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PEX5, the peroxisomal protein shuttling receptor, binds newly synthesized proteins in the cytosol and transports them to the organelle. During its stay at the peroxisomal protein translocon, PEX5 is monoubiquitinated at its cysteine 11 residue, a mandatory modification for its subsequent ATP-dependent extraction back into the cytosol. The reason why a cysteine and not a lysine residue is the ubiquitin acceptor is unknown. Using an established rat liver-based cell-free in vitro system, we found that, in contrast to wild-type PEX5, a PEX5 protein possessing a lysine at position 11 is polyubiquitinated at the peroxisomal membrane, a modification that negatively interferes with the extraction process. Wild-type PEX5 cannot retain a polyubiquitin chain because ubiquitination at cysteine 11 is a reversible reaction, with the E2-mediated deubiquitination step presenting faster kinetics than PEX5 polyubiquitination. We propose that the reversible nonconventional ubiquitination of PEX5 ensures that neither the peroxisomal protein translocon becomes obstructed with polyubiquitinated PEX5 nor is PEX5 targeted for proteasomal degradation. The peroxisomal protein shuttling receptor PEX5 transports newly synthesised proteins from the cytosol to the peroxisome, where it is monoubiquitinated at cysteine 11 so it can be extracted back to the cytosol by ATP. These authors show that longer ubiquitin chains attached to this cysteine are rapidly removed by the ubiquitin-conjugating enzyme E2D3, ensuring the correct functioning of this protein import machinery. [ABSTRACT FROM AUTHOR]

The cell division cycle associated (CDCA) genes regulate the cell cycle; however, their relationship with prognosis in glioma has been poorly reported in the literature. The Cancer Genome Atlas (TCGA) was utilized to probe the CDCA family in relation to the adverse clinical features of glioma. Glioma single-cell atlas reveals specific expression of CDCA3, 4, 5, 8 in malignant cells and CDCA7 in neural progenitor cells (NPC)-like malignant cells. Glioma data from TCGA, the China Glioma Genome Atlas Project (CGGA) and the gene expression omnibus (GEO) database all demonstrated that CDCA2, 3, 4, 5, 7 and 8 are prognostic markers for glioma. Further analysis identified CDCA2, 5 and 8 as independent prognostic factors for glioma. Lasso regression-based risk models for CDCA families demonstrated that high-risk patients were characterized by high tumor mutational burden (TMB), low levels of microsatellite instability (MSI), and low tumor immune dysfunction and rejection (TIDE) scores. These pointed to immunotherapy for glioma as a potentially viable treatment option Further CDCA clustering suggested that the high CDCA subtype exhibited a high macrophage phenotype and was associated with a higher antigen presentation capacity and high levels of immune escape. In addition, hsa-mir-15b-5p was predicted to be common regulator of CDCA3 and CDCA4, which was validated in U87 and U251 cells. Importantly, we found that CDCAs may indicate response to drug treatment, especially rapamycin, in glioma. In summary, our results suggest that CDCAs have potential applications in clinical diagnosis and as drug sensitivity markers in glioma. [ABSTRACT FROM AUTHOR]

Study reproducibility is essential to corroborate, build on, and learn from the results of scientific research but is notoriously challenging in bioinformatics, which often involves large data sets and complex analytic workflows involving many different tools. Additionally, many biologists are not trained in how to effectively record their bioinformatics analysis steps to ensure reproducibility, so critical information is often missing. Software tools used in bioinformatics can automate provenance tracking of the results they generate, removing most barriers to bioinformatics reproducibility. Here we present an implementation of that idea, Provenance Replay, a tool for generating new executable code from results generated with the QIIME 2 bioinformatics platform, and discuss considerations for bioinformatics developers who wish to implement similar functionality in their software. [ABSTRACT FROM AUTHOR]

The bidirectional causal relationship between type 2 diabetes mellitus (T2DM) and breast cancer (BC) has been established by numerous epidemiological studies. However, the underlying molecular mechanisms are not yet fully understood. Identification of hub genes implicated in T2DM-BC molecular crosstalk may help elucidate on the causative mechanisms. For this, expression series GSE29231 (T2DM-adipose tissue), GSE70905 (BC- breast adenocarcinoma biopsies) and GSE150586 (diabetes and BC breast biopsies) were extracted from Gene Expression Omnibus (GEO) database, and analyzed to obtain differentially expressed genes (DEGs). The overlapping DEGs were determined using FunRich. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and Transcription Factor (TF) analyses were performed on EnrichR software and a protein-protein interaction (PPI) network was constructed using STRING software. The network was analyzed on Cytoscape to determine hub genes and Kaplan-Meier plots were obtained. A total of 94 overlapping DEGs were identified between T2DM and BC samples. These DEGs were mainly enriched for GO terms RNA polymerase II core promoter proximal region sequence and its DNA binding, and cAMP response element binding protein, and KEGG pathways including bladder cancer, thyroid cancer and PI3K-AKT signaling. Eight hub genes were identified: interleukin 6 (IL6), tumor protein 53 (TP53), interleukin 8 (CXCL8), MYC, matrix metalloproteinase 9 (MMP9), beta-catenin 1 (CTNNB1), nitric oxide synthase 3 (NOS3) and interleukin 1 beta (IL1β). MMP9 and MYC associated unfavorably with overall survival (OS) in breast cancer patients, IL6, TP53, IL1β and CTNNB1 associated favorably, whereas NOS3 did not show any correlation with OS. Salt inducible kinase 1 (SIK1) was identified as a significant key DEG for comorbid samples when compared with BC, also dysregulated in T2DM and BC samples (adjusted p <0.05). Furthermore, four of the significant hub genes identified, including IL6, CXCL8, IL1B and MYC were also differentially expressed for comorbid samples, however at p < 0.05. Our study identifies key genes including SIK1, for comorbid state and 8 hub genes that may be implicated in T2DM-BC crosstalk. However, limitations associated with the insilico nature of this study necessitates for subsequent validation in wet lab. Hence, further investigation is crucial to study the molecular mechanisms of action underlying these genes to fully explore their potential as diagnostic and prognostic biomarkers and therapeutic targets for T2DM-BC association. [ABSTRACT FROM AUTHOR]

Some scientific studies involve huge amounts of bioinformatics data that cannot be analyzed on personal computers usually employed by researchers for day-to-day activities but rather necessitate effective computational infrastructures that can work in a distributed way. For this purpose, distributed computing systems have become useful tools to analyze large amounts of bioinformatics data and to generate relevant results on virtual environments, where software can be executed for hours or even days without affecting the personal computer or laptop of a researcher. Even if distributed computing resources have become pivotal in multiple bioinformatics laboratories, often researchers and students use them in the wrong ways, making mistakes that can cause the distributed computers to underperform or that can even generate wrong outcomes. In this context, we present here ten quick tips for the usage of Apache Spark distributed computing systems for bioinformatics analyses: ten simple guidelines that, if taken into account, can help users avoid common mistakes and can help them run their bioinformatics analyses smoothly. Even if we designed our recommendations for beginners and students, they should be followed by experts too. We think our quick tips can help anyone make use of Apache Spark distributed computing systems more efficiently and ultimately help generate better, more reliable scientific results. [ABSTRACT FROM AUTHOR]

