Unique ability of pandemic influenza to downregulate the genes involved in neuronal disorders

mRNA Profiling and Transcriptomics Analysis of Chickens Received Newcastle Disease Virus Genotype II and Genotype VII Vaccines

Newcastle Disease Virus (NDV) genotype VII (GVII) is becoming the predominant strain of NDV in the poultry industry. It causes high mortality even in vaccinated chickens with a common NDV genotype II vaccine (GII-vacc). To overcome this, the killed GVII vaccine has been used to prevent NDV outbreaks. However, the debate about vaccine differences remains ongoing. Hence, this study investigated the difference in chickens' responses to the two vaccines at the molecular level. The spleen transcriptomes from vaccinated chickens reveal that GVII-vacc affected the immune response by downregulating neuroinflammation. It also enhanced a synaptogenesis pathway that operates typically in the nervous system, suggesting a mechanism for the neurotrophic effect of this strain. We speculated that the down-regulated immune system regulation correlated with protecting the nervous system from excess leukocytes and cytokine activity. In contrast, GII-vacc inhibited apoptosis by downregulating PERK/ATF4/CHOP as part of the unfolded protein response pathway but did not affect the expression of the same synaptogenesis pathway. Thus, the application of GVII-vacc needs to be considered in countries where GVII is the leading cause of NDV outbreaks. The predicted molecular signatures may also be used in developing new vaccines that trigger specific genes in the immune system in combating NDV outbreaks.

Transcriptomic signature of cancer cachexia by integration of machine learning, literature mining and meta-analysis

BACKGROUND

Cancer cachexia is a severe metabolic syndrome marked by skeletal muscle atrophy. A successful clinical intervention for cancer cachexia is currently lacking. The study of cachexia mechanisms is largely based on preclinical animal models and the availability of high-throughput transcriptomic datasets of cachectic mouse muscles is increasing through the extensive use of next generation sequencing technologies.

METHODS

Cachectic mouse muscle transcriptomic datasets of ten different studies were combined and mined by seven attribute weighting models, which analysed both categorical variables and numerical variables. The transcriptomic signature of cancer cachexia was identified by attribute weighting algorithms and was used to evaluate the performance of eleven pattern discovery models. The signature was employed to find the best combination of drugs (drug repurposing) for developing cancer cachexia treatment strategies, as well as to evaluate currently used cachexia drugs by literature mining.

RESULTS

Attribute weighting algorithms ranked 26 genes as the transcriptomic signature of muscle from mice with cancer cachexia. Deep Learning and Random Forest models performed better in differentiating cancer cachexia cases based on muscle transcriptomic data. Literature mining revealed that a combination of melatonin and infliximab has negative interactions with 2 key genes (Rorc and Fbxo32) upregulated in the transcriptomic signature of cancer cachexia in muscle.

CONCLUSIONS

The integration of machine learning, meta-analysis and literature mining was found to be an efficient approach to identifying a robust transcriptomic signature for cancer cachexia, with implications for improving clinical diagnosis and management of this condition.

Discovery of Novel Isoxazoline Derivatives Containing Diaryl Ether against Fall Armyworms

With the continuous evolution of insect resistance, it is a tremendous challenge to control the fall armyworm (Spodoptera frugiperda) with traditional insecticides. To solve this pending issue, a series of novel isoxazoline derivatives containing diaryl ether structures were designed and synthesized, and most of the target compounds exhibited excellent insecticidal activity. Based on the three-dimensional quantitative structure-activity relationship (3D-QSAR) model analysis, we further optimized the molecular structure with compound L35 obtained and tested for its activity. Compound L35 (LC₅₀ = 1.69 mg/L) exhibited excellent insecticidal activity against S. frugiperda, which was better than those of commercial fipronil (LC₅₀ = 70.78 mg/L) and indoxacarb (LC₅₀ = 5.37 mg/L). The enzyme-linked immunosorbent assay showed that L35 could upregulate the levels of GABA in insects. In addition, molecular docking and transcriptomic results also indicated that compound L35 may affect the nervous system of S. frugiperda by acting on GABA receptors. Notably, through high-performance liquid chromatography (HPLC), we were able to obtain the two enantiomers of compound L35, and the insecticidal activity test revealed that S-(+)-L35 was 44 times more active than R-(-)-L35 against S. frugiperda. This study established the chemistry basis and mechanistic foundations for the future development of pesticide candidates against fall armyworms.

