Use and misuse of the gene ontology annotations

The transcriptome landscape of developing barley seeds

Cereal grains are an important source of food and feed. To provide comprehensive spatiotemporal information about biological processes in developing seeds of cultivated barley (Hordeum vulgare L. subsp. vulgare), we performed a transcriptomic study of the embryo, endosperm, and seed maternal tissues collected from grains 4-32 days after pollination. Weighted gene co-expression network and motif enrichment analyses identified specific groups of genes and transcription factors (TFs) potentially regulating barley seed tissue development. We defined a set of tissue-specific marker genes and families of TFs for functional studies of the pathways controlling barley grain development. Assessing selected groups of chromatin regulators revealed that epigenetic processes are highly dynamic and likely play a major role during barley endosperm development. The repressive H3K27me3 modification is globally reduced in endosperm tissues and at specific genes related to development and storage compounds. Altogether, this atlas uncovers the complexity of developmentally regulated gene expression in developing barley grains.

Transcriptome sequencing of the endangered land snail Karaftohelix adamsi from the Island Ulleung: De novo assembly, annotation, valuation of fitness genes and SSR markers

BACKGROUND

The Bradybaenidae snail Karaftohelix adamsi is endemic to Korea, with the species tracked from Island Ulleung in North Gyeongsang Province of South Korea. K. adamsi has been classified under the Endangered Wildlife Class II species of Korea and poses a severe risk of extinction following habitat disturbances. With no available information at the DNA (genome) or mRNA (transcriptome) level for the species, conservation by utilizing informed molecular resources seems difficult.

OBJECTIVE

In this study, we used the Illumina short-read sequencing and Trinity de novo assembly to draft the reference transcriptome of K. adamsi.

RESULTS

After assembly, 13,753 unigenes were obtained of which 10,511 were annotated to public databases (a maximum of 10,165 unigenes found homologs in PANM DB). A total of 6,351, 3,535, 358, and 3,407 unigenes were ascribed to the functional categories under KOG, GO, KEGG, and IPS, respectively. The transcripts such as the HSP 70, aquaporin, TLR, and MAPK, among others, were screened as putative functional resources for adaptation. DNA transposons were found to be thickly populated in comparison to retrotransposons in the assembled unigenes. Further, 2,164 SSRs were screened with the promiscuous presence of dinucleotide repeats such as AC/GT and AG/CT.

CONCLUSION

The transcriptome-guided discovery of molecular resources in K. adamsi will not only serve as a basis for functional genomics studies but also provide sustainable tools to be utilized for the protection of the species in the wild. Moreover, the development of polymorphic SSRs is valuable for the identification of species from newer habitats and cross-species genotyping.

De novo assembly of transcriptomes and differential gene expression analysis using short-read data from emerging model organisms - a brief guide

Many questions in biology benefit greatly from the use of a variety of model systems. High-throughput sequencing methods have been a triumph in the democratization of diverse model systems. They allow for the economical sequencing of an entire genome or transcriptome of interest, and with technical variations can even provide insight into genome organization and the expression and regulation of genes. The analysis and biological interpretation of such large datasets can present significant challenges that depend on the 'scientific status' of the model system. While high-quality genome and transcriptome references are readily available for well-established model systems, the establishment of such references for an emerging model system often requires extensive resources such as finances, expertise and computation capabilities. The de novo assembly of a transcriptome represents an excellent entry point for genetic and molecular studies in emerging model systems as it can efficiently assess gene content while also serving as a reference for differential gene expression studies. However, the process of de novo transcriptome assembly is non-trivial, and as a rule must be empirically optimized for every dataset. For the researcher working with an emerging model system, and with little to no experience with assembling and quantifying short-read data from the Illumina platform, these processes can be daunting. In this guide we outline the major challenges faced when establishing a reference transcriptome de novo and we provide advice on how to approach such an endeavor. We describe the major experimental and bioinformatic steps, provide some broad recommendations and cautions for the newcomer to de novo transcriptome assembly and differential gene expression analyses. Moreover, we provide an initial selection of tools that can assist in the journey from raw short-read data to assembled transcriptome and lists of differentially expressed genes.

Changes in Daily Apparent Diffusion Coefficient on Fully Quantitative Magnetic Resonance Imaging Correlate With Established Genomic Pathways of Radiation Sensitivity and Reveal Novel Biologic Associations

PURPOSE

Changes in quantitative magnetic resonance imaging (qMRI) are frequently observed during chemotherapy or radiation therapy (RT). It is hypothesized that qMRI features are reflective of underlying tissue responses. It's unknown what underlying genomic characteristics underly qMRI changes. We hypothesized that qMRI changes may correlate with DNA damage response (DDR) capacity within human tumors. Therefore, we designed the current study to correlate qMRI changes from daily RT treatment with underlying tumor transcriptomic profiles.

METHODS AND MATERIALS

Study participants were prospectively enrolled (National Clinical Trial 03500081). RNA expression levels for 757 genes from pretreatment biopsies were obtained using a custom panel that included signatures of radiation sensitivity and DDR. Daily qMRI data were obtained from a 1.5 Tesla MR linear accelerator. Using these images, d-slow, d-star, perfusion, and apparent diffusion coefficient-mean values in tumors were plotted per-fraction, over time, and associated with genomic pathways.

RESULTS

A total of 1022 qMRIs were obtained from 39 patients and both genomic data and qMRI data from 27 total patients. For 20 of those patients, we also generated normal tissue transcriptomic data. Radio sensitivity index values most closely associated with tissue of origin. Multiple genomic pathways including DNA repair, peroxisome, late estrogen receptor responses, KRAS signaling, and UV response were significantly associated with qMRI feature changes (P < .001).

CONCLUSIONS

Genomic pathway associations across metabolic, RT sensitivity, and DDR pathways indicate common tumor biology that may correlate with qMRI changes during a course of treatment. Such data provide hypothesis-generating novel mechanistic insight into the biologic meaning of qMRI changes during treatment and enable optimal selection of imaging biomarkers for biologically MR-guided RT.

Critical steps in an environmental metaproteomics workflow

Environmental metaproteomics is a rapidly advancing field that provides insights into the structure, dynamics, and metabolic activity of microbial communities. As the field is still maturing, it lacks consistent workflows, making it challenging for non-expert researchers to navigate. This review aims to introduce the workflow of environmental metaproteomics. It outlines the standard practices for sample collection, processing, and analysis, and offers strategies to overcome the unique challenges presented by common environmental matrices such as soil, freshwater, marine environments, biofilms, sludge, and symbionts. The review also highlights the bottlenecks in data analysis that are specific to metaproteomics samples and provides suggestions for researchers to obtain high-quality datasets. It includes recent benchmarking studies and descriptions of software packages specifically built for metaproteomics analysis. The article is written without assuming the reader's familiarity with single-organism proteomic workflows, making it accessible to those new to proteomics or mass spectrometry in general. This primer for environmental metaproteomics aims to improve accessibility to this exciting technology and empower researchers to tackle challenging and ambitious research questions. While it is primarily a resource for those new to the field, it should also be useful for established researchers looking to streamline or troubleshoot their metaproteomics experiments.

Predicting Protein Functions Based on Heterogeneous Graph Attention Technique

In bioinformatics, protein function prediction stands as a fundamental area of research and plays a crucial role in addressing various biological challenges, such as the identification of potential targets for drug discovery and the elucidation of disease mechanisms. However, known functional annotation databases usually provide positive experimental annotations that proteins carry out a given function, and rarely record negative experimental annotations that proteins do not carry out a given function. Therefore, existing computational methods based on deep learning models focus on these positive annotations for prediction and ignore these scarce but informative negative annotations, leading to an underestimation of precision. To address this issue, we introduce a deep learning method that utilizes a heterogeneous graph attention technique. The method first constructs a heterogeneous graph that covers the protein-protein interaction network, ontology structure, and positive and negative annotation information. Then, it learns embedding representations of proteins and ontology terms by using the heterogeneous graph attention technique. Finally, it leverages these learned representations to reconstruct the positive protein-term associations and score unobserved functional annotations. It can enhance the predictive performance by incorporating these known limited negative annotations into the constructed heterogeneous graph. Experimental results on three species (i.e., Human, Mouse, and Arabidopsis) demonstrate that our method can achieve better performance in predicting new protein annotations than state-of-the-art methods.