Background: Cystic echinococcosis (CE), caused by Echinococcus granulosus sensu lato, is a neglected zoonosis. Its diagnosis relies on imaging, supported by serology, while only imaging is useful for staging and follow-up. Since diagnostic tools and expertise are not widely available, new accurate and easily implementable assays for the diagnosis and follow-up of CE are highly needed. Methodology/Principal Findings: We aimed to identify new E. granulosus antigens through a bioinformatics selection applied to the parasite genome, followed by peptide microarray screening and validation in ELISA, using independent panels of sera from patients with hepatic CE and clinically relevant controls. From 950 proteins selected in silico, 2,379 peptides were evaluated by microarray for IgG reactivity and eight candidates selected for validation. Reactivity to one peptide was significantly higher in the CE group (p = 0.044), but had suboptimal diagnostic accuracy. Conclusions/Significance: Here we performed bioinformatics analysis and peptide microarray for antigen discovery, useful for the diagnosis of CE. Eight candidates were selected and validated. Reactivity to one peptide associated to CE but had suboptimal diagnostic accuracy. Importantly, the database developed in this study may be used to identify other antigenic candidates for CE diagnosis and follow-up. Author summary: Cystic echinococcosis (CE) is a complex, neglected parasitic zoonosis. Tools and expertise in the diagnosis of CE are not widely available and the development of accurate and easily implementable diagnostic assays for CE has been highlighted as a priority also by the WHO. Antigen discovery for the immunodiagnosis of CE has relied so far on classical methods, which are impractical and poorly sensitive. Here, starting from the Echinococcus granulosus sensu stricto genome, we aimed to identify antigenic peptides having a potential for CE diagnosis based on a multiple-criteria strategy involving the bioinformatics selection of proteins, followed by screening by peptide microarray and validation by ELISA, using a panel of well-characterized sera from patients with hepatic CE in all development stages, and from clinically relevant controls. This methodology has virtually never been applied in the field of CE. Importantly, the developed database can be used to identify other E. granulosus s.s. antigenic candidates. [ABSTRACT FROM AUTHOR]

MicroRNAs (miRNAs) are responsible for regulating gene expression post-transcriptionally. Are involved in several biological processes, such as wound healing. Understanding the miRNAs involved in this process is fundamental for the development of new therapies. So, due to the need to understand the role of these molecules, we aimed systematically review the literature in order to identify which miRNAs are involved in the wound healing and determine, through bioinformatics analysis, which signaling pathways are associated with these miRNAs. An electronic search was performed in the following databases: National Library of Medicine National Institutes of Health (PubMed), Science Direct, Scifinder, Scopus and Web of Science, using the descriptors: "(microRNA [MeSH])" and "(skin [MeSH])" and "(wound healing [MeSH])". After the search, two independent and previously calibrated reviewers selected the articles that analyzed the expression pattern of miRNAs in wound healing in in vivo studies, using the software Zotero bibliography manager. Following, bioinformatic analysis was performed using the software DIANA Tools, mirPath v.3 and the data was interpreted. The bioinformatics analysis revealed that on the day 1 there were 13 union pathways, eight of which were statistically significant. Still on the day 1, among the miRNAs that had a decrease in their expression, 12 of 17 union pathways found were statistically significant. On the day 5, among the miRNAs with an increase in expression, 16 union pathways were found, 12 of which were statistically significant. Finally, among the miRNAs with decreased expression, 11 of 15 union pathways found were statistically significant. Although it has been found substantial heterogeneity in the studies, with this systematic review, it was possible to study the panorama of miRNAs that may be altered in the wound healing. The present review summarizes existing evidence of miRNAs associated to wound healing, and these findings can contribute to new therapeutic approaches. [ABSTRACT FROM AUTHOR]

The demand for well-trained bioinformaticians to support genomics research continues to rise. Unfortunately, undergraduate training in Kenya does not prepare students for specialization in bioinformatics. Graduates are often unaware of the career opportunities in bioinformatics, and those who are may lack mentors to help them choose a specialization. The Bioinformatics Mentorship and Incubation Program seeks to bridge the gap by laying the foundation for a bioinformatics training pipeline using project-based learning. The program selects six participants through an intensive open recruitment exercise for highly competitive students to join the program for four months. The six interns undergo intensive training within the first one and a half months before being assigned to mini-projects. We track the progress of the interns weekly through code review sessions and a final presentation at the end of the four months. We have trained five cohorts, most of whom have secured master's scholarships within and outside the country and job opportunities. We demonstrate the benefit of structured mentorship using project-based learning in filling the training gap after undergraduate programs to generate well-trained bioinformaticians who are competitive in graduate programs and bioinformatics jobs. [ABSTRACT FROM AUTHOR]

Sickle cell disease (SCD) is a common genetic disorder in Africa. Some ongoing work in SCD research includes the analysis and comparisons of variation in phenotypic presentations and disease outcomes with the genotypic signatures. This has contributed to the observed growth of molecular and genetic data in SCD. However, while the "omics" data continues to pile, the capacity to interpret and turn the genetic findings into clinical practice is still underdeveloped, especially in the developing region. Building bioinformatics infrastructure and capacity in the region is key to bridging the gap. This paper seeks to illustrate how the Sickle Cell Programme (SCP) at the Muhimbili University of Health and Allied Sciences (MUHAS) in Tanzania, modeled the integration of infrastructure for bioinformatics and clinical research while running day-to-day clinical care for SCD in Tanzania. [ABSTRACT FROM AUTHOR]

In Italian universities, bioinformatics courses are increasingly being incorporated into different study paths. However, the content of bioinformatics courses is usually selected by the professor teaching the course, in the absence of national guidelines that identify the minimum indispensable knowledge in bioinformatics that undergraduate students from different scientific fields should achieve. The Training&Teaching group of the Bioinformatics Italian Society (BITS) proposed to university professors a survey aimed at portraying the current situation of bioinformatics courses within undergraduate curricula in Italy (i.e., bioinformatics courses activated within both bachelor's and master's degrees). Furthermore, the Training&Teaching group took a cue from the survey outcomes to develop recommendations for the design and the inclusion of bioinformatics courses in academic curricula. Here, we present the outcomes of the survey, as well as the BITS recommendations, with the hope that they may support BITS members in identifying learning outcomes and selecting content for their bioinformatics courses. As we share our effort with the broader international community involved in teaching bioinformatics at academic level, we seek feedback and thoughts on our proposal and hope to start a fruitful debate on the topic, including how to better fulfill the real bioinformatics knowledge needs of the research and the labor market at both the national and international level. [ABSTRACT FROM AUTHOR]

Proliferative Diabetic Retinopathy (PDR) is a chronic complication of Diabetes and the main cause of blindness among the world's working population at present. While there have been many studies on the pathogenesis of PDR, its intrinsic molecular mechanisms have not yet been fully elucidated. In recent years, several studies have employed bulk RNA-sequencing (RNA-seq) and single-cell RNA sequencing (scRNA-seq) to profile differentially expressed genes (DEGs) and cellular components associated with PDR. This study adds to this expanding body of work by identifying PDR's target genes and cellular components by conducting an integrated transcriptome bioinformatics analysis. This study integrately examined two public bulk RNA-seq datasets(including 11 PDR patients and 7 controls) and one single-cell RNA-seq datasets(including 5 PDR patients) of Fibro (Vascular) Membranes (FVMs) from PDR patients and control. A total of 176 genes were identified as DEGs between PDR patients and control among both bulk RNA-seq datasets. Based on these DEGs, 14 proteins were identified in the protein overlap within the significant ligand-receptor interactions of retinal FVMs and Protein-Protein Interaction (PPI) network, three of which were associated with PDR (CD44, ICAM1, POSTN), and POSTN might act as key ligand. This finding may provide novel gene signatures and therapeutic targets for PDR. [ABSTRACT FROM AUTHOR]