Long-Term Calorie Restriction Alters Anxiety-like Behaviour and the Brain and Adrenal Gland Transcriptomes of the Ageing Male Rat

Further examination of the molecular regulators of long-term calorie restriction (CR), reported to have an anxiolytic effect, may highlight novel therapeutic targets for anxiety disorders. Here, adult male Hooded Wistar rats were exposed to a 25% CR whilst anxiety-like behaviour was assessed at 6-, 12-, and 18-months of age via the elevated plus maze, open field, and acoustic startle tests. Next-generation sequencing was then used to measure transcriptome-wide gene expression in the hypothalamus, amygdala, pituitary, and adrenal glands. Results showed an anxiolytic behavioural profile across early, middle, and late adulthood by CR, with the strongest effects noted at 6-months. Transcriptomic analysis by seven attribute weighting algorithms, including Info Gain Ratio, Rule, Chi Squared, Gini Index, Uncertainty, Relief, and Info Gain, led to the development of a signature of long-term CR, independent of region. Complement C1q A chain (C1qa), an extracellular protein, expression was significantly decreased by CR in most regions examined. Furthermore, text mining highlighted the positive involvement of C1qa in anxiety, depression, neurodegeneration, stress, and ageing, collectively identifying a suitable biomarker candidate for CR. Overall, the current study identified anxiety-related phenotypic changes and a novel transcriptome signature of long-term CR, indicating potential therapeutic targets for anxiety, depression, and neurodegeneration.

Spatial learning and memory impaired after infection of non-neurotropic influenza virus in BALB/c male mice

During the influenza pandemic or seasonal influenza outbreak, influenza infection can cause acute influenza-associated encephalopathy/encephalitis (IAE), even death. Patients with severe IAE will also have severe neurological sequelae. Neurologic disorders have been demonstrated in the mice treated with peripheral influenza viruses infection, whether neurotropic or non-neurotropic viruses. However, previous studies focused on the acute phase of infection, and rarely paid attention to a longer range of observations. Therefore, the long-term effect of non-neurotropic virus infection on the host is not very clear. In this study, adult mice were infected with influenza virus H1N1/PR8. Then, spontaneous behavior, body weight, expression of cytokines in brain, spatial learning ability and spatial memory ability were observed, until the complete recovery period. The results showed that cytokines in the brain were highly expressed in the convalescent phase (14 day post inoculation, dpi), especially BDNF, IBA1, CX3CL1 and CD200 were still highly expressed in the recovery phase (28 dpi). Otherwise the emotional and spatial memory ability of mice were impacted in the convalescent phase (14 dpi) and the recovery phase (28 dpi). In brief, BALB/c mice infected with non-neurotropic influenza virus H1N1, the weight and motor ability decreased in acute stage. During the recovery period, the body weight and activity ability were completely restored, whereas the emotion disordered, and the ability of spatial learning and memory were impacted in the infected mice. This long-term behavior impact may be the lag injury caused by non-neurotropic influenza infection.

Targeting Hemagglutinin: Approaches for Broad Protection against the Influenza A Virus

Influenza A viruses are dynamically epidemic and genetically diverse. Due to the antigenic drift and shift of the virus, seasonal vaccines are required to be reformulated annually to match with current circulating strains. However, the mismatch between vaccinal strains and circulating strains occurs frequently, resulting in the low efficacy of seasonal vaccines. Therefore, several “universal” vaccine candidates based on the structure and function of the hemagglutinin (HA) protein have been developed to meet the requirement of a broad protection against homo-/heterosubtypic challenges. Here, we review recent novel constructs and discuss several important findings regarding the broad protective efficacy of HA-based universal vaccines.

Identifying mutation positions in all segments of influenza genome enables better differentiation between pandemic and seasonal strains

Influenza has a negative sense, single-stranded, and segmented RNA. In the context of pandemic influenza research, most studies have focused on variations in the surface proteins (Hemagglutinin and Neuraminidase). However, new findings suggest that all internal and external proteins of influenza viruses can contribute in pandemic emergence, pathogenicity and increasing host range. The occurrence of the 2009 influenza pandemic and the availability of many external and internal segments of pandemic and non-pandemic sequences offer a unique opportunity to evaluate the performance of machine learning models in discrimination of pandemic from seasonal sequences using mutation positions in all segments. In this study, we hypothesized that identifying mutation positions in all segments (proteins) encoded by the influenza genome would enable pandemic and seasonal strains to be more reliably distinguished. In a large scale study, we applied a range of data mining techniques to all segments of influenza for rule discovery and discrimination of pandemic from seasonal strains. CBA (classification based on association rule mining), Ripper and Decision tree algorithms were utilized to extract association rules among mutations. CBA outperformed the other models. Our approach could discriminate pandemic sequences from seasonal ones with more than 95% accuracy for PA and NP, 99.33% accuracy for NA and 100% accuracy, precision, specificity and sensitivity (recall) for M1, M2, PB1, NS1, and NS2. The values of precision, specificity, and sensitivity were more than 90% for other segments except PB2. If sequences of all segments of one strain were available, the accuracy of discrimination of pandemic strains was 100%. General rules extracted by rule base classification approaches, such as M1-V147I, NP-N334H, NS1-V112I, and PB1-L364I, were able to detect pandemic sequences with high accuracy. We observed that mutations on internal proteins of influenza can contribute in distinguishing the pandemic viruses, similar to the external ones.