Epigenomic tomography for probing spatially defined chromatin state in the brain

Spatially resolved epigenomic profiling is critical for understanding biology in the mammalian brain. Single-cell spatial epigenomic assays were developed recently for this purpose, but they remain costly and labor intensive for examining brain tissues across substantial dimensions and surveying a collection of brain samples. Here, we demonstrate an approach, epigenomic tomography, that maps spatial epigenomes of mouse brain at the scale of centimeters. We individually profiled neuronal and glial fractions of mouse neocortex slices with 0.5 mm thickness. Tri-methylation of histone 3 at lysine 27 (H3K27me3) or acetylation of histone 3 at lysine 27 (H3K27ac) features across these slices were grouped into clusters based on their spatial variation patterns to form epigenomic brain maps. As a proof of principle, our approach reveals striking dynamics in the frontal cortex due to kainic-acid-induced seizure, linked with transmembrane ion transporters, exocytosis of synaptic vesicles, and secretion of neurotransmitters. Epigenomic tomography provides a powerful and cost-effective tool for characterizing brain disorders based on the spatial epigenome.

vissE: a versatile tool to identify and visualise higher-order molecular phenotypes from functional enrichment analysis

Functional analysis of high throughput experiments using pathway analysis is now ubiquitous. Though powerful, these methods often produce thousands of redundant results owing to knowledgebase redundancies upstream. This scale of results hinders extensive exploration by biologists and can lead to investigator biases due to previous knowledge and expectations. To address this issue, we present vissE, a flexible network-based analysis and visualisation tool that organises information into semantic categories and provides various visualisation modules to characterise them with respect to the underlying data, thus providing a comprehensive view of the biological system. We demonstrate vissE's versatility by applying it to three different technologies: bulk, single-cell and spatial transcriptomics. Applying vissE to a factor analysis of a breast cancer spatial transcriptomic data, we identified stromal phenotypes that support tumour dissemination. Its adaptability allows vissE to enhance all existing gene-set enrichment and pathway analysis workflows, empowering biologists during molecular discovery.

A proteomic approach to investigate the role of the MECP2 gene mutation in Rett syndrome redox regulatory pathways

Mutations in the X-linked methyl-CpG-binding 2 (MECP2) gene lead to Rett Syndrome (RTT; OMIM 312750), a devasting neurodevelopmental disorder. RTT clinical manifestations are complex and with different degrees of severity, going from autistic-like behavior to loss of acquired speech, motor skills and cardiac problems. Furthermore, the correlation between the type of MECP2 mutation and the clinical phenotype is still not fully understood. Contextually, different genotypes can differently affect the patient's phenotype and omics methodologies such as proteomics could be an important tool for a molecular characterization of genotype/phenotype correlation. The aim of our study was focused on evaluating RTT oxidative stress (OS) responses related to specific MECP2 gene mutations by using proteomics and bioinformatics approaches. Primary fibroblasts isolated from patients affected by R133C and R255× mutations were compared to healthy controls (HC). After clustering primary dermal fibroblasts based on their specific MECP2 mutations, fibroblast-derived protein samples were qualitative and quantitative analyzed, using a label free quantification (LFQ) analysis by mass spectrometry (MS), achieving a preliminary correlation for RTT genotype/phenotype. Among the identified proteins involved in redox regulation pathways, NAD(P)H:quinone acceptor oxidoreductase 1 (NQO1) was found to be absent in R255× cells, while it was present in R133C and in HC fibroblasts. Moreover, NQO1 aberrant gene regulation was also confirmed when cells were challenged with 100 μM hydrogen peroxide (H2O2). In conclusion, by employing a multidisciplinary approach encompassing proteomics and bioinformatics analyses, as well as molecular biology assays, the study uncovered phenotypic responses linked to specific MECP2 gene mutations. These findings contribute to a better understanding of the complexity of RTT molecular pathways, confirming the high heterogeneity among the patients.

Learning sequence, structure, and function representations of proteins with language models

The sequence-structure-function relationships that ultimately generate the diversity of extant observed proteins is complex, as proteins bridge the gap between multiple informational and physical scales involved in nearly all cellular processes. One limitation of existing protein annotation databases such as UniProt is that less than 1% of proteins have experimentally verified functions, and computational methods are needed to fill in the missing information. Here, we demonstrate that a multi-aspect framework based on protein language models can learn sequence-structure-function representations of amino acid sequences, and can provide the foundation for sensitive sequence-structure-function aware protein sequence search and annotation. Based on this model, we introduce a multi-aspect information retrieval system for proteins, Protein-Vec, covering sequence, structure, and function aspects, that enables computational protein annotation and function prediction at tree-of-life scales.

Protective effect and mechanism of procyanidin B2 against hypoxic injury of cardiomyocytes

Background

Cardiomyocyte ischemia and hypoxia are important causes of oxidative stress damage and cardiomyocyte apoptosis in coronary heart disease (CHD). Epidemiological investigation has shown that eating more plant-based foods, such as vegetables and fruits, may significantly decrease the risk of CHD. As natural antioxidants, botanicals have fewer toxic side effects than chemical drugs and have great potential for development. Procyanidin B2 (PB2) is composed of flavan-3-ol and epicatechin and has been reported to have antioxidant and anti-inflammatory effects. However, whether PB2 exerts protective effects on hypoxic cardiomyocytes has remained unclear. This study aimed to explore the protective effect of PB2 against cardiomyocyte hypoxia and to provide new treatment strategies and ideas for myocardial ischemia and hypoxia in CHD.

Methods and results

A hypoxic cardiomyocyte model was constructed, and a CCK-8 assay proved that PB2 had a protective effect on cardiomyocytes in a hypoxic environment. DCFH fluorescence staining, DHE staining, and BODIPY lipid oxidation assessment revealed that PB2 reduced the oxidative stress levels of cardiomyocytes under hypoxic conditions. TUNEL staining, Annexin V/PI fluorescence flow cytometry, and Western blot analysis of the expression of the apoptosis marker protein cleaved caspase-3 confirmed that PB2 reduced cardiomyocyte apoptosis under hypoxic conditions. JC-1 staining indicated that PB2 reduced the mitochondrial membrane potential of cardiomyocytes under hypoxia. In addition, transcriptomic analysis proved that the expression of 158 genes in cardiomyocytes was significantly changed after PB2 was added during hypoxia, of which 53 genes were upregulated and 105 genes were downregulated. GO enrichment analysis demonstrated that the activity of cytokines, extracellular matrix proteins and other molecules was changed significantly in the biological process category. KEGG enrichment analysis showed that the IL-17 signaling pathway and JAK-STAT signaling pathway underwent significant changes. We also performed metabolomic analysis and found that the levels of 51 metabolites were significantly changed after the addition of PB2 to cardiomyocytes during hypoxia. Among them, 39 metabolites exhibited increased levels, while 12 metabolites exhibited decreased levels. KEGG enrichment analysis showed that cysteine and methionine metabolism, arginine and proline metabolism and other metabolic pathways underwent remarkable changes.

Conclusion

This study proves that PB2 can reduce the oxidative stress and apoptosis of cardiomyocytes during hypoxia to play a protective role. Transcriptomic and metabolomic analyses preliminarily revealed signaling pathways and metabolic pathways that are related to its protective mechanism. These findings lay a foundation for further research on the role of PB2 in the treatment of CHD and provide new ideas and new perspectives for research on PB2 in the treatment of other diseases.

