduced into the glycine receptor α3 subunit of mice. This point mutation renders α3 glycine receptors resistant to protein kinase A mediated phosphorylation but has otherwise only small effects on receptor function. Patch-clamp recordings from neurons in mouse spinal cord slices revealed an unexpected increase in the amplitudes of both glycinergic and GABAergic evoked inhibitory postsynaptic currents (IPSCs). Miniature IPSCs, paired-pulse ratios and synaptic variation analyses indicate a change in synaptic glycine and GABA release. The results strongly suggest that α3 subunit-containing glycine receptors are expressed on presynaptic terminals of inhibitory dorsal horn neurons where they regulate transmitter release.

Immune gene patterns and characterization of the tumor immune microenvironment associated with cancer immunotherapy efficacy.

Although immunotherapy has revolutionized cancer management, most patients do not derive benefits from it. Aiming to explore an appropriate strategy for immunotherapy efficacy prediction, we collected 6251 patients' transcriptome data from multicohort population and analyzed the data using a machine learning algorithm. In this study, we found that patients from three immune gene clusters had different overall survival when treated with immunotherapy (P < 0.001), and that these clusters had differential states of hypoxia scores and metabolism functions. The immune gene score showed good immunotherapy efficacy prediction (AUC was 0.737 at 20 months), which was well validated. The immune gene score, tumor mutation burden, and long non-coding RNA score were further combined to build a tumor immune microenvironment signature, which correlated more strongly with overall survival (AUC, 0.814 at 20 months) than when using a single variable. Thus, we recommend using the characterization of the tumor immune microenvironment associated with immunotherapy efficacy via a multi-omics analysis of cancer.

Antiplasmodial activity of the bulbs of Fritillaria cirrhosa D.Don (Syn: Fritillaria roylei Hook.): UPLC-IM-Q-TOF-MS/MS-based biochemometric approach for the identification of marker compounds.

ETHNOPHARMACOLOGICAL RELEVANCE: Fritillaria cirrhosa D.Don (Syn: Fritillaria roylei Hook.) (Hindi name: Kshirakakoli) is a critically endangered Himalayan medicinal plant, well documented in Ayurveda for its therapeutic uses against various disorders such as jvara (fever), kasa (respiratory tract disease) etc. Its bulbs are also used as Szechuan-Pei-Mu for their antipyretic properties in the traditional Chinese medicine. However, despite its ethnomedicinal usage, the therapeutic use of F. cirrhosa bulbs for jvara (fever) related conditions such as malaria has remained unexplored. Hence in the context of increasing global concerns about drug-resistant malaria, it is important to investigate the antiplasmodial activity of F. cirrhosa bulbs for novel antimalarial agents. AIM OF THE STUDY: To investigate the antiplasmodial effects of the extracts/fractions of F. cirrhosa bulbs by the biochemometric approach and to rationalize its ethnopharmacological usage for jvara (fever) related conditions such as malaria. MATERIAL AND METHODS: This study involves the UHPLC-MS-based plant material selection, preparation, quantification, and assessment of F. cirrhosa bulb extracts against CQ-sensitive Pf 3D7 & CQ-resistant Pf INDO strains. Further, UPLC-IM-Q-TOF-MS-based biochemometric approach has been applied for the identification of marker compounds responsible for the observed antiplasmodial effects. The identified marker compounds were also assessed for their in silico ADMET properties and binding efficacy with the drug transporter Pf CRT. RESULTS: Different F. cirrhosa bulb extracts/fractions showed promising antiplasmodial activity with IC50 values 2.71-19.77 µg/mL for CQ-resistant Pf INDO strain and 1.76-21.52 µg/mL for CQ-sensitive Pf 3D7 strain. UPLC-IM-Q-TOF-MS/MS-based biochemometric analysis revealed four marker compounds i.e., peimine (m/z 432.3448), peimisine (m/z 428.3504), puqiedinone (m/z 414.3379), and puqiedine (m/z 416.3509) responsible for the observed antiplasmodial activity. The identified marker compounds showed excellent binding efficacy with Pf CRT and suitable drug-like properties in silico. CONCLUSIONS: The study demonstrated promising antiplasmodial activity of the chloroform and alkaloid enriched fractions of F. cirrhosa bulbs and further identified the four marker compounds responsible for the promising antiplasmodial activity. These marker compounds i.e., peimine, peimisine, puqiedinone and puqiedine were identified by the biochemometric analysis as the putative antiplasmodial constituents of the F. cirrhosa bulbs. Further, in silico studies indicated the good binding affinity of the marker compounds with Pf CRT along with suitable ADMET properties. Overall, the study elucidates the antiplasmodial activity of F. cirrhosa bulbs from the western Himalayan region and provides nascent scientific evidence for their ethnopharmacological usage in jvara (fever) related conditions such as malaria.

