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Endocrine disrupting compounds (EDCs) are chemicals that negatively impact endocrine system function, with effluent from paper mills one example of this class of chemicals. In Florida, female Eastern mosquitofish (Gambusia holbrooki) have been observed with male secondary sexual characteristics at three paper mill-impacted sites, indicative of EDC exposure, and are still found at one site on the Fenholloway River. The potential impacts that paper mill effluent exposure has on the G. holbrooki endocrine system and the stream ecosystem are unknown. The objective of this study was to use gene expression analysis to determine if exposure to an androgen receptor agonist was occurring and to couple this analysis with in vitro assays to evaluate the presence of androgen and progesterone receptor active chemicals in the Fenholloway River. Focused gene expression analyses of masculinized G. holbrooki from downstream of the Fenholloway River paper mill were indicative of androgen exposure, while genes related to reproduction indicated potential progesterone exposure. Hepatic microarray analysis revealed an increase in the expression of metabolic genes in Fenholloway River fish, with similarities in genes and biological processes compared to G. holbrooki exposed to androgens. Water samples collected downstream of the paper mill and at a reference site indicated that progesterone and androgen receptor active chemicals were present at both sites, which corroborates previous chemical analyses. Results indicate that G. holbrooki downstream of the Fenholloway River paper mill are impacted by a mixture of both androgens and progesterones. This research provides data on the mechanisms of how paper mill effluents in Florida are acting as endocrine disruptors.

Invasive paper wasps such as Polistes dominula are a major pest and problem for biodiversity around the globe. Safe and highly targeted methods for the control of these and other social wasp populations are needed. We attempted to identify potentially-lethal gene targets that could be used on adult paper wasps in a gene silencing or RNA interference (RNAi) approach. Double-stranded RNA (dsRNA) was designed to target genes for which silencing has proven lethal in other insects. dsRNA was provided either orally to foragers or directly injected into the wasps. We also provided the dsRNA unprotected or protected from degradation by gut nucleases in two different forms (lipofectamine and carbon quantum dots). The effects of oral delivery of 22 different gene targets to forager wasps was evaluated. The expression of five different genes was successfully reduced following dsRNA ingestion or injection. These gene targets included the FACT complex subunit spt16 (DRE4) and RNA-binding protein fusilli (FUSILLI), both of which have been previously shown to have potential as lethal targets for pest control in other insects. However, we found no evidence of significant increases in adult wasp mortality following ingestion or injection of dsRNA for these genes when compared with control treatments in our experiments. The methods we used to protect the dsRNA from digestive degradation altered gene expression but similarly did not influence wasp mortality. Our results indicate that while many of the same gene targets can be silenced and induce mortality in other insects, dsRNA and RNAi approaches may not be useful for paper wasp control. [ABSTRACT FROM AUTHOR]

The paper mulberry is one of the multifunctional tree species in agroforestry systems and is also commonly utilized in traditional medicine in China and other Asian countries. However, little is known about its molecular genetics, which hinders research on and exploitation of this valuable resource. To discern the correlation between gene expression and the essential properties of the paper mulberry, we performed a transcriptomics analysis, assembling a total of 37,725 unigenes from 54,638,676 reads generated by RNA-seq. Among these, 22,692 unigenes showed greater than 60% similarity with genes from other species. The lengths of 13,566 annotated unigenes were longer than 1,000 bp. Functional clustering analysis with COG (Cluster of Orthologous Groups) revealed that 17,184 unigenes are primarily involved in transcription, translation, signal transduction, carbohydrate metabolism, secondary metabolism, and energy metabolism. GO (Gene Ontology) annotation suggests enrichment of genes encoding antioxidant activity, transporter activity, biosynthesis, metabolism and stress response, with a total of 30,659 unigenes falling in these categories. KEGG (Kyoto Encyclopedia of Genes and Genomes) metabolic pathway analysis showed that 7,199 unigenes are associated with 119 metabolic pathways. In addition to the basic metabolism, these genes are enriched for plant pathogen interaction, flavonoid metabolism and other secondary metabolic processes. Furthermore, differences in the transcriptomes of leaf, stem and root tissues were analyzed and 7,233 specifically expressed unigenes were identified. This global expression analysis provided novel insights about the molecular mechanisms of the biosynthesis of flavonoid, lignin and cellulose, as well as on the response to biotic and abiotic stresses including the remediation of contaminated soil by the paper mulberry. [ABSTRACT FROM AUTHOR]

The paper mulberry is one of the multifunctional tree species in agroforestry systems and is also commonly utilized in traditional medicine in China and other Asian countries. However, little is known about its molecular genetics, which hinders research on and exploitation of this valuable resource. To discern the correlation between gene expression and the essential properties of the paper mulberry, we performed a transcriptomics analysis, assembling a total of 37,725 unigenes from 54,638,676 reads generated by RNA-seq. Among these, 22,692 unigenes showed greater than 60% similarity with genes from other species. The lengths of 13,566 annotated unigenes were longer than 1,000 bp. Functional clustering analysis with COG (Cluster of Orthologous Groups) revealed that 17,184 unigenes are primarily involved in transcription, translation, signal transduction, carbohydrate metabolism, secondary metabolism, and energy metabolism. GO (Gene Ontology) annotation suggests enrichment of genes encoding antioxidant activity, transporter activity, biosynthesis, metabolism and stress response, with a total of 30,659 unigenes falling in these categories. KEGG (Kyoto Encyclopedia of Genes and Genomes) metabolic pathway analysis showed that 7,199 unigenes are associated with 119 metabolic pathways. In addition to the basic metabolism, these genes are enriched for plant pathogen interaction, flavonoid metabolism and other secondary metabolic processes. Furthermore, differences in the transcriptomes of leaf, stem and root tissues were analyzed and 7,233 specifically expressed unigenes were identified. This global expression analysis provided novel insights about the molecular mechanisms of the biosynthesis of flavonoid, lignin and cellulose, as well as on the response to biotic and abiotic stresses including the remediation of contaminated soil by the paper mulberry. [ABSTRACT FROM AUTHOR]

Biosensors have emerged as a valuable tool with high specificity and sensitivity for fast and reliable detection of hazardous substances in drinking water. Numerous substances have been addressed using synthetic biology approaches. However, many proposed biosensors are based on living, genetically modified organisms and are therefore limited in shelf life, usability and biosafety. We addressed these issues by the construction of an extensible, cell-free biosensor. Storage is possible through freeze drying on paper. Following the addition of an aqueous sample, a highly efficient cell-free protein synthesis (CFPS) reaction is initiated. Specific allosteric transcription factors modulate the expression of ‘superfolder’ green fluorescent protein (sfGFP) depending on the presence of the substance of interest. The resulting fluorescence intensities are analyzed with a conventional smartphone accompanied by simple and cheap light filters. An ordinary differential equitation (ODE) model of the biosensors was developed, which enabled prediction and optimization of performance. With an optimized cell-free biosensor based on the Shigella flexneri MerR transcriptional activator, detection of 6 μg/L Hg(II) ions in water was achieved. Furthermore, a completely new biosensor for the detection of gamma-hydroxybutyrate (GHB), a substance used as date-rape drug, was established by employing the naturally occurring transcriptional repressor BlcR from Agrobacterium tumefaciens. [ABSTRACT FROM AUTHOR]

White birch (Betula papyrifera) is a dominant tree species of the Boreal Forest. Recent studies have shown that it is fairly resistant to heavy metal contamination, specifically to nickel. Knowledge of regulation of genes associated with metal resistance in higher plants is very sketchy. Availability and annotation of the dwarf birch (B. nana) enables the use of high throughout sequencing approaches to understanding responses to environmental challenges in other Betula species such as B. papyrifera. The main objectives of this study are to 1) develop and characterize the B. papyrifera transcriptome, 2) assess gene expression dynamics of B. papyrifera in response to nickel stress, and 3) describe gene function based on ontology. Nickel resistant and susceptible genotypes were selected and used for transcriptome analysis. A total of 208,058 trinity genes were identified and were assembled to 275,545 total trinity transcripts. The transcripts were mapped to protein sequences and based on best match; we annotated the B. papyrifera genes and assigned gene ontology. In total, 215,700 transcripts were annotated and were compared to the published B. nana genome. Overall, a genomic match for 61% transcripts with the reference genome was found. Expression profiles were generated and 62,587 genes were found to be significantly differentially expressed among the nickel resistant, susceptible, and untreated libraries. The main nickel resistance mechanism in B. papyrifera is a downregulation of genes associated with translation (in ribosome), binding, and transporter activities. Five candidate genes associated to nickel resistance were identified. They include Glutathione S–transferase, thioredoxin family protein, putative transmembrane protein and two Nramp transporters. These genes could be useful for genetic engineering of birch trees. [ABSTRACT FROM AUTHOR]