A novel approach for predicting upstream regulators (PURE) that affect gene expression

External factors such as exposure to a chemical, drug, or toxicant (CDT), or conversely, the lack of certain chemicals can cause many diseases. The ability to identify such causal CDTs based on changes in the gene expression profile is extremely important in many studies. Furthermore, the ability to correctly infer CDTs that can revert the gene expression changes induced by a given disease phenotype is a crucial step in drug repurposing. We present an approach for Predicting Upstream REgulators (PURE) designed to tackle this challenge. PURE can correctly infer a CDT from the measured expression changes in a given phenotype, as well as correctly identify drugs that could revert disease-induced gene expression changes. We compared the proposed approach with four classical approaches as well as with the causal analysis used in Ingenuity Pathway Analysis (IPA) on 16 data sets (1 rat, 5 mouse, and 10 human data sets), involving 8 chemicals or drugs. We assessed the results based on the ability to correctly identify the CDT as indicated by its rank. We also considered the number of false positives, i.e. CDTs other than the correct CDT that were reported to be significant by each method. The proposed approach performed best in 11 out of the 16 experiments, reporting the correct CDT at the very top 7 times. IPA was the second best, reporting the correct CDT at the top 5 times, but was unable to identify the correct CDT at all in 5 out of the 16 experiments. The validation results showed that our approach, PURE, outperformed some of the most popular methods in the field. PURE could effectively infer the true CDTs responsible for the observed gene expression changes and could also be useful in drug repurposing applications.

Gallic and ferulic acids suppress proteolytic activities and volatile trimethylamine production in the food-borne spoiler Rahnella aquatilis KM05

BACKGROUND

Rahnella aquatilis is a recognised microbial threat that alters the sensory properties of seafood. The high frequency with which R. aquatilis is isolated from fish has prompted a search for alternative preservatives. In the present study, in vitro and fish-based ecosystem (raw salmon-based medium) approaches were used to validate the antimicrobial effects of gallic (GA) and ferulic (FA) acids against R. aquatilis KM05. The results were compared with data describing the response of KM05 to sodium benzoate. Bioinformatics data of the whole genome were used to analyse the potential for fish spoilage by KM05 in detail, and the results revealed the main physiological characteristics that underlie reduced seafood quality.

RESULTS

In the KM05 genome, the most abundantly enriched Gene Ontology terms were 'metabolic process', 'organic substance metabolic process' and 'cellular process'. Through an evaluation of the Pfam annotations, 15 annotations were found to be directly involved in the proteolytic activity of KM05. Peptidase_M20 was the most abundantly represented (abundance value of 14060). Proteins representing the CutC family (abundance value of 427) indicated the potential for KM05 degradation of trimethyl-amine-N-oxide. Subinhibitory concentrations of GA and FA suppressed the proteolytic activities of KM05 both in vitro and in RS medium by an average of 33-45%. These results were confirmed by quantitative real-time PCR experiments, which also showed that the expression levels of genes involved in proteolytic activities and volatile trimethylamine production were also decreased.

CONCLUSION

Phenolic compounds can be used as potential food additives for preventing quality deterioration of fish products. © 2023 Society of Chemical Industry.

Gene Functional Networks from Time Expression Profiles: A Constructive Approach Demonstrated in Chili Pepper (Capsicum annuum L.)

Gene co-expression networks are powerful tools to understand functional interactions between genes. However, large co-expression networks are difficult to interpret and do not guarantee that the relations found will be true for different genotypes. Statistically verified time expression profiles give information about significant changes in expressions through time, and genes with highly correlated time expression profiles, which are annotated in the same biological process, are likely to be functionally connected. A method to obtain robust networks of functionally related genes will be useful to understand the complexity of the transcriptome, leading to biologically relevant insights. We present an algorithm to construct gene functional networks for genes annotated in a given biological process or other aspects of interest. We assume that there are genome-wide time expression profiles for a set of representative genotypes of the species of interest. The method is based on the correlation of time expression profiles, bound by a set of thresholds that assure both, a given false discovery rate, and the discard of correlation outliers. The novelty of the method consists in that a gene expression relation must be repeatedly found in a given set of independent genotypes to be considered valid. This automatically discards relations particular to specific genotypes, assuring a network robustness, which can be set a priori. Additionally, we present an algorithm to find transcription factors candidates for regulating hub genes within a network. The algorithms are demonstrated with data from a large experiment studying gene expression during the development of the fruit in a diverse set of chili pepper genotypes. The algorithm is implemented and demonstrated in a new version of the publicly available R package "Salsa" (version 1.0).

Automated SSHHPS Analysis Predicts a Potential Host Protein Target Common to Several Neuroinvasive (+)ssRNA Viruses

Within the viral genome, short stretches of homologous host pathogen sequences (SSHHPS) span the protease cleavage sites. To identify host proteins that may be cleaved during infection, we searched the human proteome for viral protease cleavage sites (~20 amino acids). We developed a sequence-to-symptom tool, automating the search and pairing process. We used the viral protein sequence, PHI-BLAST, and UniProt database for gene ontologies and disease relationships. We applied the tool to nine neuroinvasive viruses: Venezuelan and Eastern Equine encephalitis virus (VEEV, EEEV); severe acute respiratory syndrome (SARS, SARS-CoV-2); Middle East respiratory syndrome (MERS); EV-71; Japanese encephalitis virus (JEV); West Nile (WNV); and Zika (ZIKV). A comparison of the hits identified a protein common to all nine viruses called ADGRA2 (GPR124). ADGRA2 was a predicted hit of the 3CL main protease and papain-like protease (PLpro) of SARS-CoV-2. ADGRA2 is an adhesion G protein-coupled receptor and a key endothelial regulator of brain-specific angiogenesis. It is a Wnt7A/Wnt7B specific coactivator of beta-catenin signaling and is essential for blood-brain barrier (BBB) integrity in central nervous system (CNS) diseases. We show the cleavage of the predicted sequences in MYOM1, VWF by the SARS-CoV-2 PLpro; DNAH8 (dynein) by the MERS PLpro; ADGRA2 by the alphaviral VEEV nsP2 protease; and POT1 by the SARS-CoV-2 and MERS PLpro.

PathwayKO: An integrated platform for deciphering the systems-level signaling pathways

Systems characterization of immune landscapes in health, disease and clinical intervention cases is a priority in modern medicine. High-throughput transcriptomes accumulated from gene-knockout (KO) experiments are crucial for deciphering target KO signaling pathways that are impaired by KO genes at the systems-level. There is a demand for integrative platforms. This article describes the PathwayKO platform, which has integrated state-of-the-art methods of pathway enrichment analysis, statistics analysis, and visualizing analysis to conduct cutting-edge integrative pathway analysis in a pipeline fashion and decipher target KO signaling pathways at the systems-level. We focus on describing the methodology, principles and application features of PathwayKO. First, we demonstrate that the PathwayKO platform can be utilized to comprehensively analyze real-world mouse KO transcriptomes (GSE22873 and GSE24327), which reveal systemic mechanisms underlying the innate immune responses triggered by non-infectious extensive hepatectomy (2 hours after 85% liver resection surgery) and infectious CASP-model sepsis (12 hours after CASP-model surgery). Strikingly, our results indicate that both cases hit the same core set of 21 KO MyD88-associated signaling pathways, including the Toll-like receptor signaling pathway, the NFκB signaling pathway, the MAPK signaling pathway, and the PD-L1 expression and PD-1 checkpoint pathway in cancer, alongside the pathways of bacterial, viral and parasitic infections. These findings suggest common fundamental mechanisms between these immune responses and offer informative cues that warrant future experimental validation. Such mechanisms in mice may serve as models for humans and ultimately guide formulating the research paradigms and composite strategies to reduce the high mortality rates of patients in intensive care units who have undergone successful traumatic surgical treatments. Second, we demonstrate that the PathwayKO platform model-based assessments can effectively evaluate the performance difference of pathway analysis methods when benchmarked with a collection of proper transcriptomes. Together, such advances in methods for deciphering biological insights at the systems-level may benefit the fields of bioinformatics, systems immunology and beyond.