IL1RAP expression and the enrichment of IL-33 activation signatures in severe neutrophilic asthma.

BACKGROUND: Interleukin (IL)-33 is an upstream regulator of type 2 (T2) eosinophilic inflammation and has been proposed as a key driver of some asthma phenotypes. OBJECTIVE: To derive gene signatures from in vitro studies of IL-33-stimulated cells and use these to determine IL-33-associated enrichment patterns in asthma. METHODS: Signatures downstream of IL-33 stimulation were derived from our in vitro study of human mast cells and from public datasets of in vitro stimulated human basophils, type 2 innate lymphoid cells (ILC2), regulatory T cells (Treg) and endothelial cells. Gene Set Variation Analysis (GSVA) was used to probe U-BIOPRED and ADEPT sputum transcriptomics to determine enrichment scores (ES) for each signature according to asthma severity, sputum granulocyte status and previously defined molecular phenotypes. RESULTS: IL-33-activated gene signatures were cell-specific with little gene overlap. Individual signatures, however, were associated with similar signalling pathways (TNF, NF-κB, IL-17 and JAK/STAT signalling) and immune cell differentiation pathways (Th17, Th1 and Th2 differentiation). ES for IL-33-activated gene signatures were significantly enriched in asthmatic sputum, particularly in patients with neutrophilic and mixed granulocytic phenotypes. IL-33 mRNA expression was not elevated in asthma whereas the expression of mRNA for IL1RL1, the IL-33 receptor, was up-regulated in the sputum of severe eosinophilic asthma. The mRNA expression for IL1RAP, the IL1RL1 co-receptor, was greatest in severe neutrophilic and mixed granulocytic asthma. CONCLUSIONS: IL-33-activated gene signatures are elevated in neutrophilic and mixed granulocytic asthma corresponding with IL1RAP co-receptor expression. This suggests incorporating T2-low asthma in anti-IL-33 trials.

Descriptive Statistics and Genome-Wide Copy Number Analysis of Milk Production Traits of Jiangsu Chinese Holstein Cows.

Milk production traits are the most important quantitative economic traits in dairy cow production; improving the yield and quality of milk is an important way to ensure the production efficiency of the dairy industry. This study carried out a series of in-depth statistical genetics studies and molecular analyses on the Chinese Holstein cows in the Jiangsu Province, such as descriptive statistics and copy number variation analysis. A genetic correlation, phenotypic correlation, and descriptive statistical analysis of five milk production traits (milk yield, milk fat percentage, milk fat yield, milk protein percentage, and milk protein yield) of the dairy cows were analyzed using the SPSS and DMU software. Through quality control, 4173 cows and their genomes were used for genomic study. Then, SNPs were detected using DNA chips, and a copy number variation (CNV) analysis was carried out to locate the quantitative trait loci (QTL) of the milk production traits by Perl program software Penn CNV and hidden Markov model (HMM). The phenotypic means of the milk yield, milk fat percentage, milk fat mass, milk protein percentage, and milk protein mass at the first trimester were lower than those at the other trimesters by 8.821%, 1.031%, 0.930%, 0.003%, and 0.826%, respectively. The five milk production traits showed a significant phenotypic positive correlation (p < 0.01) and a high genetic positive correlation among the three parities. Based on the GGPBovine 100 K SNP data, QTL-detecting research on the fist-parity milk performance of dairy cows was carried out via the CNV. We identified 1731 CNVs and 236 CNVRs in the 29 autosomes of 984 Holstein dairy cows, and 19 CNVRs were significantly associated with the milk production traits (p < 0.05). These CNVRs were analyzed via a bioinformatics analysis; a total of 13 gene ontology (GO) terms and 20 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were significantly enriched (p < 0.05), and these terms and pathways are mainly related to lipid metabolism, amino acid metabolism, and cellular catabolic processes. This study provided a theoretical basis for the molecular-marker-assisted selection of dairy cows by developing descriptive statistics on the milk production traits of dairy cows and by locating the QTL and functional genes that affect the milk production traits of first-born dairy cows. The results describe the basic status of the milk production traits of the Chinese Holstein cows in Jiangsu and locate the QTL and functional genes that affect the milk production traits of the first-born cows, providing a theoretical basis for the molecular-marker-assisted selection of dairy cows.

Computational solutions for spatial transcriptomics.

