Splice-disrupt genomic variants in prostate cancer.

BACKGROUND: Splice-disrupt genomic variants are one of the causes of cancer-causing errors in gene expression. Little is known about splice-disrupt genomic variants. METHODS AND RESULTS: Here, pattern of splice-disrupt variants was investigated using 21,842,764 genomic variants in different types of prostate cancer. A particular attention was paid to genomic locations of splice-disrupt variants on target genes. HLA-A in prostate cancer, MSR1 in familial prostate cancer, and EGFR in both castration-resistant prostate cancer and metastatic castration-resistant had the highest allele frequencies of splice-disrupt variations. Some splice-disrupt variants, located on coding sequences of NCOR2, PTPRC, and CRP, were solely present in the advanced metastatic castration-resistant prostate cancer. High-risk splice-disrupt variants were identified based on computationally calculated Polymorphism Phenotyping (PolyPhen), Sorting Intolerant From Tolerant (SIFT), and Genomic Evolutionary Rate Profiling (GERP) + + scores as well as the recorded clinical significance in dbSNP database of NCBI. Functional annotation of damaging splice-disrupt variants highlighted important cancer-associated functions, including endocrine resistance, lipid metabolic process, steroid metabolic process, regulation of mitotic cell cycle, and regulation of metabolic process. This is the first study that profiles the splice-disrupt genomic variants and their target genes in prostate cancer. Literature mining based variant analysis highlighted the importance of rs1800716 variant, located on the CYP2D6 gene, involved in a range of important functions, such as RNA spicing, drug interaction, death, and urotoxicity. CONCLUSIONS: This is the first study that profiles the splice-disrupt genomic variants and their target genes in different types of prostate cancer. Unravelling alternative splicing opens a new avenue towards the establishment of new diagnostic and prognostic markers for prostate cancer progression and metastasis.

MicroRNA-based regulatory circuit involved in sperm infertility.

miRNAs (MicroRNAs), known as noncoding and important endogenous factors regulating the expression protein-coding genes, are vital regulators in each biological process. Thus, this study aims to explore the key role of four microRNAs in regulating the spermatogenesis. To conduct this experiment, 55 infertile and fertile men provided the study with the sperm and testicular tissue samples. To study the spermatozoa in terms of the morphology, Diff-Quick was applied. Then, quantitative real-time polymerase chain reaction (RT-PCR) was conducted on samples. Our data indicated that in contrast to the miR-15b, significant increasing of miR-383 and miR-122 occurred in both severe oligoasthenoteratozoospermia (SOAT) and moderate oligoasthenoteratozoospermia (MOAT) compared to normal sperm group (N). In addition, it was observed that miR-15b and miR-122 increased in patients with nonobstructive azoospermia (NOA) compared with obstructive azoospermia (OA) group. Expression levels of target genes including P53, CASPASE-9 and CYCLIN D1 underwent principle changes according to miRNAs expression level. Our finding indicated that miRNAs had essential role in the regulation of spermatogenesis, and their expression altering was associated with sperm abnormalities. Thus, microRNAs can be introduced as useful biomarkers to determine male infertility reasons to choose the effective treatment.

Non-coding and coding genomic variants distinguish prostate cancer, castration-resistant prostate cancer, familial prostate cancer, and metastatic castration-resistant prostate cancer from each other.