Thousands of human non-AUG extended proteoforms lack evidence of evolutionary selection among mammals

The synthesis of most proteins begins at AUG codons, yet a small number of non-AUG initiated proteoforms are also known. Here we analyse a large number of publicly available Ribo-seq datasets to identify novel, previously uncharacterised non-AUG proteoforms using Trips-Viz implementation of a novel algorithm for detecting translated ORFs. In parallel we analyse genomic alignment of 120 mammals to identify evidence of protein coding evolution in sequences encoding potential extensions. Unexpectedly we find that the number of non-AUG proteoforms identified with ribosome profiling data greatly exceeds those with strong phylogenetic support suggesting their recent evolution. Our study argues that the protein coding potential of human genome greatly exceeds that detectable through comparative genomics and exposes the existence of multiple proteins encoded by the same genomic loci.

The challenge of managing the evolution of genomics data over time: a conceptual model-based approach

BACKGROUND

Precision medicine is a promising approach that has revolutionized disease prevention and individualized treatment. The DELFOS oracle is a model-driven genomics platform that aids clinicians in identifying relevant variations that are associated with diseases. In its previous version, the DELFOS oracle did not consider the high degree of variability of genomics data over time. However, changes in genomics data have had a profound impact on clinicians' work and pose the need for changing past, present, and future clinical actions. Therefore, our objective in this work is to consider changes in genomics data over time in the DELFOS oracle.

METHODS

Our objective has been achieved through three steps. First, we studied the characteristics of each database from which the DELFOS oracle extracts data. Second, we characterized which genomics concepts of the conceptual schema that supports the DELFOS oracle change over time. Third, we updated the DELFOS Oracle so that it can manage the temporal dimension. To validate our approach, we carried out a use case to illustrate how the new version of the DELFOS oracle handles the temporal dimension.

RESULTS

Three events can change genomics data, namely, the addition of a new variation, the addition of a new link between a variation and a phenotype, and the update of a link between a variation and a phenotype. These events have been linked to the entities of the conceptual model that are affected by them. Finally, a new version of the DELFOS oracle that can deal with the temporal dimension has been implemented.

CONCLUSION

Huge amounts of genomics data that is associated with diseases change over time, impacting patients' diagnosis and treatment. Including this information in the DELFOS oracle added an extra layer of complexity, but using a model-driven based approach mitigated the cost of implementing the needed changes. The new version handles the temporal dimension appropriately and eases clinicians' work.

In Vitro Anti-Proliferative and Apoptotic Effects of Hydroxytyrosyl Oleate on SH-SY5Y Human Neuroblastoma Cells

The antitumor activity of polyphenols derived from extra virgin olive oil and, in particular the biological activity of HTyr, has been studied extensively. However, the use of HTyr as a therapeutic agent for clinical applications is limited by its low bioavailability and rapid excretion in humans. To overcome these limitations, several synthetic strategies have been optimized to prepare lipophenols and new compounds derived from HTyr to increase lipophilicity and bioavailability. One very promising ester is hydroxytyrosyl oleate (HTyr-OL) because the chemical structure of HTyr, which is responsible for several biological activities, is linked to the monounsaturated chain of oleic acid (OA), giving the compound high lipophilicity and thus bioavailability in the cellular environment. In this study, the in vitro cytotoxic, anti-proliferative, and apoptotic induction activities of HTyr-OL were evaluated against SH-SY5Y human neuroblastoma cells, and the effects were compared with those of HTyr and OA. The results showed that the biological activity of HTyr was maintained in HTyr-OL treatments at lower dosages. In addition, the shotgun proteomic approach was used to study HTyr-OL-treated and untreated neuroblastoma cells, revealing that the antioxidant, anti-proliferative and anti-inflammatory activities of HTyr-OL were observed in the unique proteins of the two groups of samples.

Epithelial cell responses to rhinovirus identify an early-life-onset asthma phenotype in adults

BACKGROUND

The study of pathogenic mechanisms in adult asthma is often marred by a lack of precise information about the natural history of the disease. Children who have persistent wheezing (PW) during the first 6 years of life and whose symptoms start before age 3 years (PW+) are much more likely to have wheezing illnesses due to rhinovirus (RV) in infancy and to have asthma into adult life than are those who do not have PW (PW-).

OBJECTIVE

Our aim was to determine whether nasal epithelial cells from PW+ asthmatic adults as compared with cells from PW- asthmatic adults show distinct biomechanistic processes activated by RV exposure.

METHODS

Air-liquid interface cultures derived from nasal epithelial cells of 36-year old participants with active asthma with and without a history of PW in childhood (10 PW+ participants and 20 PW- participants) from the Tucson Children's Respiratory Study were challenged with a human RV-A strain (RV-A16) or control, and their RNA was sequenced.

RESULTS

A total of 35 differentially expressed genes involved in extracellular remodeling and angiogenesis distinguished the PW+ group from the PW- group at baseline and after RV-A stimulation. Notably, 22 transcriptomic pathways showed PW-by-RV interactions; the pathways were invariably overactivated in PW+ patients, and were involved in Toll-like receptor- and cytokine-mediated responses, remodeling, and angiogenic processes.

CONCLUSIONS

Asthmatic adults with a history of persistent wheeze in the first 6 years of life have specific biomolecular alterations in response to RV-A that are not present in patients without such a history. Targeting these mechanisms may slow the progression of asthma in these patients.

Epigenetics and ADHD

There is robust evidence of genetic susceptibility to Attention-Deficit Hyperactivity Disorder (ADHD); however, there still remains significant variability that is not attributable to genetic factors. The emerging field of epigenetics is beginning to reveal how genotypic expression can be mediated by an array of variables including external environmental exposure, inter-individual developmental variation, and by the genome itself. Epigenetic modification plays a central role in neurobiological and developmental processes, and disturbances to these processes can have implications for a range of mental health problems. Although the field is still in its early days, this chapter will discuss the current standing of epigenetic research into ADHD. Firstly, key relevant epigenetic processes will be discussed. This will be followed by an overview of the key findings to date investigating the role of epigenetics in ADHD. Human studies have included the theory-driven approach of candidate-gene studies (CGS), as well as the increasingly popular exploratory approach of epigenome-wide association studies (EWAS). Overall, the findings are heterogeneous. However, it is possible that with more longitudinal studies and better characterised cohorts, both predictive and protective links between epigenetic processes and ADHD will be revealed.

Uncovering BTB and CNC Homology1 (BACH1) as a Novel Cancer Therapeutic Target

BTB and CNC homology1 (BACH1), working as a transcriptional factor, is demonstrated to function on the regulation of epigenetic modifications by complex regulatory networks. Although BACH1 is reported as an oncogene, the overall analysis of its role remains lacking. In this study, we uncovered the capacity of BACH1 as a new pan-cancer therapeutic target. We found that BACH1 is highly expressed in abundant cancers and correlated with the poor prognosis of most cancers. The mutation sites of BACH1 varied in different cancer types and correlated to patients’ prognoses. The tumor mutation burden (TMB) in four cancer species and up to six tumor infiltrated immune cells had a significant relevance with BACH1. The enrichment analysis showed that the BACH1-associated genes were significantly enriched in the pathways of PD-1/L1 expression, ubiquitin-mediated proteolysis, T cell receptor, Th17 cell differentiation. We then demonstrated that BACH1 is positively correlated with the expression of many candidate genes, incluing SRPK2, GCLM, SLC40A1, and HK2 but negatively correlated with the expression of KEAP1 and GAPDH. Overall, our data shed light on BACH1’s effect on latent utility in cancer targeting therapy.