Consensus clustering has been widely used in bioinformatics and other applications to improve the accuracy, stability and reliability of clustering results. This approach ensembles cluster co-occurrences from multiple clustering runs on subsampled observations. For application to large-scale bioinformatics data, such as to discover cell types from single-cell sequencing data, for example, consensus clustering has two significant drawbacks: (i) computational inefficiency due to repeatedly applying clustering algorithms, and (ii) lack of interpretability into the important features for differentiating clusters. In this paper, we address these two challenges by developing IMPACC: Interpretable MiniPatch Adaptive Consensus Clustering. Our approach adopts three major innovations. We ensemble cluster co-occurrences from tiny subsets of both observations and features, termed minipatches, thus dramatically reducing computation time. Additionally, we develop adaptive sampling schemes for observations, which result in both improved reliability and computational savings, as well as adaptive sampling schemes of features, which lead to interpretable solutions by quickly learning the most relevant features that differentiate clusters. We study our approach on synthetic data and a variety of real large-scale bioinformatics data sets; results show that our approach not only yields more accurate and interpretable cluster solutions, but it also substantially improves computational efficiency compared to standard consensus clustering approaches. Author summary: Clustering seeks to discover groups in big data with wide applications across scientific domains, especially in bioinformatics. However, for huge and sparse data sets common with genomic sequencing technologies, clustering methods can suffer from unreliable results, lack of interpretability in terms of feature importance, and heavy computational costs. To solve these challenges, we propose an extension of consensus clustering that leverages minipatch learning, an ensemble learning framework with learners trained on tiny subsets of observations and features. With adaptive sampling frameworks on both features and observations, our method is able to achieve higher clustering accuracy and reliability, as well as simultaneously identify scientifically important features that distinguish the clusters. In addition, we offer major computational improvements, with dramatically faster speed than our competitors. Our method is general and widely applicable to data sets from any field, and especially can offer superior performance when dealing with complex sparse and high dimensional data found in bioinformatics. [ABSTRACT FROM AUTHOR]

Background: Cell survival or death is one of the key scientific issues of inflammatory response. To regulate cell death during the occurrence and development of periodontitis, various forms of programmed cell death, such as pyroptosis, ferroptosis, necroptosis, and apoptosis, have been proposed. It has been found that ferroptosis characterized by iron-dependent lipid peroxidation is involved in cancer, degenerative brain diseases and inflammatory diseases. Furthermore, NCOA4 is considered one of ferroptosis-related genes (FRGs) contributing to butyrate-induced cell death in the periodontitis. This research aims to analyze the expression of FRGs in periodontitis tissues and to explore the relationship between ferroptosis and periodontitis. Method: Genes associated with periodontitis were retrieved from two Gene Expression Omnibus datasets. Then, we normalized microarray data and removed the batch effect using the R software. We used R to convert the mRNA expression data and collected the expression of FRGs. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), transcription factor (TF) and protein-protein interaction (PPI) network analyses were used. In addition, we constructed a receiver operating characteristic curve and obtained relative mRNA expression verified by quantitative reverse-transcription polymerase chain reaction (PCR). Results: Eight and 10 FRGs related to periodontitis were upregulated and downregulated, respectively. GO analysis showed that FRGs were enriched in the regulation of glutathione biosynthetic, glutamate homeostasis, and endoplasmic reticulum-nucleus signaling pathway. The top TFs included CEBPB, JUND, ATF2. Based on the PPI network analysis, FRGs were mainly linked to the negative regulation of IRE1-mediated unfolded protein response, regulation of type IIa hypersensitivity, and regulation of apoptotic cell clearance. The expression levels of NCOA4, SLC1A5 and HSPB1 using PCR were significantly different between normal gingival samples and periodontitis samples. Furthermore, the diagnostic value of FRGs for periodontitis were "Good". Conclusions: We found significant associations between FRGs and periodontitis. The present study not only provides a new possible pathomechanism for the occurrence of periodontitis but also offers a new direction for the diagnosis and treatment of periodontitis. [ABSTRACT FROM AUTHOR]

Text formats are common in bioinformatics, as they allow for editing and filtering using standard tools, as well as, since text formats are often human readable, manual inspection and evaluation of the data. Bioinformatics is a rapidly evolving field, hence, new techniques, new software tools, new kinds of data often require the definition of new formats. Often new formats are not formally described in a standard or specification document. Although software libraries are available for accessing the most common formats, writing parsers for text formats, for which no library is currently available, is a very common though tedious task, utilized by many researchers in the field. This manuscript presents the open source software library and toolset TextFormats (available at https://github.com/ggonnella/textformats), which aims at simplifying the definition and parsing of text formats. Formats specifications are written in a simple data description format using an interactive wizard. Automatic generation of data examples and automatic testing of specifications allow for checking for correctness. Given the specification for a text format, TextFormats allows parsing and writing data in that format, using several programming languages (Nim, Python, C/C++) or the provided command line and graphical user interface tools. Although designed as a general purpose software, the main target application field, for the above mentioned reasons, is expected to be in bioinformatics: Thus, the specifications of several common existing bioinformatics formats are included. [ABSTRACT FROM AUTHOR]

Integrated bioinformatics and statistical approaches are now playing the vital role in identifying potential molecular biomarkers more accurately in presence of huge number of alternatives for disease diagnosis, prognosis and therapies by reducing time and cost compared to the wet-lab based experimental procedures. Breast cancer (BC) is one of the leading causes of cancer related deaths for women worldwide. Several dry-lab and wet-lab based studies have identified different sets of molecular biomarkers for BC. But they did not compare their results to each other so much either computationally or experimentally. In this study, an attempt was made to propose a set of molecular biomarkers that might be more effective for BC diagnosis, prognosis and therapies, by using the integrated bioinformatics and statistical approaches. At first, we identified 190 differentially expressed genes (DEGs) between BC and control samples by using the statistical LIMMA approach. Then we identified 13 DEGs (AKR1C1, IRF9, OAS1, OAS3, SLCO2A1, NT5E, NQO1, ANGPT1, FN1, ATF6B, HPGD, BCL11A, and TP53INP1) as the key genes (KGs) by protein-protein interaction (PPI) network analysis. Then we investigated the pathogenetic processes of DEGs highlighting KGs by GO terms and KEGG pathway enrichment analysis. Moreover, we disclosed the transcriptional and post-transcriptional regulatory factors of KGs by their interaction network analysis with the transcription factors (TFs) and micro-RNAs. Both supervised and unsupervised learning's including multivariate survival analysis results confirmed the strong prognostic power of the proposed KGs. Finally, we suggested KGs-guided computationally more effective seven candidate drugs (NVP-BHG712, Nilotinib, GSK2126458, YM201636, TG-02, CX-5461, AP-24534) compared to other published drugs by cross-validation with the state-of-the-art alternatives top-ranked independent receptor proteins. Thus, our findings might be played a vital role in breast cancer diagnosis, prognosis and therapies. [ABSTRACT FROM AUTHOR]

Notice of RepublicationThis article was republished on May 10th, 2024, to correct errors in the title. Please download this article again to view the correct version. The publisher apologizes for the error. The originally published, uncorrected article and the republished, corrected articles are provided here for reference.Supporting informationS1 FileOriginally published, uncorrected article.(PDF)S2 FileRepublished, corrected article.(PDF) [Extracted from the article]

Identifying genes with the largest expression changes (gene selection) to characterize a given condition is a popular first step to drive exploration into molecular mechanisms and is, therefore, paramount for therapeutic development. Reproducibility in the sciences makes it necessary to emphasize objectivity and systematic repeatability in biological and informatics analyses, including gene selection. With these two characteristics in mind, in previous works our research team has proposed using multiple criteria optimization (MCO) in gene selection to analyze microarray datasets. The result of this effort is the MCO algorithm, which selects genes with the largest expression changes without user manipulation of neither informatics nor statistical parameters. Furthermore, the user is not required to choose either a preference structure among multiple measures or a predetermined quantity of genes to be deemed significant a priori. This implies that using the same datasets and performance measures (PMs), the method will converge to the same set of selected differentially expressed genes (repeatability) despite who carries out the analysis (objectivity). The present work describes the development of an open-source tool in RStudio to enable both: (1) individual analysis of single datasets with two or three PMs and (2) meta-analysis with up to five microarray datasets, using one PM from each dataset. The capabilities afforded by the code include license-free portability and the possibility to carry out analyses via modest computer hardware, such as personal laptops. The code provides affordable, repeatable, and objective detection of differentially expressed genes from microarrays. It can be used to analyze other experiments with similar experimental comparative layouts, such as microRNA arrays and protein arrays, among others. As a demonstration of the capabilities of the code, the analysis of four publicly-available microarray datasets related to Parkinson´s Disease (PD) is presented here, treating each dataset individually or as a four-way meta-analysis. These MCO-supported analyses made it possible to identify MMP9 and TUBB2A as potential PD genetic biomarkers based on their persistent appearance across each of the case studies. A literature search confirmed the importance of these genes in PD and indeed as PD biomarkers, which evidences the code´s potential. [ABSTRACT FROM AUTHOR]