Gene expression is highly impacted by the environment and can be reflective of past events that affected developmental processes. It is therefore expected that gene expression can serve as a signal of a current or future phenotypic traits. In this paper we identify sets of genes, which we call Prognostic Transcriptomic Biomarkers (PTBs), that can predict firmness in Malus domestica (apple) fruits. In apples, all individuals of a cultivar are clones, and differences in fruit quality are due to the environment. The apples transcriptome responds to these differences in environment, which makes PTBs an attractive predictor of future fruit quality. PTBs have the potential to enhance supply chain efficiency, reduce crop loss, and provide higher and more consistent quality for consumers. However, several questions must be addressed. In this paper we answer the question of which of two common modeling approaches, Random Forest or ElasticNet, outperforms the other. We answer if PTBs with few genes are efficient at predicting traits. This is important because we need few genes to perform qPCR, and we answer the question if qPCR is a cost-effective assay as input for PTBs modeled using high-throughput RNA-seq. To do this, we conducted a pilot study using fruit texture in the 'Gala' variety of apples across several postharvest storage regiments. Fruit texture in 'Gala' apples is highly controllable by post-harvest treatments and is therefore a good candidate to explore the use of PTBs. We find that the RandomForest model is more consistent than an ElasticNet model and is predictive of firmness (r2 = 0.78) with as few as 15 genes. We also show that qPCR is reasonably consistent with RNA-seq in a follow up experiment. Results are promising for PTBs, yet more work is needed to ensure that PTBs are robust across various environmental conditions and storage treatments. [ABSTRACT FROM AUTHOR]

Polistes paper wasps are models for understanding conditions that may have characterized the origin of worker and queen castes and, therefore, the origin of paper wasp sociality. Polistes is ''primitively eusocial'' by virtue of having contextdependent caste determination and no morphological differences between castes. Even so, Polistes colonies have a temporal pattern in which most female larvae reared by the foundress become workers, and most reared by workers become future-reproductive gynes. This pattern is hypothesized to reflect development onto two pathways, which may utilize mechanisms that regulate diapause in other insects. Using expressed sequence tags (ESTs) for Polistes metricus we selected candidate genes differentially expressed in other insects in three categories: 1) diapause vs. non-diapause phenotypes and/or worker vs. queen differentiation, 2) behavioral subcastes of worker honey bees, and 3) no a priori expectation of a role in worker/gyne development. We also used a non-targeted proteomics screen to test for peptide/ protein abundance differences that could reflect larval developmental divergence. We found that foundress-reared larvae (putative worker-destined) and worker-reared larvae (putative gyne-destined) differed in quantitative expression of sixteen genes, twelve of which were associated with caste and/or diapause in other insects, and they also differed in abundance of nine peptides/proteins. Some differentially-expressed genes are involved in diapause regulation in other insects, and other differentially-expressed genes and proteins are involved in the insulin signaling pathway, nutrient metabolism, and caste determination in highly social bees. Differential expression of a gene and a peptide encoding hexameric storage proteins is especially noteworthy. Although not conclusive, our results support hypotheses of 1) larval developmental pathway divergence that can lead to caste bias in adults and 2) nutritional differences as the foundation of the pathway divergence. Finally, the differential expression in Polistes larvae of genes and proteins also differentially expressed during queen vs. worker caste development in honey bees may indicate that regulatory mechanisms of caste outcomes share similarities between primitively eusocial and advanced eusocial Hymenoptera. [ABSTRACT FROM AUTHOR]

In this paper, a novel bifunctional cellulase gene cel1 was cloned from Thermoascus aurantiacus by PCR and heterologously expressed in Pichia pastoris GS115. Bioinformatics and other related tools were used to compare the nucleotide homology of target genes, and analyze the signal peptide, transmembrane domain, hydrophilicity, secondary and tertiary structure of proteins. It was concluded that cel1 has similar endoglucanase nucleotide sequences and falls under the GH5 family. It was also found that cel1 has nucleotide sequences similar to glucosidase, which can infer that cel1 may have the properties of glucosidase, indicating that cel1 is multifunctional. At the same time, a part of the nucleotide sequence of the gene was removed to obtain a new gene cel2, and after highly efficient heterologous expression, its specific activity was found to be 2.1 times higher. Its enhancement is related to the exposure of the protein's hollow three-dimensional structure. This paper provides good material for exploring the relationship between the structure of bifunctional enzymes and their functions, which lays a solid foundation for further research and applications, and provides useful insight for gene mining of other novel enzymes. [ABSTRACT FROM AUTHOR]

Multi-layer Complex networks are commonly used for modeling and analysing biological entities. This paper presents the advantage of using COMBO (Combining Multi Bio Omics) to suggest a new role of the chromosomal aberration as a cancer driver factor. Exploiting the heterogeneous multi-layer networks, COMBO integrates gene expression and DNA-methylation data in order to identify complex bilateral relationships between transcriptome and epigenome. We evaluated the multi-layer networks generated by COMBO on different TCGA cancer datasets (COAD, BLCA, BRCA, CESC, STAD) focusing on the effect of a specific chromosomal numerical aberration, broad gain in chromosome 20, on different cancer histotypes. In addition, the effect of chromosome 8q amplification was tested in the same TCGA cancer dataset. The results demonstrate the ability of COMBO to identify the chromosome 20 amplification cancer driver force in the different TCGA Pan Cancer project datasets. [ABSTRACT FROM AUTHOR]

MicroRNAs (miRNAs) are small molecules that play an essential role in regulating gene expression by post-transcriptional gene silencing. Their study is crucial in revealing the fundamental processes underlying pathologies and, in particular, cancer. To date, most studies on miRNA regulation consider the effect of specific miRNAs on specific target mRNAs, providing wet-lab validation. However, few tools have been developed to explain the miRNA-mediated regulation at the protein level. In this paper, the MoPC computational tool is presented, that relies on the partial correlation between mRNAs and proteins conditioned on the miRNA expression to predict miRNA-target interactions in multi-omic datasets. MoPC returns the list of significant miRNA-target interactions and plot the significant correlations on the heatmap in which the miRNAs and targets are ordered by the chromosomal location. The software was applied on three TCGA/CPTAC datasets (breast, glioblastoma, and lung cancer), returning enriched results in three independent targets databases. [ABSTRACT FROM AUTHOR]

Engineering faster cellulose deconstruction is difficult because it is a complex, cooperative, multi-enzyme process. Here we use experimental evolution to select for populations of Trichoderma citrinoviride that deconstruct up to five-fold more cellulose. Ten replicate populations of T. citrinoviride were selected for growth on filter paper by serial culture. After 125 periods of growth and transfer to fresh media, the filter paper deconstruction increased an average of 2.5 fold. Two populations were examined in more detail. The activity of the secreted cellulase mixtures increased more than two-fold relative to the ancestor and the largest increase was in the extracellular β-glucosidase activity. qPCR showed at least 16-fold more transcribed RNA for egl4 (endoglucanase IV gene), cbh1 (cellobiohydrolase I gene) and bgl1 (extracellular β-glucosidase I gene) in selected populations as compared to the ancestor, and earlier peak expressions of these genes. Deep sequencing shows that the regulatory strategies used to alter cellulase secretion differ in the two strains. The improvements in cellulose deconstruction come from earlier expression of all cellulases and increased relative amount of β-glucosidase, but with small increases in the total secreted protein and therefore little increase in metabolic cost. [ABSTRACT FROM AUTHOR]