Delayed processing of blood samples impairs the accuracy of mRNA-based biomarkers

The transcriptome of peripheral white blood cells (PWBCs) are indicators of an organism’s physiological state, thus making them a prime biological sample for mRNA-based biomarker discovery. Here, we designed an experiment to evaluate the impact of delayed processing of whole blood samples on gene transcript abundance in PWBCs. We hypothesized that storing blood samples for 24 h at 4 °C would cause RNA degradation resulting in altered transcriptome profiles. There were no statistical differences in RNA quality parameters among samples processed after one, three, six, or eight hours post collection. Additionally, no significant differences were noted in RNA quality parameters or gene transcript abundance between samples collected from the jugular and coccygeal veins. However, samples processed after 24 h of storage had a lower RNA integrity number value (P = 0.03) in comparison to those processed after one hour of storage. Using RNA-sequencing, we identified four and 515 genes with differential transcript abundance in samples processed after storage for eight and 24 h, respectively, relative to samples processed after one hour. Sequencing coverage of transcripts was similar between samples from the 24-h and one-hour groups, thus showing no indication of RNA degradation. This alteration in transcriptome profiles can impair the accuracy of mRNA-based biomarkers, therefore, blood samples collected for mRNA-based biomarker discovery should be refrigerated immediately and processed within six hours post-sampling.

Black pepper and tarragon essential oils suppress the lipolytic potential and the type II secretion system of P. psychrophila KM02

Given the increasing consumer demand for raw, nonprocessed, safe, and long shelf-life fish and seafood products, research concerning the application of natural antimicrobials as alternatives to preservatives is of great interest. The aim of the following paper was to evaluate the effect of essential oils (EOs) from black pepper (BPEO) and tarragon (TEO), and their bioactive compounds: limonene (LIM), β-caryophyllene (CAR), methyl eugenol (ME), and β-phellandrene (PHE) on the lipolytic activity and type II secretion system (T2SS) of Pseudomonas psychrophila KM02 (KM02) fish isolates grown in vitro and in fish model conditions. Spectrophotometric analysis with the p-NPP reagent showed inhibition of lipolysis from 11 to 46%. These results were confirmed by RT-qPCR, as the expression levels of lipA, lipB, and genes encoding T2SS were also considerably decreased. The supplementation of marinade with BPEO and TEO contributed to KM02 growth inhibition during vacuum packaging of salmon fillets relative to control samples. Whole-genome sequencing (WGS) provided insight into the spoilage potential of KM02, proving its importance as a spoilage microorganism whose metabolic activity should be inhibited to maintain the quality and safety of fresh fish in the food market.

Urgent need for consistent standards in functional enrichment analysis

Gene set enrichment tests (a.k.a. functional enrichment analysis) are among the most frequently used methods in computational biology. Despite this popularity, there are concerns that these methods are being applied incorrectly and the results of some peer-reviewed publications are unreliable. These problems include the use of inappropriate background gene lists, lack of false discovery rate correction and lack of methodological detail. To ascertain the frequency of these issues in the literature, we performed a screen of 186 open-access research articles describing functional enrichment results. We find that 95% of analyses using over-representation tests did not implement an appropriate background gene list or did not describe this in the methods. Failure to perform p-value correction for multiple tests was identified in 43% of analyses. Many studies lacked detail in the methods section about the tools and gene sets used. An extension of this survey showed that these problems are not associated with journal or article level bibliometrics. Using seven independent RNA-seq datasets, we show misuse of enrichment tools alters results substantially. In conclusion, most published functional enrichment studies suffered from one or more major flaws, highlighting the need for stronger standards for enrichment analysis.

Functional enrichment analysis is a commonly used technique to identify trends in large scale biological datasets. In biomedicine, functional enrichment analysis of gene expression data is frequently applied to identify disease and drug mechanisms. While enrichment tests were once primarily conducted with complicated computer scripts, web-based tools are becoming more widely used. Users can paste a list of genes into a website and receive enrichment results in a matter of seconds. Despite the popularity of these tools, there are concerns that statistical problems and incomplete reporting are compromising research quality. In this article, we conducted a systematic examination of published enrichment analyses and assessed whether (i) any statistical flaws were present and (ii) sufficient methodological detail is provided such that the study could be replicated. We found that lack of methodological detail and errors in statistical analysis were widespread, which undermines the reliability and reproducibility of these research articles. A set of best practices is urgently needed to raise the quality of published work.

The Cancer Surfaceome Atlas integrates genomic, functional and drug response data to identify actionable targets

Cell-surface proteins (SPs) are a rich source of immune and targeted therapies. By systematically integrating single-cell and bulk genomics, functional studies and target actionability, in the present study we comprehensively identify and annotate genes encoding SPs (GESPs) pan-cancer. We characterize GESP expression patterns, recurrent genomic alterations, essentiality, receptor-ligand interactions and therapeutic potential. We also find that mRNA expression of GESPs is cancer-type specific and positively correlates with protein expression, and that certain GESP subgroups function as common or specific essential genes for tumor cell growth. We also predict receptor-ligand interactions substantially deregulated in cancer and, using systems biology approaches, we identify cancer-specific GESPs with therapeutic potential. We have made this resource available through the Cancer Surfaceome Atlas ( http://fcgportal.org/TCSA ) within the Functional Cancer Genome data portal.

Identification of Key Genes Associated With the Process of Hepatitis B Inflammation and Cancer Transformation by Integrated Bioinformatics Analysis

Background

Hepatocellular carcinoma (HCC) has become the main cause of cancer death worldwide. More than half of hepatocellular carcinoma developed from hepatitis B virus infection (HBV). The purpose of this study is to find the key genes in the transformation process of liver inflammation and cancer and to inhibit the development of chronic inflammation and the transformation from disease to cancer.

Methods

Two groups of GEO data (including normal/HBV and HBV/HBV-HCC) were selected for differential expression analysis. The differential expression genes of HBV-HCC in TCGA were verified to coincide with the above genes to obtain overlapping genes. Then, functional enrichment analysis, modular analysis, and survival analysis were carried out on the key genes.

Results

We identified nine central genes (CDK1, MAD2L1, CCNA2, PTTG1, NEK2) that may be closely related to the transformation of hepatitis B. The survival and prognosis gene markers composed of PTTG1, MAD2L1, RRM2, TPX2, CDK1, NEK2, DEPDC1, and ZWINT were constructed, which performed well in predicting the overall survival rate.

Conclusion

The findings of this study have certain guiding significance for further research on the transformation of hepatitis B inflammatory cancer, inhibition of chronic inflammation, and molecular targeted therapy of cancer.

Transcriptome profiling of Stevia rebaudiana MS007 revealed genes involved in flower development

Stevia rebaudiana is a medicinal plant recommended to diabetic or obese patients as an alternative sweetener owing to its low-calorie property. Previous studies have found that the stevioside level is highest at the time of flower bud formation and lowest at the time of preceding and following flower bud formation. Hence, this study aims to identify the genes involved in the flowering of local S. rebaudiana accession MS007 by investigating the transcriptomic data of two stages of growth, before flowering (BF) and after flowering (AF) that were deposited under accession number SRX6362785 and SRX6362784 at the NCBI SRA database. The transcriptomic study managed to annotate 108299 unigenes of S. rebaudiana with 8871 and 9832 genes that were differentially expressed in BF and AF samples, respectively. These genes involved in various metabolic pathways related to flower development, response to stimulus as well as photosynthesis. Pheophorbide A oxygenase ( PAO ), eukaryotic translation initiation factor 3 subunit E ( TIF3E1 ), and jasmonate ZIM domain-containing protein 1 ( JAZ1 ) were found to be involved in the flower development. The outcome of this study will help further research in the manipulation of the flowering process, especially in the breeding programme to develop photo-insensitive Stevia plant.