Transcriptome level expression data connected to the spatial organization of the cells and molecules would allow a comprehensive understanding of how gene expression is connected to the structure and function in the biological systems. The spatial transcriptomics platforms may soon provide such information. However, the current platforms still lack spatial resolution, capture only a fraction of the transcriptome heterogeneity, or lack the throughput for large scale studies. The strengths and weaknesses in current ST platforms and computational solutions need to be taken into account when planning spatial transcriptomics studies. The basis of the computational ST analysis is the solutions developed for single-cell RNA-sequencing data, with advancements taking into account the spatial connectedness of the transcriptomes. The scRNA-seq tools are modified for spatial transcriptomics or new solutions like deep learning-based joint analysis of expression, spatial, and image data are developed to extract biological information in the spatially resolved transcriptomes. The computational ST analysis can reveal remarkable biological insights into spatial patterns of gene expression, cell signaling, and cell type variations in connection with cell type-specific signaling and organization in complex tissues. This review covers the topics that help choosing the platform and computational solutions for spatial transcriptomics research. We focus on the currently available ST methods and platforms and their strengths and limitations. Of the computational solutions, we provide an overview of the analysis steps and tools used in the ST data analysis. The compatibility with the data types and the tools provided by the current ST analysis frameworks are summarized.

Genetic Variation Analysis of Porcine Circovirus Type 4 in South China in 2019 to 2021.

Porcine circovirus type 4 (PCV4) is a novel virus associated with porcine dermatitis and nephropathy syndrome (PDNS)-like signs identified firstly in China in 2019. However, the details of the molecular epidemiology of PCV4 are unclear at this time. A total of forty-two related sequences were selected from the GenBank database to explore the spread of PCV4 and its rule in genetic evolution. Of the selected strains, 41 were from south China in 2019 to 2021 and the other was a foreign representative strain. Phylogenetic tree construction, nucleotide and amino acid (aa) sequence alignment, gene recombination and antigen structure prediction were performed on the collected sequences using bioinformatics softwares. The 42 PCV4 strains were divided into two subgenotypes: PCV4a (35/42) and PCV4b (7/42), according to the constructed genetic evolution tree. PCV4a is the main epidemic strain, and it can be further divided into two different gene clusters: PCV4a-1 (22/35) and PCV4a-2 (13/35). The pairwise comparison analysis showed that the complete genome sequence similarity of the 42 PCV4 strains ranged between 97.9% and 100%, and the aa sequences of the Cap proteins of 42 PCV4 strains had three major heterogenic or hypervariable regions-27-28, 96 and 212-all located near the antigenic epitope of the Cap protein. The results of this study can provide some basis for further studying the spread and epidemic growth of PCV4, and the prevention and control of PCV4 infection in China.

Added Value of Reanalysis of Whole Exome- and Whole Genome Sequencing Data From Patients Suspected of Primary Immune Deficiency Using an Extended Gene Panel and Structural Variation Calling.

Background: Knowledge of the genetic variation underlying Primary Immune Deficiency (PID) is increasing. Reanalysis of genome-wide sequencing data from undiagnosed patients with suspected PID may improve the diagnostic rate. Methods: We included patients monitored at the Department of Infectious Diseases or the Child and Adolescent Department, Rigshospitalet, Denmark, for a suspected PID, who had been analysed previously using a targeted PID gene panel (457 PID-related genes) on whole exome- (WES) or whole genome sequencing (WGS) data. A literature review was performed to extend the PID gene panel used for reanalysis of single nucleotide variation (SNV) and small indels. Structural variant (SV) calling was added on WGS data. Results: Genetic data from 94 patients (86 adults) including 36 WES and 58 WGS was reanalysed a median of 23 months after the initial analysis. The extended gene panel included 208 additional PID-related genes. Genetic reanalysis led to a small increase in the proportion of patients with new suspicious PID related variants of uncertain significance (VUS). The proportion of patients with a causal genetic diagnosis was constant. In total, five patients (5%, including three WES and two WGS) had a new suspicious PID VUS identified due to reanalysis. Among these, two patients had a variant added due to the expansion of the PID gene panel, and three patients had a variant reclassified to a VUS in a gene included in the initial PID gene panel. The total proportion of patients with PID related VUS, likely pathogenic, and pathogenic variants increased from 43 (46%) to 47 (50%), as one patient had a VUS detected in both initial- and reanalysis. In addition, we detected new suspicious SNVs and SVs of uncertain significance in PID candidate genes with unknown inheritance and/or as heterozygous variants in genes with autosomal recessive inheritance in 8 patients. Conclusion: These data indicate a possible diagnostic gain of reassessing WES/WGS data from patients with suspected PID. Reasons for the possible gain included improved knowledge of genotype-phenotype correlation, expanding the gene panel, and adding SV analyses. Future studies of genotype-phenotype correlations may provide additional knowledge on the impact of the new suspicious VUSs.