A considerable number of deposited variants has provided new possibilities for knowledge discovery in different types of prostate cancer. Here, we analyzed variants located on 3'UTR, 5'UTR, CDs, Intergenic, and Intronic regions in castration-resistant prostate cancer (8496 variants), familial prostate cancer (3241 variants), metastatic castration-resistant prostate cancer (3693 variants), and prostate cancer (16599 variants). Chromosome regions 10p15-p14 and 2p13 were highly enriched (P < 0.00001) for variants located in 3'UTR, 5'UTR, CDs, intergenic, and intronic regions in castration-resistant prostate cancer. In contrast, 10p15-p14, 10q23.3, 12q13.11, 13q12.3, 1q25, and 8p22 regions were enriched (P < 0.001) in familial prostate cancer. In metastatic castration-resistant prostate cancer, 10p15-p14, 10q23.3, 11q22-q23, 14q21.1, and 14q32.13 were highly variant regions (P < 0.001). Chromosome 2 and chromosome 1 hosted many enriched variant regions. AKR1C3, BRCA1, BRCA2, CHGA, CYP19A1, HOXB13, KLK3, and PTEN contained the highest number of 3'UTR, 5'UTR, CDs, Intergenic, and Intronic variants. Network analysis showed that these genes are upstream of important functions including prostate gland development, tumor recurrence, prostate cancer-specific survival, tumor progression, cancer mortality, long-term survival, cancer recurrence, angiogenesis, and AR. Interestingly, all of EGFR, JAK2, NR3C1, PDZD2, and SEMA3C genes had single nucleotide polymorphisms (SNP) in castration-resistant prostate cancer, consistent with high selection pressure on these genes during drug treatment and consequent resistance. High occurrence of variants in 3'UTRs suggests the importance of regulatory variants in different types of prostate cancer; an area that has been neglected compared with coding variants. This study provides a comprehensive overview of genomic regions contributing to different types of prostate cancer.

Novel approach for identification of influenza virus host range and zoonotic transmissible sequences by determination of host-related associative positions in viral genome segments.

BACKGROUND: Recent (2013 and 2009) zoonotic transmission of avian or porcine influenza to humans highlights an increase in host range by evading species barriers. Gene reassortment or antigenic shift between viruses from two or more hosts can generate a new life-threatening virus when the new shuffled virus is no longer recognized by antibodies existing within human populations. There is no large scale study to help understand the underlying mechanisms of host transmission. Furthermore, there is no clear understanding of how different segments of the influenza genome contribute in the final determination of host range. METHODS: To obtain insight into the rules underpinning host range determination, various supervised machine learning algorithms were employed to mine reassortment changes in different viral segments in a range of hosts. Our multi-host dataset contained whole segments of 674 influenza strains organized into three host categories: avian, human, and swine. Some of the sequences were assigned to multiple hosts. In point of fact, the datasets are a form of multi-labeled dataset and we utilized a multi-label learning method to identify discriminative sequence sites. Then algorithms such as CBA, Ripper, and decision tree were applied to extract informative and descriptive association rules for each viral protein segment. RESULT: We found informative rules in all segments that are common within the same host class but varied between different hosts. For example, for infection of an avian host, HA14V and NS1230S were the most important discriminative and combinatorial positions. CONCLUSION: Host range identification is facilitated by high support combined rules in this study. Our major goal was to detect discriminative genomic positions that were able to identify multi host viruses, because such viruses are likely to cause pandemic or disastrous epidemics.

Root and shoot parts of strawberry: factories for production of functional human pro-insulin.

Diabetes, a disease caused by excessive blood sugar, is caused by the lack of insulin. For commercial production, insulin is made in bacteria or yeast by protein recombinant technology. The focus of this research is evaluating another resource and producing of recombinant insulin protein in as strawberry as this plant has high potential in production of pharmaceutical proteins. Strawberry is a suitable bioreactor for production of recombinant proteins especially edible vaccines. In this research, human pro-insulin gene was cloned in pCAMBIA1304 vector under CaMV35S promoter and NOS terminator. Agrobacterium tumefaciens LBA4404, AGL1, EHA105, EHA101, C58, C58 (pGV2260) and C58 (pGV3101) strains were used for transformation of pro-insulin gene into strawberry cv. Camarosa, Selva, Sarian Hybrid, Pajaro, Paros, Gaviota, Alpine. Additionally, Agrobacterium rhizogenes K599, R1000, A4 and MSU440 strains were utilized for gene transformation into hairy roots. PCR analysis indicated the presence of transformed human pro-insulin gene in the strawberry and hairy roots. Also, its transcription was confirmed using RT-PCR. Furthermore, the analysis of plants, fruits and hairy roots at the level of proteins using dot blot, ELISA, SDS-PAGE and ECL tests re-confirmed the expression of this protein in the transgenic plants as well as hairy roots. Protein purification of human pro-insulin from transgenic tissues was performed using affinity chromatography. Finally, the bioassay of recombinant pro-insulin was performed. The analysis of second generations of transgenic plants (T1) at DNA and protein levels was also performed as a complementary experiment. This study opens a new avenue in molecular farming of human pro-insulin through its mass production in roots and shoots of strawberry.