Cleaning the Medicago Microarray Database to Improve Gene Function Analysis

Transcriptomics studies have been facilitated by the development of microarray and RNA-Seq technologies, with thousands of expression datasets available for many species. However, the quality of data can be highly variable, making the combined analysis of different datasets difficult and unreliable. Most of the microarray data for Medicago truncatula, the barrel medic, have been stored and made publicly accessible on the web database Medicago truncatula Gene Expression atlas (MtGEA). The aim of this work is to ameliorate the quality of the MtGEA database through a general method based on logical and statistical relationships among parameters and conditions. The initial 716 columns available in the dataset were reduced to 607 by evaluating the quality of data through the sum of the expression levels over the entire transcriptome probes and Pearson correlation among hybridizations. The reduced dataset shows great improvements in the consistency of the data, with a reduction in both false positives and false negatives resulting from Pearson correlation and GO enrichment analysis among genes. The approach we used is of general validity and our intent is to extend the analysis to other plant microarray databases.

Transcriptome Analysis of Oleaginous Fungus Mucor circinelloides WJ11 in Response to Exogenous Soybean Oil as Carbon Source

Mucor circinelloides is an oleaginous fungus that utilizes a wide variety of carbon substrates for its growth. The different sources of carbon strongly influence the total lipid content of the fungus. These different carbon substrates are assimilated and dissimilated through different metabolic pathways before entering into the TAG synthesis pathway. In the present study, we attempted to explore the mechanism of ex-novo lipid biosynthesis in M. circinelloides WJ11 in response to exogenous plant oil as a carbon source through transcriptomic analysis. The lipid content of WJ11 grown in a media containing mixed soybean oil with glucose as a carbon source was up to 43.8%, an increase of 13.9% as compared to glucose alone as the carbon source. RNA-Seq analysis was performed to investigate global gene expression patterns in the oil-treated WJ11. Based on RNA-seq analysis, among the 4646 differentially expressed genes (DEGs), 2379 were up-regulated and 2267 down-regulated. The expression of acetyl-CoA synthetase, 6-phosphofructokinase, alcohol dehydrogenase (NADP+), fructose-bisphosphate aldolase, and pyruvate kinase was down-regulated while genes related to triglyceride synthesis were up-regulated. The majority of genes and pathways related to lipid biosynthesis were up-regulated indicating a diversion of metabolic pathways towards lipid biosynthesis. The data generated advance the genomic resources and provide insights into the mechanisms of ex-novo lipid accumulation in fungi that use exogenous oil as a carbon source.

Overcoming false-positive gene-category enrichment in the analysis of spatially resolved transcriptomic brain atlas data

Transcriptomic atlases have improved our understanding of the correlations between gene-expression patterns and spatially varying properties of brain structure and function. Gene-category enrichment analysis (GCEA) is a common method to identify functional gene categories that drive these associations, using gene-to-category annotation systems like the Gene Ontology (GO). Here, we show that applying standard GCEA methodology to spatial transcriptomic data is affected by substantial false-positive bias, with GO categories displaying an over 500-fold average inflation of false-positive associations with random neural phenotypes in mouse and human. The estimated false-positive rate of a GO category is associated with its rate of being reported as significantly enriched in the literature, suggesting that published reports are affected by this false-positive bias. We show that within-category gene-gene coexpression and spatial autocorrelation are key drivers of the false-positive bias and introduce flexible ensemble-based null models that can account for these effects, made available as a software toolbox.

Pathway analysis for genome-wide genetic variation data: Analytic principles, latest developments, and new opportunities

Pathway analysis, also known as gene-set enrichment analysis, is a multilocus analytic strategy that integrates a priori, biological knowledge into the statistical analysis of high-throughput genetics data. Originally developed for the studies of gene expression data, it has become a powerful analytic procedure for in-depth mining of genome-wide genetic variation data. Astonishing discoveries were made in the past years, uncovering genes and biological mechanisms underlying common and complex disorders. However, as massive amounts of diverse functional genomics data accrue, there is a pressing need for newer generations of pathway analysis methods that can utilize multiple layers of high-throughput genomics data. In this review, we provide an intellectual foundation of this powerful analytic strategy, as well as an update of the state-of-the-art in recent method developments. The goal of this review is threefold: (1) introduce the motivation and basic steps of pathway analysis for genome-wide genetic variation data; (2) review the merits and the shortcomings of classic and newly emerging integrative pathway analysis tools; and (3) discuss remaining challenges and future directions for further method developments.

The Bgee suite: integrated curated expression atlas and comparative transcriptomics in animals

Bgee is a database to retrieve and compare gene expression patterns in multiple animal species, produced by integrating multiple data types (RNA-Seq, Affymetrix, in situ hybridization, and EST data). It is based exclusively on curated healthy wild-type expression data (e.g., no gene knock-out, no treatment, no disease), to provide a comparable reference of normal gene expression. Curation includes very large datasets such as GTEx (re-annotation of samples as ‘healthy’ or not) as well as many small ones. Data are integrated and made comparable between species thanks to consistent data annotation and processing, and to calls of presence/absence of expression, along with expression scores. As a result, Bgee is capable of detecting the conditions of expression of any single gene, accommodating any data type and species. Bgee provides several tools for analyses, allowing, e.g., automated comparisons of gene expression patterns within and between species, retrieval of the prefered conditions of expression of any gene, or enrichment analyses of conditions with expression of sets of genes. Bgee release 14.1 includes 29 animal species, and is available at https://bgee.org/ and through its Bioconductor R package BgeeDB.

Homogentisic acid induces cytoskeleton and extracellular matrix alteration in alkaptonuric cartilage

Alkaptonuria (AKU) is an ultra-rare disease caused by the deficient activity of homogentisate 1,2-dioxygenase enzyme, leading the accumulation of homogentisic acid (HGA) in connective tissues implicating the formation of a black pigmentation called "ochronosis." Although AKU is a multisystemic disease, the most affected tissue is the articular cartilage, which during the pathology appears to be highly damaged. In this study, a model of alkaptonuric chondrocytes and cartilage was realized to investigate the role of HGA in the alteration of the extracellular matrix (ECM). The AKU tissues lost its architecture composed of collagen, proteoglycans, and all the proteins that characterize the ECM. The cause of this alteration in AKU cartilage is attributed to a degeneration of the cytoskeletal network in chondrocytes caused by the accumulation of HGA. The three cytoskeletal proteins, actin, vimentin, and tubulin, were analyzed and a modification in their amount and disposition in AKU chondrocytes model was identified. Cytoskeleton is involved in many fundamental cellular processes; therefore, the aberration in this complex network is involved in the manifestation of AKU disease.

GO Enrichment Analysis for Differential Proteomics Using ProteoRE

With the increased simplicity of producing proteomics data, the bottleneck has now shifted to the functional analysis of large lists of proteins to translate this primary level of information into meaningful biological knowledge. Tools implementing such approach are a powerful way to gain biological insights related to their samples, provided that biologists/clinicians have access to computational solutions even when they have little programming experience or bioinformatics support. To achieve this goal, we designed ProteoRE (Proteomics Research Environment), a unified online research service that provides end-users with a set of tools to interpret their proteomics data in a collaborative and reproducible manner. ProteoRE is built upon the Galaxy framework, a workflow system allowing for data and analysis persistence, and providing user interfaces to facilitate the interaction with tools dedicated to the functional and the visual analysis of proteomics datasets. A set of tools relying on computational methods selected for their complementarity in terms of functional analysis was developed and made accessible via the ProteoRE web portal. In this chapter, a step-by-step protocol linking these tools is designed to perform a functional annotation and GO-based enrichment analyses applied to a set of differentially expressed proteins as a use case. Analytical practices, guidelines as well as tips related to this strategy are also provided. Tools, datasets, and results are freely available at http://www.proteore.org , allowing researchers to reuse them.