Over the last few decades, the nature of life sciences research has changed enormously, generating a need for a workforce with a variety of computational skills such as those required to store, manage, and analyse the large biological datasets produced by next-generation sequencing. Those with such expertise are increasingly in demand for employment in both research and industry. Despite this, bioinformatics education has failed to keep pace with advances in research. At secondary school level, computing is often taught in isolation from other sciences, and its importance in biological research is not fully realised, leaving pupils unprepared for the computational component of Higher Education and, subsequently, research in the life sciences. The 4273pi Bioinformatics at School project (https://4273pi.org) aims to address this issue by designing and delivering curriculum-linked, hands-on bioinformatics workshops for secondary school biology pupils, with an emphasis on equitable access. So far, we have reached over 180 schools across Scotland through visits or teacher events, and our open education resources are used internationally. Here, we describe our project, our aims and motivations, and the practical lessons we have learned from implementing a successful bioinformatics education project over the last 5 years. [ABSTRACT FROM AUTHOR]

Introduction: The coronavirus disease 2019 (COVID-19), emerged in late 2019, was caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The risk factors for idiopathic pulmonary fibrosis (IPF) and COVID-19 are reported to be common. This study aimed to determine the potential role of differentially expressed genes (DEGs) common in IPF and COVID-19. Materials and methods: Based on GEO database, we obtained DEGs from one SARS-CoV-2 dataset and five IPF datasets. A series of enrichment analysis were performed to identify the function of upregulated and downregulated DEGs, respectively. Two plugins in Cytoscape, Cytohubba and MCODE, were utilized to identify hub genes after a protein-protein interaction (PPI) network. Finally, candidate drugs were predicted to target the upregulated DEGs. Results: A total of 188 DEGs were found between COVID-19 and IPF, out of which 117 were upregulated and 71 were downregulated. The upregulated DEGs were involved in cytokine function, while downregulated DEGs were associated with extracellular matrix disassembly. Twenty-two hub genes were upregulated in COVID-19 and IPF, for which 155 candidate drugs were predicted (adj.P.value < 0.01). Conclusion: Identifying the hub genes aberrantly regulated in both COVID-19 and IPF may enable development of molecules, encoded by those genes, as therapeutic targets for preventing IPF progression and SARS-CoV-2 infections. [ABSTRACT FROM AUTHOR]

Cancer is one of the most important public health problems in the world. The curative effect of traditional surgery, radiotherapy and chemotherapy is limited and has inevitable side effects. As a potential target for tumor therapy, few studies have comprehensively analyzed the role of CALR in cancers. Therefore, by using GeneCards, UALCAN, GEPIA, Kaplan-Meier Plotter, COSMIC, Regulome Explorer, String, GeneMANIA and TIMER databases, we collected and analyzed relevant data to conduct in-depth bioinformatics research on the CALR expression in Pan-cancer to assess the possibility of CALR as a potential therapeutic target and survival biomarker. We studied the CALR expression in normal human tissues and various tumors of different stages, and found that CALR expression was associated with relapse free survival (RFS). We verified the expression of CALR in breast cancer cell lines by vitro experiments. Mutations of CALR were widely present in tumors. CALR interacted with different genes and various proteins. In tumors, a variety of immune cells are closely related to CALR. In conclusion, CALR can be used as a biomarker for predicting prognosis and a potential target for tumor molecular and immunotherapy. [ABSTRACT FROM AUTHOR]

While it is not completely necessary to assess the demand for bioinformatics training in this approach, the integration of a bioinformatics elective into strategically selected undergraduate programs will facilitate the uptake of the course. Rule 4d: Create a bioinformatics research group at the faculty level or a national center of... The establishment of a bioinformatics center requires a cadre of independent researchers capable of producing high-quality competitive bioinformatics research. The benefits of introducing bioinformatics courses at the undergraduate level have previously been illustrated and include substantial learning gains in terms of bioinformatics confidence and ability [[13]] and increased interest and enthusiasm for bioinformatics [[14]]. [Extracted from the article]

Background: Liver hepatocellular carcinoma (LIHC) is one of the most common malignant tumors. However, the etiology and exact molecular mechanism of LIHC are still not fully understood, which makes it urgent for us to further study the molecular events behind. Methods: In this study, differences in mRNA expression between LIHC samples and normal adjacent samples were found through analyzing the TCGA database, and key targets were sought. We analyzed 371 LIHC samples and 50 normal adjacent samples according to P <0.01 and logFC>2.5, a total of 1092 genes were identified differentially expressed, including 995 up-regulated genes and 97 down-regulated genes. We predicted the interactions of these differentially expressed mRNAs, and used Cyto-Hubba to locate the hub gene-dynein cytoplasmic 1 intermediate chain 1 (DYNC1I1). Results: Survival analysis showed that DYNC1I1 was a prognostic factor for LIHC male patients. Functional enrichment indicated that DYNC1I1 and differentially expressed interacting proteins were involved in the cell cycle. Conclusion: In conclusion, this study discovers that DYNC1I1 can be used as a prognostic marker for LIHC male patients. [ABSTRACT FROM AUTHOR]

The ever increasing applications of bioinformatics in providing effective interpretation of large and complex biological data require expertise in the use of sophisticated computational tools and advanced statistical tests, skills that are mostly lacking in the Sudanese research community. This can be attributed to paucity in the development and promotion of bioinformatics, lack of senior bioinformaticians, and the general status quo of inadequate research funding in Sudan. In this paper, we describe the challenges that have encountered the development of bioinformatics as a discipline in Sudan. Additionally, we highlight on specific actions that may help develop and promote its education and training. The paper takes the National University Biomedical Research Institute (NUBRI) as an example of an institute that has tackled many of these challenges and strives to drive powerful efforts in the development of bioinformatics in the country. Author summary: Bioinformatics is gaining recognition globally and in Sudan as an important subdiscipline of biological sciences, one that enables researchers to efficiently interpret complex biological data. A limited number of Sudanese academic institutions have acknowledged this field despite its increasingly recognized importance. The development of bioinformatics in the country requires interdisciplinary collaborations involving experts in life sciences, research methodology, healthcare, computer, and data sciences. This can be achieved through designing educational programs and workshops alongside proposing and establishing effective collaborative research projects. In this context, we comprehensively discussed the present state of bioinformatics in Sudan, the challenges faced, as well as the efforts exerted by academic institutions including NUBRI, to upgrade infrastructure and establish local and international collaborations. [ABSTRACT FROM AUTHOR]

Many important questions remain regarding severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), the viral pathogen responsible for COVID-19. These questions include the mechanisms explaining the high percentage of asymptomatic but highly infectious individuals, the wide variability in disease susceptibility, and the mechanisms of long-lasting debilitating effects. Bioinformatic analysis of four coronavirus datasets representing previous outbreaks (SARS-CoV-1 and MERS-CoV), as well as SARS-CoV-2, revealed evidence of diverse host factors that appear to be coopted to facilitate virus-induced suppression of interferon-induced innate immunity, promotion of viral replication and subversion and/or evasion of antiviral immune surveillance. These host factors merit further study given their postulated roles in COVID-19-induced loss of smell and brain, heart, vascular, lung, liver, and gut dysfunction. [ABSTRACT FROM AUTHOR]