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.

A Panel of miRNA Biomarkers Common to Serum and Brain-Derived Extracellular Vesicles Identified in Mouse Model of Amyotrophic Lateral Sclerosis.

Amyotrophic lateral sclerosis (ALS) is a progressive motor neuron disease characterised by the deposition of aggregated proteins including TAR DNA-binding protein 43 (TDP-43) in vulnerable motor neurons and the brain. Extracellular vesicles (EVs) facilitate the spread of neurodegenerative diseases and can be easily accessed in the bloodstream. This study aimed to identify a panel of EV miRNAs that can capture the pathology occurring in the brain and peripheral circulation. EVs were isolated from the cortex (BDEVs) and serum (serum EVs) of 3 month-old and 6-month-old TDP-43*Q331K and TDP-43*WT mice. Following characterisation and miRNA isolation, the EVs underwent next-generation sequencing where 24 differentially packaged miRNAs were identified in the TDP-43*Q331K BDEVs and 7 in the TDP-43*Q331K serum EVs. Several miRNAs, including miR-183-5p, were linked to ALS. Additionally, miR-122-5p and miR-486b-5p were identified in both panels, demonstrating the ability of the serum EVs to capture the dysregulation occurring in the brain. This is the first study to identify miRNAs common to both the serum EVs and BDEVs in a mouse model of ALS.

Gut Microbiota and Behavioural Issues in Production, Performance, and Companion Animals: A Systematic Review.

The literature has identified poor nutrition as the leading factor in the manifestation of many behavioural issues in animals, including aggression, hyperalertness, and stereotypies. Literature focused on all species of interest consistently reported that although there were no significant differences in the richness of specific bacterial taxa in the microbiota of individual subjects with abnormal behaviour (termed alpha diversity), there was variability in species diversity between these subjects compared to controls (termed beta diversity). As seen in humans with mental disorders, animals exhibiting abnormal behaviour often have an enrichment of pro-inflammatory and lactic acid-producing bacteria and a reduction in butyrate-producing bacteria. It is evident from the literature that an association exists between gut microbiota diversity (and by extension, the concurrent production of microbial metabolites) and abnormal behavioural phenotypes across various species, including pigs, dogs, and horses. Similar microbiota population changes are also evident in human mental health patients. However, there are insufficient data to identify this association as a cause or effect. This review provides testable hypotheses for future research to establish causal relationships between gut microbiota and behavioural issues in animals, offering promising potential for the development of novel therapeutic and/or preventative interventions aimed at restoring a healthy gut-brain-immune axis to mitigate behavioural issues and, in turn, improve health, performance, and production in animals.

Crosstalk between short- and long-term calorie restriction transcriptomic signatures with anxiety-like behavior, aging, and neurodegeneration: implications for drug repurposing.

Calorie restriction (CR) is considered an effective intervention for anxiety, aging, and obesity. We investigated the effects of short- and long-term CR on behavior as well as transcriptome profiles in the hypothalamus, amygdala, prefrontal cortex, pituitary, and adrenal glands of Hooded Wistar and Long Evans male rats. A reduction in anxiety-like behavior, as assessed via the elevated plus maze, was observed in both short- and long-term CR. Despite this, short- and long-term CR regulated different sets of genes, leading to distinct transcriptomic signatures. The employed models were able to simultaneously analyze categorical and numerical variables, evaluating the effect of tissue type along with expression data. In all tissues, transcription factors, zinc finger protein 45-like and zinc finger BTB domain-containing two, were the top selected genes by the models in short and long-term CR treatments, respectively. Text mining identified associations between genes of the short-term CR signature and neurodegeneration, stress, and obesity and between genes of the long-term signature and the nervous system. Literature mining-based drug repurposing showed that alongside known CR mimetics such as resveratrol and rapamycin, candidates not typically associated with CR mimetics may be repurposed based on their interaction with transcriptomic signatures of CR. This study goes some way to unravelling the global effects of CR and opens new avenues for treatment for emotional disorders, neurodegeneration, and obesity.

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.