Aims: To identify differential expression of shorter non-coding RNA (ncRNA) genes associated with autism spectrum disorders (ASD). Background: ncRNA are functional molecules that derive from non-translated DNA sequence. The HUGO Gene Nomenclature Committee (HGNC) have approved ncRNA gene classes with alignment to the reference human genome. One subset is microRNA (miRNA), which are highly conserved, short RNA molecules that regulate gene expression by direct post-transcriptional repression of messenger RNA. Several miRNA genes are implicated in the development and regulation of the nervous system. Expression of miRNA genes in ASD cohorts have been examined by multiple research groups. Other shorter classes of ncRNA have been examined less. A comprehensive systematic review examining expression of shorter ncRNA gene classes in ASD is timely to inform the direction of research. Methods: We extracted data from studies examining ncRNA gene expression in ASD compared with non-ASD controls. We included studies on miRNA, piwi-interacting RNA (piRNA), small NF90 (ILF3) associated RNA (snaR), small nuclear RNA (snRNA), small nucleolar RNA (snoRNA), transfer RNA (tRNA), vault RNA (vtRNA) and Y RNA. The following electronic databases were searched: Cochrane Library, EMBASE, PubMed, Web of Science, PsycINFO, ERIC, AMED and CINAHL for papers published from January 2000 to May 2022. Studies were screened by two independent investigators with a third resolving discrepancies. Data was extracted from eligible papers. Results: Forty-eight eligible studies were included in our systematic review with the majority examining miRNA gene expression alone. Sixty-four miRNA genes had differential expression in ASD compared to controls as reported in two or more studies, but often in opposing directions. Four miRNA genes had differential expression in the same direction in the same tissue type in at least 3 separate studies. Increased expression was reported in miR-106b-5p, miR-155-5p and miR-146a-5p in blood, post-mortem brain, and across several tissue types, respectively. Decreased expression was reported in miR-328-3p in bloods samples. Seven studies examined differential expression from other classes of ncRNA, including piRNA, snRNA, snoRNA and Y RNA. No individual ncRNA genes were reported in more than one study. Six studies reported differentially expressed snoRNA genes in ASD. A meta-analysis was not possible because of inconsistent methodologies, disparate tissue types examined, and varying forms of data presented. Conclusion: There is limited but promising evidence associating the expression of certain miRNA genes and ASD, although the studies are of variable methodological quality and the results are largely inconsistent. There is emerging evidence associating differential expression of snoRNA genes in ASD. It is not currently possible to say whether the reports of differential expression in ncRNA may relate to ASD aetiology, a response to shared environmental factors linked to ASD such as sleep and nutrition, other molecular functions, human diversity, or chance findings. To improve our understanding of any potential association, we recommend improved and standardised methodologies and reporting of raw data. Further high-quality research is required to shine a light on possible associations, which may yet yield important information. [ABSTRACT FROM AUTHOR]

Non-polyadenylated RNA includes a large subset of crucial regulators of RNA expression and constitutes a substantial portion of the transcriptome, playing essential roles in gene regulation. For example, enhancer RNAs are long non-coding RNAs that perform enhancer-like functions, are bi-directionally transcribed, and usually lack polyA tails. This paper presents a novel method, selSeq, that selectively removes mRNA and pre-mRNA from samples enabling the selective sequencing of crucial regulatory elements, including non-polyadenylated RNAs such as long non-coding RNA, enhancer RNA, and non-canonical mRNA. [ABSTRACT FROM AUTHOR]

Gene expression programming (GEP) is one of the most prominent algorithms in function mining. In order to obtain a more accurate function model in configuration parameters-execution efficiency (CP-EE) of map-reduce job in the high-speed railway catenary monitoring system, this paper proposes a novel algorithm, called GEP based on multi-strategy (MS-GEP). Compared to traditional GEP, the proposed algorithm can escape premature convergence and jump out of local optimum. First, an adaptive mutation rate is designed according to the evolutionary generations, population diversity, and individual fitness values. A manual intervention strategy is then proposed to determine whether the algorithm enters the dilemma of local optimum based on the generations of population evolutionary stagnation. Finally, the average quality of the population is changed by randomly replacing individuals, and the ancestral population is traced to change the evolutionary direction. The experimental results on the benchmarks of function mining show that the proposed MS-GEP has better solution quality and higher population diversity than other GEP algorithms. Furthermore, the proposed MS-GEP has higher accuracy on the function model of CP-EE of high-speed railway catenary monitoring system than other commonly used algorithms in the field of function mining. [ABSTRACT FROM AUTHOR]

Increased production of Prostaglandin D2 (PGD2) is linked to development and progression of asthma and allergy. PGD2 is rapidly degraded to its metabolites, which initiate type 2 innate lymphoid cells (ILC2) migration and IL-5/IL-13 cytokine secretion in a PGD2 receptor 2 (DP2)-dependent manner. Blockade of DP2 has shown therapeutic benefit in subsets of asthma patients. Cellular mechanisms of ILC2 activity in response to PGD2 and its metabolites are still unclear. We hypothesized that ILC2 respond non-uniformly to PGD2 metabolites. ILC2s were isolated from peripheral blood of patients with atopic asthma. ILC2s were stimulated with PGD2 and four PGD2 metabolites (Δ12-PGJ2, Δ12-PGD2, 15-deoxyΔ12,14-PGD2, 9α,11β-PGF2) with or without the selective DP2 antagonist fevipiprant. Total RNA was sequenced, and differentially expressed genes (DEG) were identified by DeSeq2. Differential gene expression analysis revealed an upregulation of pro-inflammatory DEGs in ILC2s stimulated with PGD2 (14 DEGs), Δ12-PGD2 (27 DEGs), 15-deoxyΔ12,14-PGD2 (56 DEGs) and Δ12-PGJ2 (136 DEGs), but not with 9α,11β-PGF2. Common upregulated DEGs were i.e. ARG2, SLC43A2, LAYN, IGFLR1, or EPHX2. Inhibition of DP2 via fevipiprant mainly resulted in downregulation of pro-inflammatory genes such as DUSP4, SPRED2, DUSP6, ETV1, ASB2, CD38, ADGRG1, DDIT4, TRPM2, or CD69. DEGs were related to migration and various immune response-relevant pathways such as "chemokine (C-C motif) ligand 4 production", "cell migration", "interleukin-13 production", "regulation of receptor signaling pathway via JAK-STAT", or "lymphocyte apoptotic process", underlining the pro-inflammatory effects of PGD2 metabolite-induced immune responses in ILC2s as well as the anti-inflammatory effects of DP2 inhibition via fevipiprant. Furthermore, PGD2 and metabolites showed distinct profiles in ILC2 activation. Overall, these results expand our understanding of DP2 initiated ILC2 activity. [ABSTRACT FROM AUTHOR]

Noncoding RNAs play a part in many chronic diseases and interact with each other to regulate gene expression. MicroRNA-9-5p (miR9) has been thought to be a potential inhibitor of diabetic cardiomyopathy. Here we examined the role of miR9 in regulating cardiac fibrosis in the context of diabetic cardiomyopathy. We further expanded our studies through investigation of a regulatory circularRNA, circRNA_012164, on the action of miR9. We showed at both the in vivo and in vitro level that glucose induced downregulation of miR9 and upregulation of circRNA_012164 resulted in the subsequent upregulation of downstream fibrotic genes. Further, knockdown of circRNA_012164 shows protective effects in cardiac endothelial cells and reverses increased transcription of genes associated with fibrosis and fibroblast proliferation through a regulatory axis with miR9. This study presents a novel regulatory axis involving noncoding RNA that is evidently important in the development of cardiac fibrosis in diabetic cardiomyopathy. [ABSTRACT FROM AUTHOR]

Traditional differential expression genes (DEGs) identification models have limitations in small sample size datasets because they require meeting distribution assumptions, otherwise resulting high false positive/negative rates due to sample variation. In contrast, tabular data model based on deep learning (DL) frameworks do not need to consider the data distribution types and sample variation. However, applying DL to RNA-Seq data is still a challenge due to the lack of proper labeling and the small sample size compared to the number of genes. Data augmentation (DA) extracts data features using different methods and procedures, which can significantly increase complementary pseudo-values from limited data without significant additional cost. Based on this, we combine DA and DL framework-based tabular data model, propose a model TabDEG, to predict DEGs and their up-regulation/down-regulation directions from gene expression data obtained from the Cancer Genome Atlas database. Compared to five counterpart methods, TabDEG has high sensitivity and low misclassification rates. Experiment shows that TabDEG is robust and effective in enhancing data features to facilitate classification of high-dimensional small sample size datasets and validates that TabDEG-predicted DEGs are mapped to important gene ontology terms and pathways associated with cancer. [ABSTRACT FROM AUTHOR]