As powerful computational tools and 'big data' transform the biological sciences, bioinformatics training is becoming necessary to prepare the next generation of life scientists. Furthermore, because the tools and resources employed in bioinformatics are constantly evolving, bioinformatics learning materials must be continuously improved. In addition, these learning materials need to move beyond today's typical step-by-step guides to promote deeper conceptual understanding by students. One of the goals of the Network for Integrating Bioinformatics into Life Sciences Education (NIBSLE) is to create, curate, disseminate, and assess appropriate open-access bioinformatics learning resources. Here we describe the evolution, integration, and assessment of a learning resource that explores essential concepts of biological sequence similarity. Pre/post student assessment data from diverse life science courses show significant learning gains. These results indicate that the learning resource is a beneficial educational product for the integration of bioinformatics across curricula. [ABSTRACT FROM AUTHOR]

This coupled with various other barriers such as access to training resources, access to skilled trainers, remote locations, and the relatively high costs associated with training activities act as major barriers to delivering training in LMICs. Bioinformatics training is required at every stage of a scientist's research career. Funding for training is often available through dedicated grants for training or through explicit budget lines in research projects that aim to build bioinformatics capacity. One of the important strands of these initiatives is a drive to organize and deliver valuable bioinformatics training, but organizing and delivering short bioinformatics training workshops in an LMIC present a unique set of challenges. [Extracted from the article]

Extramammary Paget's disease (EMPD) is an intra-epidermal adenocarcinoma. Till now, the mechanisms underlying the pathogenesis of scrotal EMPD is poorly known. This present study aims to explore the knowledge of molecular mechanism of scrotal EMPD by identifying the hub genes and candidate drugs using integrated bioinformatics approaches. Firstly, the microarray datasets (GSE117285) were downloaded from the GEO database and then analyzed using GEO2R in order to obtain differentially expressed genes (DEGs). Moreover, hub genes were identified on the basis of their degree of connectivity using Cytohubba plugin of cytoscape tool. Finally, GEPIA and DGIdb were used for the survival analysis and selection of therapeutic candidates, respectively. A total of 786 DEGs were identified, of which 10 genes were considered as hub genes on the basis of the highest degree of connectivity. After the survival analysis of ten hub genes, a total of 5 genes were found to be altered in EMPD patients. Furthermore, 14 drugs of CHEK1, CCNA2, and CDK1 were found to have therapeutic potential against EMPD. This study updates the information and yields a new perspective in the context of understanding the pathogenesis of EMPD. In future, hub genes and candidate drugs might be capable of improving the personalized detection and therapies for EMPD. [ABSTRACT FROM AUTHOR]

In October of 2020, in response to the Coronavirus Disease 2019 (COVID-19) pandemic, our team hosted our first fully online workshop teaching the QIIME 2 microbiome bioinformatics platform. We had 75 enrolled participants who joined from at least 25 different countries on 6 continents, and we had 22 instructors on 4 continents. In the 5-day workshop, participants worked hands-on with a cloud-based shared compute cluster that we deployed for this course. The event was well received, and participants provided feedback and suggestions in a postworkshop questionnaire. In January of 2021, we followed this workshop with a second fully online workshop, incorporating lessons from the first. Here, we present details on the technology and protocols that we used to run these workshops, focusing on the first workshop and then introducing changes made for the second workshop. We discuss what worked well, what didn't work well, and what we plan to do differently in future workshops. [ABSTRACT FROM AUTHOR]

The Coronavirus Disease 2019 (COVID-19) outbreaks have caused universities all across the globe to close their campuses and forced them to initiate online teaching. This article reviews the pedagogical foundations for developing effective distance education practices, starting from the assumption that promoting autonomous thinking is an essential element to guarantee full citizenship in a democracy and for moral decision-making in situations of rapid change, which has become a pressing need in the context of a pandemic. In addition, the main obstacles related to this new context are identified, and solutions are proposed according to the existing bibliography in learning sciences. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is the commonest progressive neurodegenerative condition in humans, and is currently incurable. A wide spectrum of comorbidities, including other neurodegenerative diseases, are frequently associated with AD. How AD interacts with those comorbidities can be examined by analysing gene expression patterns in affected tissues using bioinformatics tools. We surveyed public data repositories for available gene expression data on tissue from AD subjects and from people affected by neurodegenerative diseases that are often found as comorbidities with AD. We then utilized large set of gene expression data, cell-related data and other public resources through an analytical process to identify functional disease links. This process incorporated gene set enrichment analysis and utilized semantic similarity to give proximity measures. We identified genes with abnormal expressions that were common to AD and its comorbidities, as well as shared gene ontology terms and molecular pathways. Our methodological pipeline was implemented in the R platform as an open-source package and available at the following link: https://github.com/unchowdhury/AD%5fcomorbidity. The pipeline was thus able to identify factors and pathways that may constitute functional links between AD and these common comorbidities by which they affect each others development and progression. This pipeline can also be useful to identify key pathological factors and therapeutic targets for other diseases and disease interactions. [ABSTRACT FROM AUTHOR]

There are errors in the Funding section. The correct Funding statement is: This work was supported by the National Natural Science Foundation of China (NO.81470881 and NO.82172956), the Jiangsu Commission of Health (LGY2017031 and BRA2015473). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. There was no additional external funding received for this study.By Lei Xu; Hongqiang Zhang; Yu Shao and Zan FuReported by Author; Author; Author; Author [Extracted from the article]

Many initiatives have addressed the global need to upskill biologists in bioinformatics tools and techniques. Australia is not unique in its requirement for such training, but due to its large size and relatively small and geographically dispersed population, Australia faces specific challenges. A combined training approach was implemented by the authors to overcome these challenges. The "hybrid" method combines guidance from experienced trainers with the benefits of both webinar-style delivery and concurrent face-to-face hands-on practical exercises in classrooms. Since 2017, the hybrid method has been used to conduct 9 hands-on bioinformatics training sessions at international scale in which over 800 researchers have been trained in diverse topics on a range of software platforms. The method has become a key tool to ensure scalable and more equitable delivery of short-course bioinformatics training across Australia and can be easily adapted to other locations, topics, or settings. [ABSTRACT FROM AUTHOR]

With more microbiome studies being conducted by African-based research groups, there is an increasing demand for knowledge and skills in the design and analysis of microbiome studies and data. However, high-quality bioinformatics courses are often impeded by differences in computational environments, complicated software stacks, numerous dependencies, and versions of bioinformatics tools along with a lack of local computational infrastructure and expertise. To address this, H3ABioNet developed a 16S rRNA Microbiome Intermediate Bioinformatics Training course, extending its remote classroom model. The course was developed alongside experienced microbiome researchers, bioinformaticians, and systems administrators, who identified key topics to address. Development of containerised workflows has previously been undertaken by H3ABioNet, and Singularity containers were used here to enable the deployment of a standard replicable software stack across different hosting sites. The pilot ran successfully in 2019 across 23 sites registered in 11 African countries, with more than 200 participants formally enrolled and 106 volunteer staff for onsite support. The pulling, running, and testing of the containers, software, and analyses on various clusters were performed prior to the start of the course by hosting classrooms. The containers allowed the replication of analyses and results across all participating classrooms running a cluster and remained available posttraining ensuring analyses could be repeated on real data. Participants thus received the opportunity to analyse their own data, while local staff were trained and supported by experienced experts, increasing local capacity for ongoing research support. This provides a model for delivering topic-specific bioinformatics courses across Africa and other remote/low-resourced regions which overcomes barriers such as inadequate infrastructures, geographical distance, and access to expertise and educational materials. [ABSTRACT FROM AUTHOR]