A proteomics approach to further highlight the altered inflammatory condition in Rett syndrome

Rett syndrome (RTT) is a progressive neurodevelopmental disorder caused by mutations in the X-linked MECP2 gene. RTT patients show multisystem disturbances associated with perturbed redox homeostasis and inflammation, which appear as possible key factors in RTT pathogenesis. In this study, using primary dermal fibroblasts from control and RTT subjects, we performed a proteomic analysis that, together with data mining approaches, allowed us to carry out a comprehensive characterization of RTT cellular proteome. Functional and pathway enrichment analyses showed that differentially expressed proteins in RTT were mainly enriched in biological processes related to immune/inflammatory responses. Overall, by using proteomic data mining as supportive approach, our results provide a detailed insight into the molecular pathways involved in RTT immune dysfunction that, causing tissue and organ damage, can increase the vulnerability of affected patients to unknown endogenous factors or infections.

The molecular response mechanisms of a diatom Thalassiosira pseudonana to the toxicity of BDE-47 based on whole transcriptome analysis

Polybrominated diphenyl ethers (PBDEs) are ubiquitously distributed persistent organic pollutants (POPs) in marine environments. Phytoplankton are the entrance of PBDEs entering to biotic environments from abiotic environments, while the responding mechanisms of phytoplankton to PBDEs have not been full established. Therefore, we chose the model diatom Thalassiosira pseudonana in this study, by integrating whole transcriptome analysis with physiological-biochemical data, to reveal the molecular responding mechanisms of T. pseudonana to the toxicity of BDE-47. Our results indicated the changes of genes expressions correlated to the physiological-biochemical changes, and there were multiple molecular mechanisms of T. pseudonana responding to the toxicity of BDE-47: Gene expressions evidence explained the suppression of light reaction and proved the occurrence of cellular oxidative stress; In the meanwhile, up-regulations of genes in pathways involving carbon metabolisms happened, including the Calvin cycle, glycolysis, TCA cycle, fatty acid synthesis, and triacylglycerol synthesis; Lastly, DNA damage was found and three outcome including DNA repair, cell cycle arrest and programmed cell death (PCD) happened, which could finally inhibit the cell division and population growth of T. pseudonana. This study presented the most complete molecular responding mechanisms of phytoplankton cells to PBDEs, and provided valuable information of various PBDEs-sensitive genes with multiple functions for further research involving organic pollutants and phytoplankton.

Whole-transcriptome sequencing (RNA-seq) study of the ZFL zebrafish liver cell line after acute exposure to Cd2+ ions

Cadmium (Cd) is a non-essential metal with no known biological function and a broad range of toxic effects in biological systems. We used whole-transcriptome sequencing (RNA-seq) to study the effects of Cd²⁺ toxicity in zebrafish liver cells, ZFL. The results of an RNA-Seq analysis of ZFL cells exposed to 5, 10 or 20 μM Cd²⁺ for 4- or 24-h. The differentially expressed genes affected by Cd²⁺ were analyzed by using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis to study the regulated pathways. Cd²⁺ regulated the expression of genes associated with cellular Cu, Zn, and Fe homeostasis, DNA replication leading to cell cycle arrest and apoptosis, and glutathione metabolism. Cd²⁺ boosted up the amino acid synthesis, possibly to support the glutathione metabolism for tackling the oxidative stress generated from Cd²⁺. Cd²⁺ stimulation was similar to heat or xenobiotics, based on the responses from ZFL such as endoplasmic reticulum stress and protein folding. We linked also those finding of gene activations relating to carcinogenesis of Cd. This paper provides a comprehensive analysis of the expression profiles induced by Cd²⁺ exposure in ZFL cells, as well as useful insights into the specific toxic effects.

Transcriptome Analysis of Amyloodinium ocellatum Tomonts Revealed Basic Information on the Major Potential Virulence Factors

The ectoparasite protozoan Amyloodinium ocellatum (AO) is the etiological agent of amyloodiniosis in European seabass (Dicentrarchus labrax) (ESB). There is a lack of information about basic molecular data on AO biology and its interaction with the host. Therefore, de novo transcriptome sequencing of AO tomonts was performed. AO trophonts were detached from infested ESB gills, and quickly becoming early tomonts were purified by Percoll^® density gradient. Tomont total RNA was processed and quality was assessed immediately. cDNA libraries were constructed using TruSeq^® Stranded mRNA kit and sequenced using Illumina sequencer. CLC assembly was used to generate the Transcriptome assembly of AO tomonts. Out of 48,188 contigs, 56.12% belong to dinophyceae wherein Symbiodinium microadriaticum had 94.61% similarity among dinophyceae. Functional annotations of contigs indicated that 12,677 had associated GO term, 9005 with KEGG term. The contigs belonging to dinophyceae resulted in the detection of several peptidases. A BLAST search for known virulent factors from the virulence database resulted in hits to Rab proteins, AP120, Ribosomal phosphoprotein, Heat-shock protein70, Casein kinases, Plasmepsin IV, and Brucipain. Hsp70 and casein kinase II alpha were characterized in-silico. Altogether, these results provide a reference database in understanding AO molecular biology, aiding to the development of novel diagnostics and future vaccines.

Cancer cell line-specific protein profiles in extracellular vesicles identified by proteomics

Extracellular vesicles (EVs), are important for intercellular communication in both physiological and pathological processes. To explore the potential of cancer derived EVs as disease biomarkers for diagnosis, monitoring, and treatment decision, it is necessary to thoroughly characterize their biomolecular content. The aim of the study was to characterize and compare the protein content of EVs derived from three different cancer cell lines in search of a specific molecular signature, with emphasis on proteins related to the carcinogenic process. Oral squamous cell carcinoma (OSCC), pancreatic ductal adenocarcinoma (PDAC) and melanoma brain metastasis cell lines were cultured in CELLine AD1000 flasks. EVs were isolated by ultrafiltration and size-exclusion chromatography and characterized. Next, the isolated EVs underwent liquid chromatography-mass spectrometry (LC-MS) analysis for protein identification. Functional enrichment analysis was performed for a more general overview of the biological processes involved. More than 600 different proteins were identified in EVs from each particular cell line. Here, 14%, 10%, and 24% of the identified proteins were unique in OSCC, PDAC, and melanoma vesicles, respectively. A specific protein profile was discovered for each cell line, e.g., EGFR in OSCC, Muc5AC in PDAC, and FN1 in melanoma vesicles. Nevertheless, 25% of all the identified proteins were common to all cell lines. Functional enrichment analysis linked the proteins in each data set to biological processes such as "biological adhesion", "cell motility", and "cellular component biogenesis". EV proteomics discovered cancer-specific protein profiles, with proteins involved in processes promoting tumor progression. In addition, the biological processes associated to the melanoma-derived EVs were distinct from the ones linked to the EVs isolated from OSCC and PDAC. The malignancy specific biomolecular cues in EVs may have potential applications as diagnostic biomarkers and in therapy.

Digging into the Genomic Past of Swiss Honey Bees by Whole-Genome Sequencing Museum Specimens

Historical specimens in museum collections provide opportunities to gain insights into the genomic past. For the Western honey bee, Apis mellifera L., this is particularly important because its populations are currently under threat worldwide and have experienced many changes in management and environment over the last century. Using Swiss Apis mellifera mellifera as a case study, our research provides important insights into the genetic diversity of native honey bees prior to the industrial-scale introductions and trade of non-native stocks during the 20th century—the onset of intensive commercial breeding and the decline of wild honey bees following the arrival of Varroa destructor. We sequenced whole-genomes of 22 honey bees from the Natural History Museum in Bern collected in Switzerland, including the oldest A. mellifera sample ever sequenced. We identify both, a historic and a recent migrant, natural or human-mediated, which corroborates with the population history of honey bees in Switzerland. Contrary to what we expected, we find no evidence for a significant genetic bottleneck in Swiss honey bees, and find that genetic diversity is not only maintained, but even slightly increased, most probably due to modern apicultural practices. Finally, we identify signals of selection between historic and modern honey bee populations associated with genes enriched in functions linked to xenobiotics, suggesting a possible selective pressure from the increasing use and diversity of chemicals used in agriculture and apiculture over the last century.