In systems biology, the accurate reconstruction of Gene Regulatory Networks (GRNs) is crucial since these networks can facilitate the solving of complex biological problems. Amongst the plethora of methods available for GRN reconstruction, information theory and fuzzy concepts-based methods have abiding popularity. However, most of these methods are not only complex, incurring a high computational burden, but they may also produce a high number of false positives, leading to inaccurate inferred networks. In this paper, we propose a novel hybrid fuzzy GRN inference model called MICFuzzy which involves the aggregation of the effects of Maximal Information Coefficient (MIC). This model has an information theory-based pre-processing stage, the output of which is applied as an input to the novel fuzzy model. In this preprocessing stage, the MIC component filters relevant genes for each target gene to significantly reduce the computational burden of the fuzzy model when selecting the regulatory genes from these filtered gene lists. The novel fuzzy model uses the regulatory effect of the identified activator-repressor gene pairs to determine target gene expression levels. This approach facilitates accurate network inference by generating a high number of true regulatory interactions while significantly reducing false regulatory predictions. The performance of MICFuzzy was evaluated using DREAM3 and DREAM4 challenge data, and the SOS real gene expression dataset. MICFuzzy outperformed the other state-of-the-art methods in terms of F-score, Matthews Correlation Coefficient, Structural Accuracy, and SS_mean, and outperformed most of them in terms of efficiency. MICFuzzy also had improved efficiency compared with the classical fuzzy model since the design of MICFuzzy leads to a reduction in combinatorial computation. [ABSTRACT FROM AUTHOR]

Gene expression sample data, which usually contains massive expression profiles of genes, is commonly used for disease related gene analysis. The selection of relevant genes from huge amount of genes is always a fundamental process in applications of gene expression data. As more and more genes have been detected, the size of gene expression data becomes larger and larger; this challenges the computing efficiency for extracting the relevant and important genes from gene expression data. In this paper, we provide a novel Bi-dimensional Principal Feature Selection (BPFS) method for efficiently extracting critical genes from big gene expression data. It applies the principal component analysis (PCA) method on sample and gene domains successively, aiming at extracting the relevant gene features and reducing redundancies while losing less information. The experimental results on four real-world cancer gene expression datasets show that the proposed BPFS method greatly reduces the data size and achieves a nearly double processing speed compared to the counterpart methods, while maintaining better accuracy and effectiveness., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2022 Hou et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background: Body size, nutrition and socioeconomic position (SEP) in early life have been associated with a wide range of long-term health effects. Epigenetics is one possible mechanism through which these early life exposures can impact later life health. We conducted a systematic review examining the observational evidence for the impact of body size, nutrition and SEP in early life on the epigenome in humans. Methods: This systematic review is registered with the PROSPERO database (registration number: CRD42016050193). Three datasets were simultaneously searched using Ovid and the resulting studies were evaluated by at least two independent reviewers. Studies measuring epigenetic markers either at the same time as, or after, the early life exposure and have a measure of body size, nutrition or SEP in early life (up to 12 years), written in English and from a community-dwelling participants were included. Results: We identified 90 eligible studies. Seventeen of these papers examined more than one early life exposure of interest. Fifty six papers examined body size, 37 nutrition and 17 SEP. All of the included papers examined DNA methylation (DNAm) as the epigenetic marker. Overall there was no strong evidence for a consistent association between these early life variables in DNAm which may be due to the heterogeneous study designs, data collection methods and statistical analyses. Conclusions: Despite these inconclusive results, the hypothesis that the early life environment can impact DNAm, potentially persisting into adult life, was supported by some studies and warrants further investigation. We provide recommendations for future studies. [ABSTRACT FROM AUTHOR]

Background: Amyotrophic lateral sclerosis (ALS) is a relentlessly progressive and fatal neurodegenerative diseases for which at present no cure is available. Despite the extensive research the progress from diagnosis to prognosis in ALS and frontotemporal dementia (FTD) has been slow which represents suboptimal understanding of disease pathophysiological processes. In recent studies, several genes have been associated with the ALS and FTD diseases such as SOD1, TDP43, and TBK1, whereas the hexanucleotide GGGGCC repeat expansion (HRE) in C9orf72 gene is a most frequent cause of ALS and FTD, that has changed the understanding of these diseases. Methods: The goal of this study was to identify and spatially determine differential gene expression signature differences between cerebellum and frontal cortex in C9orf72-associated ALS (C9-ALS), to study the network properties of these differentially expressed genes, and to identify miRNAs targeting the common differentially expressed genes in both the tissues. This study thus highlights underlying differential cell susceptibilities to the disease mechanisms in C9-ALS and suggesting therapeutic target selection in C9-ALS. Results: In this manuscript, we have identified that the genes involved in neuron development, protein localization and transcription are mostly enriched in cerebellum of C9-ALS patients, while the UPR-related genes are enriched in the frontal cortex. Of note, UPR pathway genes were mostly dysregulated both in the C9-ALS cerebellum and frontal cortex. Overall, the data presented here show that defects in normal RNA processing and the UPR pathway are the pathological hallmarks of C9-ALS. Interestingly, the cerebellum showed more strong transcriptome changes than the frontal cortex. Conclusion: Interestingly, the cerebellum region showed more significant transcriptomic changes as compared to the frontal cortex region suggesting its active participation in the disease process. This nuanced understanding may offer valuable insights for the development of targeted therapeutic strategies aimed at mitigating disease progression in C9-ALS. [ABSTRACT FROM AUTHOR]

Single-cell RNA sequencing (scRNA-seq) is a high-throughput experimental technique for studying gene expression at the single-cell level. As a key component of single-cell data analysis, differential expression analysis (DEA) serves as the foundation for all subsequent secondary studies. Despite the fact that biological replicates are of vital importance in DEA process, small biological replication is still common in sequencing experiment now, which may impose problems to current DEA methods. Therefore, it is necessary to conduct a thorough comparison of various DEA approaches under small biological replications. Here, we compare 6 performance metrics on both simulated and real scRNA-seq datasets to assess the adaptability of 8 DEA approaches, with a particular emphasis on how well they function under small biological replications. Our findings suggest that DEA algorithms extended from bulk RNA-seq are still competitive under small biological replicate conditions, whereas the newly developed method DEF-scRNA-seq which is based on information entropy offers significant advantages. Our research not only provides appropriate suggestions for selecting DEA methods under different conditions, but also emphasizes the application value of machine learning algorithms in this field. [ABSTRACT FROM AUTHOR]

Tomato (Solanum lycopersicum) has many epidermal cell outgrowths including conical petal cells and multiple types of trichomes. These include the anther-specific trichome mesh which holds the anthers connate. The R2R3 Myb Subgroup 9 family of transcription factors is involved in development of epidermal cell outgrowths throughout the angiosperms. No previous study has examined all members of this transcription factor family in a single species. All 7 R2R3 Myb Subgroup 9 genes were isolated from tomato. They were ectopically expressed in tobacco to assess their ability to induce epidermal cell outgrowth. Endogenous expression patterns were examined by semi-quantitative RT-PCR at different stages of floral development relative to the development of anther trichomes. We report variation in the degree of epidermal cell outgrowth produced in transgenic tobacco by each ectopically expressed gene. Based on expression profile and ectopic activity, SlMIXTA-2 is likely involved in the production of leaf trichomes. SlMIXTA-2 is expressed most strongly in the leaves, and not expressed in the floral tissue. SlMYB17-2 is the best candidate for the regulation of the anther trichome mesh. SlMYB17-2 is expressed strongly in the floral tissue and produces a clear phenotype of epidermal cell outgrowths when ectopically expressed in tobacco. Analysis of the phenotypes of transgenic plants ectopically expressing all 7 genes has revealed the different extent to which members of the same transcription factor subfamily can induce cellular outgrowth. [ABSTRACT FROM AUTHOR]