Selecting the most suitable pipeline or tool will be dependent on a number of factors including: Your target species and quality of data Some bioinformatic pipelines/software may work better for a particular species based on their unique features (e.g., genome size, repeat complexity, ploidy, etc.) or based on the quality of data (e.g., scaffold length, short reads versus long reads, etc.). While software installation is sometimes provided as a service for some shared HPC platforms, understanding the basics of software installation is useful in helping you troubleshoot any installation-based errors and identify which software you can likely install locally yourself (i.e., without requiring root user privileges). Common software installation methods for bioinformatics tools Package managers APT (Advanced Package Tool) (https://www.debian.org/doc/manuals/apt-guide/index.en.html) is a package manager that is often already installed by default on many Debian distributions and enables very simple installation of available tools. This feature makes conda extremely useful, particularly for bioinformatics software where different pipelines may utilise the same tools but require different versions of a particular tool. [Extracted from the article]

Low-grade gliomas (LGGs) is a primary invasive brain tumor that grows slowly but is incurable and eventually develops into high malignant glioma. Novel biomarkers for the tumorigenesis and lifetime of LGG are critically demanded to be investigated. In this study, the expression levels of procollagen-lysine, 2-oxoglutarate 5-dioxygenases (PLODs) were analyzed by ONCOMINE, HPA and GEPIA. The GEPIA online platform was applied to evaluate the interrelation between PLODs and survival index in LGG. Furthermore, functions of PLODs and co-expression genes were inspected by the DAVID. Moreover, we used TIMER, cBioportal, GeneMINIA and NetworkAnalyst analysis to reveal the mechanism of PLODs in LGG. We found that expression levels of each PLOD family members were up-regulated in patients with LGG. Higher expression of PLODs was closely related to shorter disease-free survival (DFS) and overall survival (OS). The findings showed that LGG cases with or without alterations were significantly correlated with the OS and DFS. The mechanism of PLODs in LGG may be involved in response to hypoxia, oxidoreductase activity, Lysine degradation and immune cell infiltration. In general, this research has investigated the values of PLODs in LGG, which could serve as biomarkers for diagnosis, prognosis and potential therapeutic targets of LGG patients. [ABSTRACT FROM AUTHOR]

With the recent research development, the importance of microRNAs (miRNAs) in renal clear cell carcinoma (CCRCC) has become widely known. The purpose of this study is to screen out the potential biomarkers of renal clear cell carcinoma (CCRCC) by microarray analysis. The miRNA chip (GSE16441) and mRNA chip (GSE66270) related to CCRCC were downloaded from the Gene Expression Omnibus (GEO) database. After data filtering and pretreating, R platform and a series of analysis tools (funrich3.1.3, string, Cytoscape_ 3.2.1, David, etc.) were used to analyze chip data and identify the specific and highly sensitive biomarkers. Finally, by constructing the miRNA -mRNA interaction network, it was determined that five miRNAs (hsa-mir-199a-5p, hsa-mir-199b-5p, hsa-mir-532-3p and hsa-mir-429) and two key genes (ETS1 and hapln1) are significantly related to the overall survival rate of patients. [ABSTRACT FROM AUTHOR]

In this work, we present a diagnostic analysis of strengths, weaknesses, opportunities and threats (SWOT) of the current state of Bioinformatics in Mexico. We conducted semi-structured interviews among researchers and academics with key expertise in this field, identified by bibliometric analyses and qualitative sampling techniques. Additionally, an online survey was conducted reaching a higher number of respondents. Among the relevant findings of our study, the lack of specialized human resources and technological infrastructure stood out, along with deficiencies in the number and quality of academic programs, scarce public investment and a weak relationship between public and private institutions. However, there are great opportunities for developing a national Bioinformatics to support different economic sectors. In our opinion, this work could be useful to favor a comprehensive network among Mexican researchers, in order to lay the foundations of a national strategy towards a well designed public policy. [ABSTRACT FROM AUTHOR]

As the life sciences have become more data intensive, the pressure to incorporate the requisite training into life-science education and training programs has increased. To facilitate curriculum development, various sets of (bio)informatics competencies have been articulated; however, these have proved difficult to implement in practice. Addressing this issue, we have created a curriculum-design and -evaluation tool to support the development of specific Knowledge, Skills and Abilities (KSAs) that reflect the scientific method and promote both bioinformatics practice and the achievement of competencies. Twelve KSAs were extracted via formal analysis, and stages along a developmental trajectory, from uninitiated student to independent practitioner, were identified. Demonstration of each KSA by a performer at each stage was initially described (Performance Level Descriptors, PLDs), evaluated, and revised at an international workshop. This work was subsequently extended and further refined to yield the Mastery Rubric for Bioinformatics (MR-Bi). The MR-Bi was validated by demonstrating alignment between the KSAs and competencies, and its consistency with principles of adult learning. The MR-Bi tool provides a formal framework to support curriculum building, training, and self-directed learning. It prioritizes the development of independence and scientific reasoning, and is structured to allow individuals (regardless of career stage, disciplinary background, or skill level) to locate themselves within the framework. The KSAs and their PLDs promote scientific problem formulation and problem solving, lending the MR-Bi durability and flexibility. With its explicit developmental trajectory, the tool can be used by developing or practicing scientists to direct their (and their team's) acquisition of new, or to deepen existing, bioinformatics KSAs. The MR-Bi is a tool that can contribute to the cultivation of a next generation of bioinformaticians who are able to design reproducible and rigorous research, and to critically analyze results from their own, and others', work. [ABSTRACT FROM AUTHOR]

Thrips transmit one of the most devastating plant viruses worldwide–tomato spotted wilt tospovirus (TSWV). Tomato spotted wilt tospovirus is a type species in the genus Orthotospovirus and family Tospoviridae. Although there are more than 7,000 thrips species, only nine thrips species are known to transmit TSWV. In this study, we investigated the molecular factors that could affect thrips ability to transmit TSWV. We assembled transcriptomes of a vector, Frankliniella fusca [Hinds], and a non-vector, Frankliniella tritici [Fitch], and performed qualitative comparisons of contigs associated with virus reception, virus infection, and innate immunity. Annotations of F. fusca and F. tritici contigs revealed slight differences across biological process and molecular functional groups. Comparison of virus cell surface receptors revealed that homologs of integrin were present in both species. However, homologs of another receptor, heperan sulfate, were present in F. fusca alone. Contigs associated with virus replication were identified in both species, but a contig involved in inhibition of virus replication (radical s-adenosylmethionine) was only present in the non-vector, F. tritici. Additionally, some differences in immune signaling pathways were identified between vector and non-vector thrips. Detailed investigations are necessary to functionally characterize these differences between vector and non-vector thrips and assess their relevance in orthotospovirus transmission. [ABSTRACT FROM AUTHOR]

Superoxide dismutases (SODs), as a family of metalloenzymes related to the removal of reactive oxygen species (ROS), have not previously been investigated at genome-wide level in tea plant. In this study, 10 CsSOD genes were identified in tea plant genome, including 7 Cu/Zn-SODs (CSDs), 2 Fe-SODs (FSDs) and one Mn-SOD (MSD), and phylogenetically classified in three subgroups, respectively. Physico-chemical characteristic, conserved motifs and potential protein interaction analyses about CsSOD proteins were carried out. Exon-intron structures and codon usage bias about CsSOD genes were also examined. Exon-intron structures analysis revealed that different CsSOD genes contained various number of introns. On the basis of the prediction of regulatory miRNAs of CsSODs, a modification 5' RNA ligase-mediated (RLM)-RACE was performed and validated that csn-miR398a-3p-1 directly cleaves CsCSD4. By prediction of cis-acting elements, the expression patterns of 10 CsSOD genes and their regulatory miRNAs were detected under cold, drought, exogenous methyl jasmonate (MeJA) and gibberellin (GA3) treatments. The results showed that most of CsSODs except for CsFSD2 were induced under cold stress and CsCSDs may play primary roles under drought stress; exogenous GA3 and MeJA could also stimulated/inhibited distinct CsSODs at different stages. In addition, we found that csn-miR398a-3p-1 negatively regulated the expression of CsCSD4 may be a crucial regulatory mechanism under cold stress. This study provides a certain basis for the studies about stress resistance in tea plants, even provide insight into comprehending the classification, evolution, diverse functions and influencing factors of expression patterns for CsSOD genes. [ABSTRACT FROM AUTHOR]