Biliverdin administration regulates the microRNA-mRNA expressional network associated with neuroprotection in cerebral ischemia reperfusion injury in rats

Inflammatory response has an important role in the outcome of cerebral ischemia reperfusion injury (CIR). Biliverdin (BV) administration can relieve CIR in rats, but the mechanism remains unknown. The aim of the present study was to explore the expressional network of microRNA (miRNA)-mRNA in CIR rats following BV administration. A rat middle cerebral artery occlusion model with BV treatment was established. After neurobehavior was evaluated by neurological severity scores (NSS), miRNA and mRNA expressional profiles were analyzed by microarray technology from the cerebral cortex subjected to ischemia and BV administration. Then, bioinformatics prediction was used to screen the correlation between miRNA and mRNA, and 20 candidate miRNAs and 33 candidate mRNAs were verified by reverse transcription-quantitative polymerase chain reaction. Furthermore, the regulation relationship between ETS proto-oncogene 1 (Ets1) and miRNA204-5p was examined by luciferase assay. A total of 86 miRNAs were differentially expressed in the BV group compared with the other groups. A total of 10 miRNAs and 26 candidate genes were identified as a core 'microRNA-mRNA' regulatory network that was linked with the functional improvement of BV administration in CIR rats. Lastly, the luciferase assay results confirmed that miRNA204-5p directly targeted Ets1. The present findings suggest that BV administration may regulate multiple miRNAs and mRNAs to improve neurobehavior in CIR rats, by influencing cell proliferation, apoptosis, maintaining ATP homeostasis, and angiogenesis.

Cross-Species Meta-Analysis of Transcriptomic Data in Combination With Supervised Machine Learning Models Identifies the Common Gene Signature of Lactation Process

Lactation, a physiologically complex process, takes place in mammary gland after parturition. The expression profile of the effective genes in lactation has not comprehensively been elucidated. Herein, meta-analysis, using publicly available microarray data, was conducted identify the differentially expressed genes (DEGs) between pre- and post-peak milk production. Three microarray datasets of Rat, Bos Taurus, and Tammar wallaby were used. Samples related to pre-peak (n = 85) and post-peak (n = 24) milk production were selected. Meta-analysis revealed 31 DEGs across the studied species. Interestingly, 10 genes, including MRPS18B, SF1, UQCRC1, NUCB1, RNF126, ADSL, TNNC1, FIS1, HES5 and THTPA, were not detected in original studies that highlights meta-analysis power in biosignature discovery. Common target and regulator analysis highlighted the high connectivity of CTNNB1, CDD4 and LPL as gene network hubs. As data originally came from three different species, to check the effects of heterogeneous data sources on DEGs, 10 attribute weighting (machine learning) algorithms were applied. Attribute weighting results showed that the type of organism had no or little effect on the selected gene list. Systems biology analysis suggested that these DEGs affect the milk production by improving the immune system performance and mammary cell growth. This is the first study employing both meta-analysis and machine learning approaches for comparative analysis of gene expression pattern of mammary glands in two important time points of lactation process. The finding may pave the way to use of publically available to elucidate the underlying molecular mechanisms of physiologically complex traits such as lactation in mammals.

A role for viral infections in Parkinson's etiology?

Despite over 200 years since its first description by James Parkinson, the cause(s) of most cases of Parkinson's disease (PD) are yet to be elucidated. The disparity between the current understanding of PD symptomology and pathology has led to numerous symptomatic therapies, but no strategy for prevention or disease cure. An association between certain viral infections and neurodegenerative diseases has been recognized, but largely ignored or dismissed as controversial, for decades. Recent epidemiological studies have renewed scientific interest in investigating microbial interactions with the central nervous system (CNS). This review examines past and current clinical findings and overviews the potential molecular implications of viruses in PD pathology.