Objectives: Muscle health is essential for children's physical development and future health. PPARGC1A gene encode the peroxisome proliferator-activated receptor coactivator 1α which coactivates transcription factors that control mediating skeletal muscle fiber type conversion and skeletal muscle fiber formation. The PPARGC1A rs8192678 Gly/Ser (Gly482Ser) polymorphism was associated with the regulation of skeletal muscle fibre type. This paper aims to explore the association between the PPARGC1A rs8192678 (Gly482Ser) polymorphism and muscle fitness in Chinese schoolchildren. Methods: We detected the distribution of the PPARGC1A rs8192678 (Gly482Ser) polymorphism by DNA typing of saliva samples from untrained Southern Chinese Han children aged 7–12 years. Considering that muscle studies in children cannot use invasive sampling, we analyzed the association between alleles and genotypes with high validity tests of muscle fitness assesment in children(handgrip strength, standing long jump, sit-ups and push-ups). Results: The results showed no significant differences in height, weight or body mass index between the sexes. The grip strength indicators were correlated with age in boys and height and weight in girls. Sit-ups were significantly higher in girls with the PPARGC1A Gly/Gly genotype than in boys, and handgrip strength and standing long jump were significantly lower in girls with the PPARGC1A rs8192678 (Gly482Ser) genotype than in boys. Genetic model analysis showed that the Gly482 allele had a dominant genetic effect on the Gly482 allele is hypothesized to influence the expression of type I fibers in skeletal muscle in girls, while the Ser482 allele affects on type II fibers in girls. The two alleles had little genetic effect on boys. Conclusions: The results suggested the possible association of the PPARGC1A rs8192678 (Gly482Ser) polymorphism on myofibril type-related phenotypes in Han Chinese children in southern China, with a particular impact on girls. [ABSTRACT FROM AUTHOR]

Mapper, a topological algorithm, is frequently used as an exploratory tool to build a graphical representation of data. This representation can help to gain a better understanding of the intrinsic shape of high-dimensional genomic data and to retain information that may be lost using standard dimension-reduction algorithms. We propose a novel workflow to process and analyze RNA-seq data from tumor and healthy subjects integrating Mapper, differential gene expression, and spectral shape analysis. Precisely, we show that a Gaussian mixture approximation method can be used to produce graphical structures that successfully separate tumor and healthy subjects, and produce two subgroups of tumor subjects. A further analysis using DESeq2, a popular tool for the detection of differentially expressed genes, shows that these two subgroups of tumor cells bear two distinct gene regulations, suggesting two discrete paths for forming lung cancer, which could not be highlighted by other popular clustering methods, including t-distributed stochastic neighbor embedding (t-SNE). Although Mapper shows promise in analyzing high-dimensional data, tools to statistically analyze Mapper graphical structures are limited in the existing literature. In this paper, we develop a scoring method using heat kernel signatures that provides an empirical setting for statistical inferences such as hypothesis testing, sensitivity analysis, and correlation analysis. [ABSTRACT FROM AUTHOR]

Background: Several meta-analyses of the relationship between endothelial nitric oxide synthase (eNOS) T-786C gene polymorphism and chronic kidney disease (CKD) have been published. However, the results of these studies were inconsistent, and it is undetermined whether sample sizes are sufficient to reach a definite conclusion. Objective: To elucidate the relationship between T-786C and CKD by combining previous studies with our case-control sample and incorporate trial sequential analysis (TSA) to verify whether the sample size is adequate to draw a definite conclusion. Methods: PubMed and Embase databases were searched for relevant articles on eNOS T-786C and CKD before February 28, 2021. TSA was also incorporated to ascertain a conclusion. A total of 558 hemodialysis cases in the case-control study was recruited from nine dialysis centers in the northern area of Taiwan in 2020. Additionally, 640 healthy subjects of the control group, with estimated glomerular filtration rate (eGFR) ≥ 60 mL/min/1.73 m2, were selected from participants of the annual elderly health examination program at the Tri-Service General Hospital. The functional analysis was based on eQTL data from GTExPortal. Results: After screening with eligibility criteria, 15 papers were included and eventually combined in a meta-analysis. The result of the TSA showed that the sample size for Caucasians was adequate to ascertain the correlation between eNOS T-786C and CKD but was insufficient for Asians. Therefore, we added our case-control samples (n = 1198), though not associated with CKD (odds ratio [OR] = 1.01, 95% confidence interval [CI] = 0.69–1.46), into a meta-analysis, which supported that eNOS T-786C was significantly associated with CKD in Asians (OR = 1.39, 95% CI = 1.04–1.85) by using an adequate cumulative sample size (n = 4572) analyzed by TSA. Data of eQTL from GTEx showed that T-786C with the C minor allele exhibited relatively lower eNOS mRNA expression in whole blood, indicating the hazardous role of eNOS T-786C in CKD. Conclusions: eNOS T-786C genetic polymorphism was of conclusive significance in the association with CKD among Asians in our meta-analysis. Our case-control samples play a decisive role in changing conclusions from indefinite to definite. [ABSTRACT FROM AUTHOR]

Gene expression data has the characteristics of high dimensionality and a small sample size and contains a large number of redundant genes unrelated to a disease. The direct application of machine learning to classify this type of data will not only incur a great time cost but will also sometimes fail to improved classification performance. To counter this problem, this paper proposes a dimension-reduction algorithm based on weighted kernel principal component analysis (WKPCA), constructs kernel function weights according to kernel matrix eigenvalues, and combines multiple kernel functions to reduce the feature dimensions. To further improve the dimensional reduction efficiency of WKPCA, t-class kernel functions are constructed, and corresponding theoretical proofs are given. Moreover, the cumulative optimal performance rate is constructed to measure the overall performance of WKPCA combined with machine learning algorithms. Naive Bayes, K-nearest neighbour, random forest, iterative random forest and support vector machine approaches are used in classifiers to analyse 6 real gene expression dataset. Compared with the all-variable model, linear principal component dimension reduction and single kernel function dimension reduction, the results show that the classification performance of the 5 machine learning methods mentioned above can be improved effectively by WKPCA dimension reduction. [ABSTRACT FROM AUTHOR]

Background: Copy number aberrations (CNAs) in cancer affect disease outcomes by regulating molecular phenotypes, such as gene expressions, that drive important biological processes. To gain comprehensive insights into molecular biomarkers for cancer, it is critical to identify key groups of CNAs, the associated gene modules, regulatory modules, and their downstream effect on outcomes. Methods: In this paper, we demonstrate an innovative use of sparse canonical correlation analysis (sCCA) to effectively identify the ensemble of CNAs, and gene modules in the context of binary and censored disease endpoints. Our approach detects potentially orthogonal gene expression modules which are highly correlated with sets of CNA and then identifies the genes within these modules that are associated with the outcome. Results: Analyzing clinical and genomic data on 1,904 breast cancer patients from the METABRIC study, we found 14 gene modules to be regulated by groups of proximally located CNA sites. We validated this finding using an independent set of 1,077 breast invasive carcinoma samples from The Cancer Genome Atlas (TCGA). Our analysis of 7 clinical endpoints identified several novel and interpretable regulatory associations, highlighting the role of CNAs in key biological pathways and processes for breast cancer. Genes significantly associated with the outcomes were enriched for early estrogen response pathway, DNA repair pathways as well as targets of transcription factors such as E2F4, MYC, and ETS1 that have recognized roles in tumor characteristics and survival. Subsequent meta-analysis across the endpoints further identified several genes through the aggregation of weaker associations. Conclusions: Our findings suggest that sCCA analysis can aggregate weaker associations to identify interpretable and important genes, modules, and clinically consequential pathways. [ABSTRACT FROM AUTHOR]

The inference of gene regulatory networks (GRNs) from expression data is a challenging problem in systems biology. The stochasticity or fluctuations in the biochemical processes that regulate the transcription process poses as one of the major challenges. In this paper, we propose a novel GRN inference approach, named the Probabilistic Extended Petri Net for Gene Regulatory Network (PEPN-GRN), for the inference of gene regulatory networks from noisy expression data. The proposed inference approach makes use of transition of discrete gene expression levels across adjacent time points as different evidence types that relate to the production or decay of genes. The paper examines three variants of the PEPN-GRN method, which mainly differ by the way the scores of network edges are computed using evidence types. The proposed method is evaluated on the benchmark DREAM4 in silico data sets and a real time series data set of E. coli from the DREAM5 challenge. The PEPN-GRN_v3 variant (the third variant of the PEPN-GRN approach) sought to learn the weights of evidence types in accordance with their contribution to the activation and inhibition gene regulation process. The learned weights help understand the time-shifted and inverted time-shifted relationship between regulator and target gene. Thus, PEPN-GRN_v3, along with the inference of network edges, also provides a functional understanding of the gene regulation process. [ABSTRACT FROM AUTHOR]