Neisseria gonorrhoeae antimicrobial resistance (AMR) surveillance is essential for tracking the emergence and spread of AMR strains in local, national and international populations. This is crucial for developing or refining treatment guidelines. N. gonorrhoeae multiantigen sequence typing (NG-MAST) is beneficial for describing the molecular epidemiology of gonococci at national and international levels. Elucidation of AMR determinants to β-lactam drugs, is a means of monitoring the development of resistance. In Ghana, little is known about the current gonococcal AMR prevalence and no characterization of gonococcal isolates has been previously performed. In this study, gonococcal isolates (n = 44) collected from five health facilities in Ghana from 2012 to 2015, were examined using AMR testing, NG-MAST and sequencing of penA. High rates of resistance were identified to tetracycline (100%), benzylpenicillin (90.9%), and ciprofloxacin (81.8%). One isolate had a high cefixime MIC (0.75 μg/ml). Twenty-eight NG-MAST sequence types (STs) were identified, seventeen of which were novel. The isolate with the high cefixime MIC contained a mosaic penA-34 allele and belonged to NG-MAST ST1407, an internationally spreading multidrug-resistant clone that has accounted for most cefixime resistance in many countries. In conclusion, AMR testing, NG-MAST, and sequencing of the AMR determinant penA, revealed high rates of resistance to tetracycline, benzylpenicillin, and ciprofloxacin; as well as a highly diverse population of N. gonorrhoeae in Ghana. It is imperative to continue with enhanced AMR surveillance and to understand the molecular epidemiology of gonococcal strains circulating in Ghana and other African countries. [ABSTRACT FROM AUTHOR]

Candida auris is an invasive and multidrug-resistant ascomycetous yeast that is under global surveillance. All clinical cases of C. auris infection diagnosed from 1997 to 2019 in Japan were non-invasive and sporadic otitis media cases. In the present study, we performed whole-genome sequencing of seven C. auris strains isolated from patients with otitis media in Japan, all of which belonged to clade II. Comparative genome analysis using the high-quality draft genome sequences JCM 15448T revealed that single nucleotide variations (SNVs), clade-specific accessory genes, and copy number variations (CNVs) were identified in each C. auris clade. A total of 61 genes involved in cell wall and stress response-related functions was absent in clade II, and the pattern of conserved CNVs in each clade was more stable in clade II than in other clades. Our data suggest that the genomic structural diversity is stable in C. auris isolated from each biogeographic location, and Japanese strains isolated from patients with otitis media might belong to an ancestral type of C. auris. One Japanese strain, TWCC 58362, with reduced susceptibility to fluconazole, exhibited no mutation in ergosterol biosynthesis-related genes (ERG). However, TWCC 58362-specific variations, including SNVs, indels, and CNVs were detected, suggesting that gene duplication events in C. auris might contribute to antifungal drug resistance. Taken together, we demonstrated that genomic structural variations in C. auris could correlate to geographical dissemination, epidemiology, lesions in the host, and antifungal resistance. [ABSTRACT FROM AUTHOR]

The spliceosome is a large ribonucleoprotein complex that removes introns from pre-mRNAs. At its functional core lies the essential pre-mRNA processing factor 8 (Prp8) protein. Across diverse eukaryotes, this protein cofactor of RNA catalysis harbors a self-splicing element called an intein. Inteins in Prp8 are extremely pervasive and are found at 7 different sites in various species. Here, we focus on the Prp8 intein from Cryptococcus neoformans (Cne), a human fungal pathogen. We solved the crystal structure of this intein, revealing structural homology among protein splicing sequences in eukaryotes, including the Hedgehog C terminus. Working with the Cne Prp8 intein in a reporter assay, we find that the biologically relevant divalent metals copper and zinc inhibit intein splicing, albeit by 2 different mechanisms. Copper likely stimulates reversible modifications on a catalytically important cysteine, whereas zinc binds at the terminal asparagine and the same critical cysteine. Importantly, we also show that copper treatment inhibits Prp8 protein splicing in Cne. Lastly, an intein-containing Prp8 precursor model is presented, suggesting that metal-induced protein-splicing inhibition would disturb function of both Prp8 and the spliceosome. These results indicate that Prp8 protein splicing can be modulated, with potential functional implications for the spliceosome. [ABSTRACT FROM AUTHOR]

Phosphohexomutase superfamily enzymes catalyze the reversible intramolecular transfer of a phosphoryl moiety on hexose sugars. Bacillus subtilis phosphoglucomutase PgcA catalyzes the reversible interconversion of glucose 6-phosphate (Glc-6-P) and glucose 1-phosphate (Glc-1-P), a precursor of UDP-glucose (UDP-Glc). B. subtilis phosphoglucosamine mutase (GlmM) is a member of the same enzyme superfamily that converts glucosamine 6-phosphate (GlcN-6-P) to glucosamine 1-phosphate (GlcN-1-P), a precursor of the amino sugar moiety of peptidoglycan. Here, we present evidence that B. subtilis PgcA possesses activity as a phosphoglucosamine mutase that contributes to peptidoglycan biosynthesis. This activity was made genetically apparent by the synthetic lethality of pgcA with glmR, a positive regulator of amino sugar biosynthesis, which can be specifically suppressed by overproduction of GlmM. A gain-of-function mutation in a substrate binding loop (PgcA G47S) increases this secondary activity and suppresses a glmR mutant. Our results demonstrate that bacterial phosphoglucomutases may possess secondary phosphoglucosamine mutase activity, and that this dual activity may provide some level of functional redundancy for the essential peptidoglycan biosynthesis pathway. [ABSTRACT FROM AUTHOR]

Recent evidence implicates mononuclear diploid cardiomyocytes as a proliferative and regenerative subpopulation of the postnatal heart. The number of these cardiomyocytes is a complex trait showing substantial natural variation among inbred mouse strains based on the combined influences of multiple polymorphic genes. One gene confirmed to influence this parameter is the cardiomyocyte-specific kinase Tnni3k. Here, we have studied Tnni3k alleles across a number of species. Using a newly-generated kinase-dead allele in mice, we show that Tnni3k function is dependent on its kinase activity. In an in vitro kinase assay, we show that several common human TNNI3K kinase domain variants substantially compromise kinase activity, suggesting that TNNI3K may influence human heart regenerative capacity and potentially also other aspects of human heart disease. We show that two kinase domain frameshift mutations in mice cause loss-of-function consequences by nonsense-mediated decay. We further show that the Tnni3k gene in two species of mole-rat has independently devolved into a pseudogene, presumably associated with the transition of these species to a low metabolism and hypoxic subterranean life. This may be explained by the observation that Tnni3k function in mice converges with oxidative stress to regulate mononuclear diploid cardiomyocyte frequency. Unlike other studied rodents, naked mole-rats have a surprisingly high (30%) mononuclear cardiomyocyte level but most of their mononuclear cardiomyocytes are polyploid; their mononuclear diploid cardiomyocyte level (7%) is within the known range (2–10%) of inbred mouse strains. Naked mole-rats provide further insight on a recent proposal that cardiomyocyte polyploidy is associated with evolutionary acquisition of endothermy. [ABSTRACT FROM AUTHOR]