Proteomic profiling reveals mitochondrial alterations in Rett syndrome

Rett syndrome (RTT) is a pervasive neurodevelopmental disorder associated with mutation in MECP2 gene. Despite a well-defined genetic cause, there is a growing consensus that a metabolic component could play a pivotal role in RTT pathophysiology. Indeed, perturbed redox homeostasis and inflammation, i.e. oxinflammation, with mitochondria dysfunction as the central hub between the two phenomena, appear as possible key contributing factors to RTT pathogenesis and its clinical features. While these RTT-related changes have been widely documented by transcriptomic profiling, proteomics studies supporting these evidences are still limited. Here, using primary dermal fibroblasts from control and patients, we perform a large-scale proteomic analysis that, together with data mining approaches, allow us to carry out the first comprehensive characterization of RTT cellular proteome, showing mainly changes in expression of proteins involved in the mitochondrial network. These findings parallel with an altered expression of key mediators of mitochondrial dynamics and mitophagy associated with abnormal mitochondrial morphology. In conclusion, our proteomic analysis confirms the pathological relevance of mitochondrial dysfunction in RTT pathogenesis and progression.

Facial shape and allometry quantitative trait locus intervals in the Diversity Outbred mouse are enriched for known skeletal and facial development genes

The biology of how faces are built and come to differ from one another is complex. Discovering normal variants that contribute to differences in facial morphology is one key to untangling this complexity, with important implications for medicine and evolutionary biology. This study maps quantitative trait loci (QTL) for skeletal facial shape using Diversity Outbred (DO) mice. The DO is a randomly outcrossed population with high heterozygosity that captures the allelic diversity of eight inbred mouse lines from three subspecies. The study uses a sample of 1147 DO animals (the largest sample yet employed for a shape QTL study in mouse), each characterized by 22 three-dimensional landmarks, 56,885 autosomal and X-chromosome markers, and sex and age classifiers. We identified 37 facial shape QTL across 20 shape principal components (PCs) using a mixed effects regression that accounts for kinship among observations. The QTL include some previously identified intervals as well as new regions that expand the list of potential targets for future experimental study. Three QTL characterized shape associations with size (allometry). Median support interval size was 3.5 Mb. Narrowing additional analysis to QTL for the five largest magnitude shape PCs, we found significant overrepresentation of genes with known roles in growth, skeletal and facial development, and sensory organ development. For most intervals, one or more of these genes lies within 0.25 Mb of the QTL's peak. QTL effect sizes were small, with none explaining more than 0.5% of facial shape variation. Thus, our results are consistent with a model of facial diversity that is influenced by key genes in skeletal and facial development and, simultaneously, is highly polygenic.

Identification of Known and Novel MicroRNAs in Raspberry Organs Through High-Throughput Sequencing

MicroRNAs (miRNAs) are a class of small endogenous RNAs that play important regulatory roles in plants by negatively affecting gene expression. Studies on the identification of miRNAs and their functions in various plant species and organs have significantly contributed to plant development research. In the current study, we utilized high-throughput sequencing to detect the miRNAs in the root, stem, and leaf tissues of raspberry (Rubus idaeus). A total of more than 35 million small RNA reads ranging in size from 18 to 35 nucleotides were obtained, with 147 known miRNAs and 542 novel miRNAs identified among the three organs. Sequence verification and the relative expression profiles of the six known miRNAs were investigated by stem-loop quantitative real-time PCR. Furthermore, the potential target genes of the known and novel miRNAs were predicted and subjected to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway annotation. Enrichment analysis of the GO-associated biological processes and molecular functions revealed that these target genes were potentially involved in a wide range of metabolic pathways and developmental processes. Moreover, the miRNA target prediction revealed that most of the targets predicted as transcription factor-coding genes are involved in cellular and metabolic processes. This report is the first to identify miRNAs in raspberry. The detected miRNAs were analyzed by cluster analysis according to their expression, which revealed that these conservative miRNAs are necessary for plant functioning. The results add novel miRNAs to the raspberry transcriptome, providing a useful resource for the further elucidation of the functional roles of miRNAs in raspberry growth and development.

Soybean aphids adapted to host-plant resistance by down regulating putative effectors and up regulating transposable elements

In agricultural systems, crops equipped with host-plant resistance (HPR) have enhanced protection against pests, and are used as a safe and sustainable tool in pest management. In soybean, HPR can control the soybean aphid (Aphis glycines), but certain aphid populations have overcome this resistance (i.e., virulence). The molecular mechanisms underlying aphid virulence to HPR are unknown, but likely involve effector proteins that are secreted by aphids to modulate plant defenses. Another mechanism to facilitate adaptation is through the activity of transposable elements, which can become activated by stress. In this study, we performed RNA sequencing of virulent and avirulent soybean aphids fed susceptible or resistant (Rag1 + Rag2) soybean. Our goal was to better understand the molecular mechanisms underlying soybean aphid virulence. Our data showed that virulent aphids mostly down regulate putative effector genes relative to avirulent aphids, especially when aphids were fed susceptible soybean. Decreased expression of effectors may help evade HPR plant defenses. Virulent aphids also transcriptionally up regulate a diverse set of transposable elements and nearby genes, which is consistent with stress adaptation. Our work demonstrates two mechanisms of pest adaptation to resistance, and identifies effector gene targets for future functional testing.

A Literature Review of Gene Function Prediction by Modeling Gene Ontology

Annotating the functional properties of gene products, i.e., RNAs and proteins, is a fundamental task in biology. The Gene Ontology database (GO) was developed to systematically describe the functional properties of gene products across species, and to facilitate the computational prediction of gene function. As GO is routinely updated, it serves as the gold standard and main knowledge source in functional genomics. Many gene function prediction methods making use of GO have been proposed. But no literature review has summarized these methods and the possibilities for future efforts from the perspective of GO. To bridge this gap, we review the existing methods with an emphasis on recent solutions. First, we introduce the conventions of GO and the widely adopted evaluation metrics for gene function prediction. Next, we summarize current methods of gene function prediction that apply GO in different ways, such as using hierarchical or flat inter-relationships between GO terms, compressing massive GO terms and quantifying semantic similarities. Although many efforts have improved performance by harnessing GO, we conclude that there remain many largely overlooked but important topics for future research.

Identification of novel therapeutic target and epitopes through proteome mining from essential hypothetical proteins in Salmonella strains: An In silico approach towards antivirulence therapy and vaccine development

Salmonella strains are responsible for a huge mortality rate through foodborne ailment in the world that necessitated the discovery of novel drugs and vaccines. Essential hypothetical proteins (EHPs), whose structures and functions were previously unknown, could serve as potential therapeutic and vaccine targets. Antivirulence therapy shall emerge as a superior therapeutic approach that uses virulence factors as drug targets. This study annotated the biological functions of 96 out of total 106 essential hypothetical proteins in five strains of Salmonella and classified into nine important protein categories. 34 virulence factors were predicted among the EHPs, out of which, 11 were identified to be pathogen specific potential drug targets for antivirulence therapy. These targets were non-homologous to both human and gut microbiota proteome to avoid cross-reactivity with them. Seven identified targets had druggable property, while the rest four targets were novel targets. Four identified targets (DEG10320148, DEG10110027, DEG10110040 and DEG10110142) had antigenic properties and were further classified as: two membrane-bound Lipid-binding transmembrane proteins, a Zinc-binding membrane protein and an extracellular glycosylase. These targets could be potentially used for the development of subunit vaccines. The study further identified 11 highly conserved and exposed epitope sequences from these 4 vaccine targets. The three-dimensional structures of the vaccine targets were also elucidated along with highlighting the conformation of the epitopes. This study identified potential therapeutic targets for antivirulence therapy against Salmonella. It would therefore instigate in novel drug designing as well as provide important leads to new Salmonella vaccine development.