In gene expression data analysis framework, ultrahigh dimensionality and measurement error are ubiquitous features. Therefore, it is crucial to correct measurement error effects and make variable selection when fitting a regression model. In this paper, we introduce a python package BOOME, which refers to BOOsting algorithm for Measurement Error in binary responses and ultrahigh-dimensional predictors. We primarily focus on logistic regression and probit models with responses, predictors, or both contaminated with measurement error. The BOOME aims to address measurement error effects, and employ boosting procedure to make variable selection and estimation. [ABSTRACT FROM AUTHOR]

Analysis of gene expression data is an attractive topic in the field of bioinformatics, and a typical application is to classify and predict individuals' diseases or tumors by treating gene expression values as predictors. A primary challenge of this study comes from ultrahigh-dimensionality, which makes that (i) many predictors in the dataset might be non-informative, (ii) pairwise dependence structures possibly exist among high-dimensional predictors, yielding the network structure. While many supervised learning methods have been developed, it is expected that the prediction performance would be affected if impacts of ultrahigh-dimensionality were not carefully addressed. In this paper, we propose a new statistical learning algorithm to deal with multi-classification subject to ultrahigh-dimensional gene expressions. In the proposed algorithm, we employ the model-free feature screening method to retain informative gene expression values from ultrahigh-dimensional data, and then construct predictive models with network structures of selected gene expression accommodated. Different from existing supervised learning methods that build predictive models based on entire dataset, our approach is able to identify informative predictors and dependence structures for gene expression. Throughout analysis of a real dataset, we find that the proposed algorithm gives precise classification as well as accurate prediction, and outperforms some commonly used supervised learning methods. [ABSTRACT FROM AUTHOR]

For high-dimensional and massive data sets, traditional centralized gene expression programming (GEP) or improved algorithms lead to increased run-time and decreased prediction accuracy. To solve this problem, this paper proposes a new improved algorithm called distributed function mining for gene expression programming based on fast reduction (DFMGEP-FR). In DFMGEP-FR, fast attribution reduction in binary search algorithms (FAR-BSA) is proposed to quickly find the optimal attribution set, and the function consistency replacement algorithm is given to solve integration of the local function model. Thorough comparative experiments for DFMGEP-FR, centralized GEP and the parallel gene expression programming algorithm based on simulated annealing (parallel GEPSA) are included in this paper. For the waveform, mushroom, connect-4 and musk datasets, the comparative results show that the average time-consumption of DFMGEP-FR drops by 89.09%%, 88.85%, 85.79% and 93.06%, respectively, in contrast to centralized GEP and by 12.5%, 8.42%, 9.62% and 13.75%, respectively, compared with parallel GEPSA. Six well-studied UCI test data sets demonstrate the efficiency and capability of our proposed DFMGEP-FR algorithm for distributed function mining. [ABSTRACT FROM AUTHOR]

Ocular surface mucins are thought to play vital roles in maintaining the homeostasis of the pre-ocular surface tear film. We performed ocular surface tests with impression cytology to assess the expression levels of mucin-related genes on the ocular surface in healthy eyes. In addition, we investigated alterations in mucin-related gene expression secondary to treatment with rebamipide ophthalmic suspension in patients with Sjögren's syndrome-associated dry eyes (SS-DE). Thirty-three healthy individuals (control group) and 13 patients from our hospital with SS-DE were enrolled. Impression cytology was performed using Schirmer's test paper for RNA sampling. The mRNA levels of SAM-pointed domain-containing ETS-like factor (SPDEF), mucin 5AC (MUC5AC), and mucin 16 (MUC16) were determined using a real-time reverse transcription-polymerase chain reaction. The ocular surface test was performed once for the control group, and at baseline as well as 2, 4, 8, and 12 weeks after treatment in the Sjögren's syndrome-associated dry eyes group. mRNA levels of SPDEF, MUC5AC, and MUC16 were not significantly different between the control and SS-DE groups before rebamipide ophthalmic suspension treatment. SPDEF mRNA levels in control subjects were significantly correlated with levels of MUC5AC. Among SS-DE patients, SPDEF mRNA levels were significantly increased at 2, 4, and 8 weeks after treatment compared with baseline levels. MUC16 mRNA levels were significantly decreased from baseline levels at 4 and 8 weeks post-treatment. Ocular surface test using impression cytology is a clinically useful tool for assessing mucous conditions on the ocular surface and can be used to determine the effects of instillation treatment with eye drops that affect mucin production at the ocular surface. [ABSTRACT FROM AUTHOR]

We investigated the functional mechanism of long non-coding small nucleolar host gene 17 (SNHG17) in diffuse large B-cell lymphoma (DLBCL). lncRNAs related to the prognosis of patients with DLBCL were screened to analyze long non-coding small nucleolar host gene 17 (SNHG17) expression in DLBCL and normal tissues, and a nomogram established for predicting DLBCL prognosis. SNHG17 expression in B-cell lymphoma cells was detected using qPCR. The effects of SNHG17 with/without doxorubicin on the proliferation and apoptosis of DoHH2 and Daudi were detected. The effects of combined SNHG17 and doxorubicin were analyzed. The regulatory function of SNHG17 in DLBCL was investigated using a mouse tumor xenotransplantation model. RNA sequencing was used to analyze the signaling pathways involved in SNHG17 knockdown in B-cell lymphoma cell lines. The target relationships among SNHG17, microRNA, and downstream mRNA biomolecules were detected. A higher SNHG17 level predicted a lower survival rate. SNHG17 was highly expressed in DLBCL patient tissues and cell lines. We established a prognostic model containing SNHG17 expression, which could effectively predict the overall survival rate of DLBCL patients. SNHG17 knockdown inhibited the proliferation and induced the apoptosis of B-cell lymphoma cells, and the combination of SNHG17 and doxorubicin had a synergistic effect. SNHG17, miR-34a-5p, and ZESTE gene enhancer homolog 2 (EZH2) had common hypothetical binding sites, and the luciferase reporter assay verified that miR-34a-5p was the direct target of SNHG17, and EZH2 was the direct target of miR-34a-5p. The carcinogenic function of SNHG17 in the proliferation and apoptosis of DLBCL cells was partially reversed by a miR-34a-5p inhibitor. SNHG17 increases EZH2 levels by inhibiting miR-34a-5p. Our findings indicate SNHG17 as critical for promoting DLBCL progression by regulating the EZH2 signaling pathway and sponging miR-34a-5p. These findings provide a new prognostic marker and therapeutic target for the prognosis and treatment of DLBCL. [ABSTRACT FROM AUTHOR]

Introduction: Heme-oxygenase 1 (HMOX1) is a critical stress response gene that catalyzes the multistep oxidation of heme. A GT(n) repeat of variable length in the promoter in has been associated with a wide range of human diseases, including infections. This paper aims to summarise and systematically review associations between the length of the HMOX1 GT(n) promoter and infectious disease in humans. Methods: A search using relevant terms was performed in PubMED and EMBASE through to 15/01/21 identifying all research that studied an association between the HMOX1 GT(n) repeat polymorphism and the incidence and/or outcome of any human infectious disease. Citations were screened for additional studies. Potential studies were screened for inclusion by two authors. Data was extracted on allele frequency, genotype, strength of association, mechanism of genotyping, and potential biases. A narrative review was performed across each type of infection. Results: 1,533 studies were identified in the search, and one via citation screening. Sixteen studies were ultimately included, seven in malaria, three in HIV, three in sepsis, and one each in pneumonia, hepatitis C, and acute respiratory distress syndrome (ARDS). Sample sizes for nearly all studies were small (biggest study, n = 1,646). Allelic definition was different across all included studies. All studies were at some risk of bias. In malaria, three studies suggested that longer alleles were associated with reduced risk of severe malaria, particularly malaria-induced renal dysfunction, with four studies identifying a null association. In sepsis, two studies suggested an association with longer alleles and better outcomes. Conclusions: Despite the importance of HMOX1 in survival from infection, and the association between repeat length and gene expression, the clinical data supporting an association between repeat length and incidence and/or outcome of infection remain inconclusive. [ABSTRACT FROM AUTHOR]