The genus Franciscobasis Machado & Bedê, 2016 is endemic to the Serra da Canastra National Park in Minas Gerais state, Brazil. Two species of Franciscobasis were described simultaneously with the genus description: F. franciscoi and F. sonia, the latter described only from females. Through morphological and molecular analysis, we investigated if F. sonia may represent the young female of F. franciscoi. Resulting data did not present adequate differences between females to characterize them as different species. Therefore, we suggest that F. sonia is a junior synonym of F. franciscoi, and the female of F. franciscoi goes through a complex ontogenetic color change. [ABSTRACT FROM AUTHOR]

Hepatitis C virus (HCV) is a major cause of liver cirrhosis and hepatocellular carcinoma. Genotyping of HCV is crucial for successful therapy. To determine the HCV subgenotypes circulating in Palestine and to study the genetic variability of their core, we collected 84 serum samples which had tested positive for anti-HCV antibodies. Thirty-seven of these samples came from hemodialysis patients. Serum samples were subjected to viral RNA isolation and amplification of the HCV core gene. Thirty-three of the samples (39%) tested positive for HCV RNA. The HCV subgenotypes circulating in Palestine included 1a, 3a, and 4a, detected in 38%, 25%, and 22% of the samples, respectively. Furthermore, subgenotype 1b was present in three samples (9%), while the rare subgenotype 4v was present in two samples (6%). We identified a number of substitutions in the retrieved HCV core sequences, such as HCV 1b substitutions R70Q and M91L, which some studies have associated with hepatocellular carcinoma risk and poor virological response. In contrast to two previous studies reporting that HCV genotype 4 was predominant in the Gaza strip (present in just over 70% of samples), genotype 4 was detected in only 31% of the samples in our current study, whereas genotype 1 and 3 were present in 69% of samples. These differences may relate to the fact that many of our samples came from the West Bank and East Jerusalem. The co-circulation of different HCV genotypes and subgenotypes in Palestine suggests that subgenotyping prior to treatment is crucial in Palestinian patients. [ABSTRACT FROM AUTHOR]

Alternative splicing diversifies mRNA transcripts in human cells. While the spliceosome pairs exons with a high degree of accuracy, the rates of rare aberrant and non-canonical pre-mRNA splicing have not been evaluated at the nucleotide level to determine the quantity and identity of these events across splice junctions. Using ultra-deep sequencing the frequency of aberrant and non-canonical splicing events for three splice junctions flanking exon 7 of SMN1 were determined at single nucleotide resolution. After correction for background noise introduced by PCR amplification and sequencing steps, pre-mRNA splicing was shown to maintain a low overall rate of aberrant and non-canonically spliced events. Several previously unannotated splicing events across 3 exon|intron junctions in SMN1 were identified. Mutations within SMN exon 7 were shown to affect splicing fidelity by modulating RNA secondary structures, by altering the binding site of regulatory proteins and by changing the 5’ splice site strength. Mutations also create a truncated SMN1 exon 7 through the introduction of a de novo non-canonical 5’ splice site. The results from the ultra-deep sequencing approach highlight the impressive fidelity of pre-mRNA splicing and demonstrate that the immediate sequence context around splice sites is the main driving force behind non-canonical splice site pairing. [ABSTRACT FROM AUTHOR]

Background: Detection and pathology analysis of Mycobacterium leprae using skin biopsy tissues are essential for leprosy diagnosis and monitoring response to treatment. Although formalin fixation of patient tissues may not be ideal for molecular studies, biopsy samples are the most accessible material from suspected cases. Therefore, clinical molecular laboratories must be able to utilize formalin-fixed, paraffin-embedded (FFPE) material. Objective: To determine the best molecular method for diagnosing and monitoring leprosy in FFPE specimens, we developed a single-tube nested PCR (STNPCR) (131 bp) and SYBRGreen PCR (101 bp) assay using primers for the M. leprae-specific repetitive element (RLEP) gene and evaluated the results compared to those using previously established RLEP primers (372 bp). Methods: FFPE biopsy samples obtained from 145 leprosy patients (during or after multidrug therapy (MDT)) and patients with 29 other confounding dermatoses were examined by the bacteria index (BI) and by simple PCR, STNPCR, and SYBRGreen PCR using primers amplifying a 372-bp, 131-bp or 101-bp fragment of RLEP, respectively. Results: In leprosy patients receiving MDT, STNPCR showed a highest specificity of 100% and a positive predictive value (PPV) of 100%. For multibacillary (MB), paucibacillary (PB) and all leprosy patients, the highest sensitivities were 91.42%, 39.13%, and 67.92%, negative predictive values (NPVs) were 8.57%, 60.36%, and 32.07%, and the highest accuracies were 93.93%, 62.67%, and 74.81%, respectively, higher than the results of SYBRGreen PCR and simple PCR. For post-MDT leprosy patients, SYBRGreen PCR showed the highest sensitivity of 50.0%, highest specificity of 100%, a PPV of 100%, an NPV of 100% and the highest accuracy of 83.72% for MB patients, which were higher than those of STNPCR and simple PCR. STNPCR showed the highest sensitivity of 26.66% and 34.48%, highest specificity of 100% and 100%, a PPV of 100% and 100%, NPV of 72.50% and 60.21%, and highest accuracy of 75.00% and 67.24% for PB and leprosy patients, respectively, higher than those of SYBRGreen PCR and simple PCR. Conclusions: These findings suggest that STNPCR or SYBRGreen PCR (131-bp and 101-bp fragment amplification, respectively) for RLEP using FFPE specimens performs better as a diagnostic test and for monitoring response to MDT than does simple PCR based on 372-bp fragment amplification. Additionally, STNPCR showed increased sensitivity for PB diagnosis using FFPE specimens, which can be transferred remotely or retrieved from previous leprosy patients. [ABSTRACT FROM AUTHOR]

Background: RNA sequencing has been proposed as a means of increasing diagnostic rates in studies of undiagnosed rare inherited disease. Recent studies have reported diagnostic improvements in the range of 7.5–35% by profiling splicing, gene expression quantification and allele specific expression. To-date however, no study has systematically assessed the presence of gene-fusion transcripts in cases of germline disease. Fusion transcripts are routinely identified in cancer studies and are increasingly recognized as having diagnostic, prognostic or therapeutic relevance. Isolated reports exist of fusion transcripts being detected in cases of developmental and neurological phenotypes, and thus, systematic application of fusion detection to germline conditions may further increase diagnostic rates. However, current fusion detection methods are unsuited to the investigation of germline disease due to performance biases arising from their development using tumor, cell-line or in-silico data. Methods: We describe a tailored approach to fusion candidate identification and prioritization in a cohort of 47 undiagnosed, suspected inherited disease patients. We modify an existing fusion transcript detection algorithm by eliminating its cell line-derived filtering steps, and instead, prioritize candidates using a custom workflow that integrates genomic and transcriptomic sequence alignment, biological and technical annotations, customized categorization logic, and phenotypic prioritization. Results: We demonstrate that our approach to fusion transcript identification and prioritization detects genuine fusion events excluded by standard analyses and efficiently removes phenotypically unimportant candidates and false positive events, resulting in a reduced candidate list enriched for events with potential phenotypic relevance. We describe the successful genetic resolution of two previously undiagnosed disease cases through the detection of pathogenic fusion transcripts. Furthermore, we report the experimental validation of five additional cases of fusion transcripts with potential phenotypic relevance. Conclusions: The approach we describe can be implemented to enable the detection of phenotypically relevant fusion transcripts in studies of rare inherited disease. Fusion transcript detection has the potential to increase diagnostic rates in rare inherited disease and should be included in RNA-based analytical pipelines aimed at genetic diagnosis. [ABSTRACT FROM AUTHOR]