Integration of machine learning and meta-analysis identifies the transcriptomic bio-signature of mastitis disease in cattle

Gram-negative bacteria such as Escherichia coli (E. coli) are assumed to be among the main agents that cause severe mastitis disease with clinical signs in dairy cattle. Rapid detection of this disease is so important in order to prevent transmission to other cows and helps to reduce inappropriate use of antibiotics. With the rapid progress in high-throughput technologies, and accumulation of various kinds of '-omics' data in public repositories, there is an opportunity to retrieve, integrate, and reanalyze these resources to improve the diagnosis and treatment of different diseases and to provide mechanistic insights into host resistance in an efficient way. Meta-analysis is a relatively inexpensive option with good potential to increase the statistical power and generalizability of single-study analysis. In the current meta-analysis research, six microarray-based studies that investigate the transcriptome profile of mammary gland tissue after induced mastitis by E. coli infection were used. This meta-analysis not only reinforced the findings in individual studies, but also several novel terms including responses to hypoxia, response to drug, anti-apoptosis and positive regulation of transcription from RNA polymerase II promoter enriched by up-regulated genes. Finally, in order to identify the small sets of genes that are sufficiently informative in E. coli mastitis, the differentially expressed gene introduced by meta-analysis were prioritized by using ten different attribute weighting algorithms. Twelve meta-genes were detected by the majority of attribute weighting algorithms (with weight above 0.7) as most informative genes including CXCL8 (IL8), NFKBIZ, HP, ZC3H12A, PDE4B, CASP4, CXCL2, CCL20, GRO1(CXCL1), CFB, S100A9, and S100A8. Interestingly, the results have been demonstrated that all of these genes are the key genes in the immune response, inflammation or mastitis. The Decision tree models efficiently discovered the best combination of the meta-genes as bio-signature and confirmed that some of the top-ranked genes -ZC3H12A, CXCL2, GRO, CFB- as biomarkers for E. coli mastitis (with the accuracy 83% in average). This research properly indicated that by combination of two novel data mining tools, meta-analysis and machine learning, increased power to detect most informative genes that can help to improve the diagnosis and treatment strategies for E. coli associated with mastitis in cattle.

Isolation, Characterization, Cryopreservation of Human Amniotic Stem Cells and Differentiation to Osteogenic and Adipogenic Cells

Human stem cells and progenitor cells can be used to treat cancer and replace dysfunctional cells within a tissue or organ. The objective of this study was to identify the appropriate cells type in regenerative medicine and targeted therapy. As an alternative to embryonic and bone marrow stem cells, we examined human amniotic fluid stem cells (hAFSCs), one of the potential source of multipotent stem cells isolated from both cell pellet (using single-stage method), and supernatant of human amniotic fluid. Source of isolation and unique property of the cells emphasize that these cells are one of the promising new tools in therapeutic field. Double sources for isolation and availability of the left over samples in diagnostic laboratory at the same time have less legal and ethical concerns compared with embryonic stem cell studies. Cells were isolated, cultured for 18^th passage for 6 months and characterized using qPCR and flow cytometry. Cells showed good proliferative ability in culture condition. The cells successfully differentiated into the adipogenic and osteogenic lineages. Based on these findings, amniotic fluid can be considered as an appropriate and convenient source of human amniotic fluid stem cells. These cells provide potential tools for therapeutic applications in the field of regenerative medicine. To get a better understanding of crosstalk between Oct4/NANOG with osteogenesis and adipogenesis, we used network analysis based on Common Targets algorithm and Common Regulators algorithm as well as subnetwork discovery based on gene set enrichment. Network analysis highlighted the possible role of MIR 302A and MIR let-7g. We demonstrated the high expression of MIR 302A and low expression of MIR let7g in hAFSCs by qPCR.

Identification of the key regulating genes of diminished ovarian reserve (DOR) by network and gene ontology analysis

Diminished ovarian reserve (DOR) is one of the reasons for infertility that not only affects both older and young women. Ovarian reserve assessment can be used as a new prognostic tool for infertility treatment decision making. Here, up- and down-regulated gene expression profiles of granulosa cells were analysed to generate a putative interaction map of the involved genes. In addition, gene ontology (GO) analysis was used to get insight intol the biological processes and molecular functions of involved proteins in DOR. Eleven up-regulated genes and nine down-regulated genes were identified and assessed by constructing interaction networks based on their biological processes. PTGS2, CTGF, LHCGR, CITED, SOCS2, STAR and FSTL3 were the key nodes in the up-regulated networks, while the IGF2, AMH, GREM, and FOXC1 proteins were key in the down-regulated networks. MIRN101-1, MIRN153-1 and MIRN194-1 inhibited the expression of SOCS2, while CSH1 and BMP2 positively regulated IGF1 and IGF2. Ossification, ovarian follicle development, vasculogenesis, sequence-specific DNA binding transcription factor activity, and golgi apparatus are the major differential groups between up-regulated and down-regulated genes in DOR. Meta-analysis of publicly available transcriptomic data highlighted the high coexpression of CTGF, connective tissue growth factor, with the other key regulators of DOR. CTGF is involved in organ senescence and focal adhesion pathway according to GO analysis. These findings provide a comprehensive system biology based insight into the aetiology of DOR through network and gene ontology analyses.