C. albicans is a commensal organism present in the human microbiome of more than 60% of the healthy population. Transition from commensalism to invasive candidiasis may occur after a local or a general failure of host's immune system. This transition to a more virulent phenotype may reside either on the capacity to form hyphae or on an acquired resistance to antifungal drugs. Indeed, overexpression of genes coding drug efflux pumps or adhesins, cell wall proteins facilitating the contact between the fungus and the host, usually marks the virulence profile of invasive Candida spp. In this paper, we compare virulence of two clinical isolates of C. albicans with that of laboratory-induced resistant strains by challenging G. mellonella larvae with these pathogens along with monitoring transcriptional profiles of drug efflux pumps genes CDR1, CDR2, MDR1 and the adhesin genes ALS1 and HWP1. Although both clinical isolates were found resistant to both fluconazole and micafungin they were found less virulent than laboratory-induced resistant strains. An unexpected behavior emerged for the former clinical isolate in which three genes, CDR1, CDR2 and HWP1, usually correlated with virulence, although hyperexpressed, conferred a less aggressive phenotype. On the contrary, in the other isolate, we observed a decreased expression of CDR1, CDR2 and HWP1as well as of MDR1 and ALS1 that may be consistent with the less aggressive performance observed in this strain. These altered gene expressions might directly influence Candida virulence or they might be an epiphenomenon of a vaster rearrangement occurred in these strains during the challenge with the host's environment. An in-deepth comprehension of this scenario could be crucial for developing interventions able to counteract C. albicans invasiveness and lethality., Competing Interests: The authors have declared that no competing interests exist.

The quantitative real time polymerase chain reaction (qPCR) has become a key molecular enabling technology with an immense range of research, clinical, forensic as well as diagnostic applications. Its relatively moderate instrumentation and reagent requirements have led to its adoption by numerous laboratories, including those located in the Arabian world, where qPCR, which targets DNA, and reverse transcription qPCR (RT-qPCR), which targets RNA, are widely used for region-specific biotechnology, agricultural and human genetic studies. However, it has become increasingly apparent that there are significant problems with both the quality of qPCR-based data as well as the transparency of reporting. This realisation led to the publication of the Minimum Information for Publication of Quantitative Real-Time PCR Experiments (MIQE) guidelines in 2009 and their more widespread adoption in the last couple of years. An analysis of the performance of biomedical research in the Arabian world between 2001–2005 suggests that the Arabian world is producing fewer biomedical publications of lower quality than other Middle Eastern countries. Hence we have analysed specifically the quality of RT-qPCR-based peer-reviewed papers published since 2009 from Arabian researchers using a bespoke iOS/Android app developed by one of the authors. Our results show that compliance with 15 essential MIQE criteria was low (median of 40%, range 0–93%) and few details on RNA quality controls (22% compliance), assays design (12%), RT strategies (32%), amplification efficiencies (30%) and the normalisation process (3%). These data indicate that one of the reasons for the poor performance of Arabian world biomedical research may be the low standard of any supporting qPCR experiments and identify which aspects of qPCR experiments require significant improvements. [ABSTRACT FROM AUTHOR]

The process of Nuclei detection in high-grade breast cancer images is quite challenging in the case of image processing techniques due to certain heterogeneous characteristics of cancer nuclei such as enlarged and irregularly shaped nuclei, highly coarse chromatin marginalized to the nuclei periphery and visible nucleoli. Recent reviews state that existing techniques show appreciable segmentation accuracy on breast histopathology images whose nuclei are dispersed and regular in texture and shape; however, typical cancer nuclei are often clustered and have irregular texture and shape properties. This paper proposes a novel segmentation algorithm for detecting individual nuclei from Hematoxylin and Eosin (H&E) stained breast histopathology images. This detection framework estimates a nuclei saliency map using tensor voting followed by boundary extraction of the nuclei on the saliency map using a Loopy Back Propagation (LBP) algorithm on a Markov Random Field (MRF). The method was tested on both whole-slide images and frames of breast cancer histopathology images. Experimental results demonstrate high segmentation performance with efficient precision, recall and dice-coefficient rates, upon testing high-grade breast cancer images containing several thousand nuclei. In addition to the optimal performance on the highly complex images presented in this paper, this method also gave appreciable results in comparison with two recently published methods—Wienert et al. (2012) and Veta et al. (2013), which were tested using their own datasets. [ABSTRACT FROM AUTHOR]

The Guilt-by-Association (GBA) principle, according to which genes with similar expression profiles are functionally associated, is widely applied for functional analyses using large heterogeneous collections of transcriptomics data. However, the use of such large collections could hamper GBA functional analysis for genes whose expression is condition specific. In these cases a smaller set of condition related experiments should instead be used, but identifying such functionally relevant experiments from large collections based on literature knowledge alone is an impractical task. We begin this paper by analyzing, both from a mathematical and a biological point of view, why only condition specific experiments should be used in GBA functional analysis. We are able to show that this phenomenon is independent of the functional categorization scheme and of the organisms being analyzed. We then present a semi-supervised algorithm that can select functionally relevant experiments from large collections of transcriptomics experiments. Our algorithm is able to select experiments relevant to a given GO term, MIPS FunCat term or even KEGG pathways. We extensively test our algorithm on large dataset collections for yeast and Arabidopsis. We demonstrate that: using the selected experiments there is a statistically significant improvement in correlation between genes in the functional category of interest; the selected experiments improve GBA-based gene function prediction; the effectiveness of the selected experiments increases with annotation specificity; our algorithm can be successfully applied to GBA- based pathway reconstruction. Importantly, the set of experiments selected by the algorithm reflects the existing literature knowledge about the experiments. [ABSTRACT FROM AUTHOR]

Drug response prediction is a critical step for personalized treatment of cancer patients and ultimately leads to precision medicine. A lot of machine-learning based methods have been proposed to predict drug response from different types of genomic data. However, currently available methods could only give a “point” prediction of drug response value but fail to provide the reliability and distribution of the prediction, which are of equal interest in clinical practice. In this paper, we proposed a method based on quantile regression forest and applied it to the CCLE dataset. Through the out-of-bag validation, our method achieved much higher prediction accuracy of drug response than other available tools. The assessment of prediction reliability by prediction intervals and its significance in personalized medicine were illustrated by several examples. Functional analysis of selected drug response associated genes showed that the proposed method achieves more biologically plausible results. [ABSTRACT FROM AUTHOR]

Indiscriminate use of antibiotics to treat infections that are of viral origin contributes to unnecessary use which potentially may induce resistance in commensal bacteria. To counteract this a number of host gene transcriptional studies have been conducted to identify genes that are differently expressed during bacterial and viral infections in humans, and thus could be used as a tool to base decisions on the use of antibiotics. In this paper, we aimed to evaluate the potential of a selection of genes that have been considered biomarkers in humans, to differentially diagnose bacterial from viral infections in the pig. First porcine PBMC were induced with six toll-like receptor (TLR) agonists (FliC, LPS, ODN 2216, Pam3CSK4, poly I:C, R848) to mimic host gene expression induced by bacterial or viral pathogens, or exposed to heat-killed Actinobacillus pleuropneumoniae or a split influenza virus. Genes that were differentially expressed between bacterial and viral inducers were further evaluated on clinical material comprising eleven healthy pigs, and six pigs infected with A. pleuropneumoniae. This comprised three virally upregulated genes (IFI44L, MxA, RSAD2) and four bacterially upregulated genes (IL-1β, IL-8, FAM89A, S100PBP). All six infected pigs could be differentially diagnosed to healthy pigs using a host gene transcription assay based on the geometric average of the bacterially induced genes IL-8 and S100PBP over that of the virally induced gene MxA. [ABSTRACT FROM AUTHOR]

Recent high-throughput techniques have generated a flood of biological data in all aspects. The transformation and visualization of multi-dimensional and numerical gene or protein expression data in a single heatmap can provide a concise but comprehensive presentation of molecular dynamics under different conditions. In this work, we developed an easy-to-use tool named HemI (Heat map Illustrator), which can visualize either gene or protein expression data in heatmaps. Additionally, the heatmaps can be recolored, rescaled or rotated in a customized manner. In addition, HemI provides multiple clustering strategies for analyzing the data. Publication-quality figures can be exported directly. We propose that HemI can be a useful toolkit for conveniently visualizing and manipulating heatmaps. The stand-alone packages of HemI were implemented in Java and can be accessed at . [ABSTRACT FROM AUTHOR]

Feature selection and classification are the main topics in microarray data analysis. Although many feature selection methods have been proposed and developed in this field, SVM-RFE (Support Vector Machine based on Recursive Feature Elimination) is proved as one of the best feature selection methods, which ranks the features (genes) by training support vector machine classification model and selects key genes combining with recursive feature elimination strategy. The principal drawback of SVM-RFE is the huge time consumption. To overcome this limitation, we introduce a more efficient implementation of linear support vector machines and improve the recursive feature elimination strategy and then combine them together to select informative genes. Besides, we propose a simple resampling method to preprocess the datasets, which makes the information distribution of different kinds of samples balanced and the classification results more credible. Moreover, the applicability of four common classifiers is also studied in this paper. Extensive experiments are conducted on six most frequently used microarray datasets in this field, and the results show that the proposed methods have not only reduced the time consumption greatly but also obtained comparable classification performance. [ABSTRACT FROM AUTHOR]

Established cell lines are widely used in research, however an appealing question is the comparability of the cells between various laboratories, their characteristics and stability in time. Problematic is also the cell line misidentification, genetic and phenotypic shift or Mycoplasma contamination which are often forgotten in research papers. The monocyte/macrophage-like cell line RAW 264.7 has been one of the most commonly used myeloid cell line for more than 40 years. Despite its phenotypic and functional stability is often discussed in literature or at various scientific discussion panels, their stability during the consecutive passages has not been confirmed in any solid study. So far, only a few functional features of these cells have been studied, for example their ability to differentiate into osteoclasts. Therefore, in the present paper we have investigated the phenotype and functional stability of the RAW 264.7 cell line from passage no. 5 till passage no. 50. We found out that the phenotype (expression of particular macrophage-characteristic genes and surface markers) and functional characteristics (phagocytosis and NO production) of RAW 264.7 cell line remains stable through passages: from passage no. 10 up to passage no. 30. Overall, our results indicated that the RAW 264.7 cell line should not be used after the passage no. 30 otherwise it may influence the data reliability. [ABSTRACT FROM AUTHOR]

This work presents optimal linear quadratic regulator (LQR) based on genetic algorithm (GA) to solve the two degrees of freedom (2 DoF) motion control problem in head seas for wave piercing catamarans (WPC). The proposed LQR based GA control strategy is to select optimal weighting matrices (Q and R). The seakeeping performance of WPC based on proposed algorithm is challenged because of multi-input multi-output (MIMO) system of uncertain coefficient problems. Besides the kinematical constraint problems of WPC, the external conditions must be considered, like the sea disturbance and the actuators (a T-foil and two flaps) control. Moreover, this paper describes the MATLAB and LabVIEW software plats to simulate the reduction effects of WPC. Finally, the real-time (RT) NI CompactRIO embedded controller is selected to test the effectiveness of the actuators based on proposed techniques. In conclusion, simulation and experimental results prove the correctness of the proposed algorithm. The percentage of heave and pitch reductions are more than 18% in different high speeds and bad sea conditions. And the results also verify the feasibility of NI CompactRIO embedded controller. [ABSTRACT FROM AUTHOR]

A wide variety of methods have been proposed in protein subnuclear localization to improve the prediction accuracy. However, one important trend of these means is to treat fusion representation by fusing multiple feature representations, of which, the fusion process takes a lot of time. In view of this, this paper novelly proposed a method by combining a new single feature representation and a new algorithm to obtain good recognition rate. Specifically, based on the position-specific scoring matrix (PSSM), we proposed a new expression, correlation position-specific scoring matrix (CoPSSM) as the protein feature representation. Based on the classic nonlinear dimension reduction algorithm, kernel linear discriminant analysis (KLDA), we added a new discriminant criterion and proposed a dichotomous greedy genetic algorithm (DGGA) to intelligently select its kernel bandwidth parameter. Two public datasets with Jackknife test and KNN classifier were used for the numerical experiments. The results showed that the overall success rate (OSR) with single representation CoPSSM is larger than that with many relevant representations. The OSR of the proposed method can reach as high as 87.444% and 90.3361% for these two datasets, respectively, outperforming many current methods. To show the generalization of the proposed algorithm, two extra standard datasets of protein subcellular were chosen to conduct the expending experiment, and the prediction accuracy by Jackknife test and Independent test is still considerable. [ABSTRACT FROM AUTHOR]

With the advancement of technology, analysis of large-scale data of gene expression is feasible and has become very popular in the era of machine learning. This paper develops an improved ridge approach for the genome regression modeling. When multicollinearity exists in the data set with outliers, we consider a robust ridge estimator, namely the rank ridge regression estimator, for parameter estimation and prediction. On the other hand, the efficiency of the rank ridge regression estimator is highly dependent on the ridge parameter. In general, it is difficult to provide a satisfactory answer about the selection for the ridge parameter. Because of the good properties of generalized cross validation (GCV) and its simplicity, we use it to choose the optimum value of the ridge parameter. The GCV function creates a balance between the precision of the estimators and the bias caused by the ridge estimation. It behaves like an improved estimator of risk and can be used when the number of explanatory variables is larger than the sample size in high-dimensional problems. Finally, some numerical illustrations are given to support our findings. [ABSTRACT FROM AUTHOR]

A closer association has been found between the microRNA-146a rs2910164 polymorphism and the risk of head and neck carcinoma in some molecular epidemiological studies. Recently two meta-analyses were performed to explore the relationship between miRNA-146a polymorphisms and the susceptibility of squamous cell carcinoma of the head and neck (SCCHN); however, they yielded conflicting results in susceptibility regarding ethnic variations. Hence, the present study was performed to explain the relationship between the miRNA-146a rs2910164 polymorphism and the risk of SCCHN development of Chinese patients. We retrieved databases and screened eligible papers up to March 10, 2017 and then we extracted the essential data. The subgroup analyses were also performed based on the tumor site, region, and genotyping means. Crude odds ratios (OR) at 95% confidence intervals (CI) were chosen to describe the strength of the association. As a result, 6 publications were included in our study which involved 8 independent case-control studies. A significant association was found between miR-146a rs2910164 polymorphisms and the risk of SCCHN in Chinese patients according to the overall data [CC+CG vs. GG: OR = 1.13; 95%CI = 1.00–1.29; CC vs. GG: OR = 1.19; 95%CI = 1.03–1.38]. According to the subgroup analysis based on tumor site, the risk of cancer was significantly increased among laryngeal cancer (dominant model: OR = 1.76, 95%CI = 1.26~2.46, P = 0.001; homozygote model: OR = 1.83, 95%CI = 1.25~2.67, P = 0.002) and nasopharyngeal carcinoma (homozygote model: OR = 1.41, 95%CI = 1.05~1.90, P = 0.022). In summary, variant alleles of miR-146a rs2910164 alleles may have an association with the increased risk of SCCHN in Chinese patients, and these associations differed based on tumor site. Further studies including a larger sample size will be necessary to clarify these results. [ABSTRACT FROM AUTHOR]

Organismal complexity broadly relates to the number of different cell types within an organism and generally increases across a phylogeny. Whilst gene expression will underpin organismal complexity, it has long been clear that a simple count of gene number is not a sufficient explanation. In this paper, we use open-access information from the Ensembl databases to quantify the functional diversity of human genes that are broadly involved in transcription. Functional diversity is described in terms of the numbers of paralogues, protein isoforms and structural domains for each gene. The change in functional diversity is then calculated for up to nine orthologues from the nematode worm to human and correlated to the change in cell-type number. Those with the highest correlation are subject to gene ontology term enrichment and interaction analyses. We found that a range of genes that encode proteins associated with dynamic changes to chromatin are good candidates to contribute to organismal complexity. [ABSTRACT FROM AUTHOR]