Generation of Anterior Segment of the Eye Cells from hiPSCs in Microfluidic Platforms

Ophthalmic diseases affect many people, causing partial or total loss of vision and a reduced quality of life. The anterior segment of the eye accounts for nearly half of all visual impairment that can lead to blindness. Therefore, there is a growing demand for ocular research and regenerative medicine that specifically targets the anterior segment to improve vision quality. This study aims to generate a microfluidic platform for investigating the formation of the anterior segment of the eye derived from human induced pluripotent stem cells (hiPSC) under various spatial-mechanoresponsive conditions. Microfluidic platforms are developed to examine the effects of dynamic conditions on the generation of hiPSCs-derived ocular organoids. The differentiation protocol is validated, and mechanoresponsive genes are identified through transcriptomic analysis. Several culture strategies is implemented for the anterior segment of eye cells in a microfluidic chip. hiPSC-derived cells showed anterior eye cell characteristics in mRNA and protein expression levels under dynamic culture conditions. The expression levels of yes-associated protein and transcriptional coactivator PDZ binding motif (YAP/TAZ) and PIEZO1, varied depending on the differentiation and growth conditions of the cells, as well as the metabolomic profiles under dynamic culture conditions.

Retinoids and EZH2 inhibitors cooperate to orchestrate anti-oncogenic effects on bladder cancer cells

The highly mutated nature of bladder cancers harboring mutations in chromatin regulatory genes opposing Polycomb-mediated repression highlights the importance of targeting EZH2 in bladder cancer. Furthermore, the critical role of the retinoic acid signaling pathway in the development and homeostasis of the urothelium, and the anti-oncogenic effects of retinoids are well established. Therefore, our aim is to simultaneously target EZH2 and retinoic acid signaling in bladder cancer to potentiate the therapeutic response. Here we report that this coordinated targeting strategy stimulates an anti-oncogenic profile, as reflected by inducing a synergistic reduction in cell viability that was associated with increased apoptosis and cell cycle arrest in a cooperative and orchestrated manner. This study characterized anti-oncogenic transcriptional reprogramming centered on the transcriptional regulator CHOP by stimulating the endoplasmic reticulum stress response. We further portrayed a molecular mechanism whereby EZH2 maintains H3K27me3-mediated repression of a subset of genes involved in unfolded protein responses, reflecting the molecular mechanism underlying this co-targeting strategy. These findings highlight the importance of co-targeting the EZH2 and retinoic acid pathway in bladder cancers and encourage the design of novel treatments employing retinoids coupled with EZH2 inhibitors in bladder carcinoma.

Benchmarking the accuracy of structure-based binding affinity predictors on Spike-ACE2 deep mutational interaction set

Since the start of COVID-19 pandemic, a huge effort has been devoted to understanding the Spike (SARS-CoV-2)-ACE2 recognition mechanism. To this end, two deep mutational scanning studies traced the impact of all possible mutations across receptor binding domain (RBD) of Spike and catalytic domain of human ACE2. By concentrating on the interface mutations of these experimental data, we benchmarked six commonly used structure-based binding affinity predictors (FoldX, EvoEF1, MutaBind2, SSIPe, HADDOCK, and UEP). These predictors were selected based on their user-friendliness, accessibility, and speed. As a result of our benchmarking efforts, we observed that none of the methods could generate a meaningful correlation with the experimental binding data. The best correlation is achieved by FoldX (R = -0.51). When we simplified the prediction problem to a binary classification, that is, whether a mutation is enriching or depleting the binding, we showed that the highest accuracy is achieved by FoldX with a 64% success rate. Surprisingly, on this set, simple energetic scoring functions performed significantly better than the ones using extra evolutionary-based terms, as in Mutabind and SSIPe. Furthermore, we demonstrated that recent AI approaches, mmCSM-PPI and TopNetTree, yielded comparable performances to the force field-based techniques. These observations suggest plenty of room to improve the binding affinity predictors in guessing the variant-induced binding profile changes of a host-pathogen system, such as Spike-ACE2. To aid such improvements we provide our benchmarking data at https://github.com/CSB-KaracaLab/RBD-ACE2-MutBench with the option to visualize our mutant models at https://rbd-ace2-mutbench.github.io/.

TRAIL promotes the polarization of human macrophages toward a proinflammatory M1 phenotype and is associated with increased survival in cancer patients with high tumor macrophage content

Background

TNF-related apoptosis-inducing ligand (TRAIL) is a member of the TNF superfamily that can either induce cell death or activate survival pathways after binding to death receptors (DRs) DR4 or DR5. TRAIL is investigated as a therapeutic agent in clinical trials due to its selective toxicity to transformed cells. Macrophages can be polarized into pro-inflammatory/tumor-fighting M1 macrophages or anti-inflammatory/tumor-supportive M2 macrophages and an imbalance between M1 and M2 macrophages can promote diseases. Therefore, identifying modulators that regulate macrophage polarization is important to design effective macrophage-targeted immunotherapies. The impact of TRAIL on macrophage polarization is not known.

Methods

Primary human monocyte-derived macrophages were pre-treated with either TRAIL or with DR4 or DR5-specific ligands and then polarized into M1, M2a, or M2c phenotypes in vitro. The expression of M1 and M2 markers in macrophage subtypes was analyzed by RNA sequencing, qPCR, ELISA, and flow cytometry. Furthermore, the cytotoxicity of the macrophages against U937 AML tumor targets was assessed by flow cytometry. TCGA datasets were also analyzed to correlate TRAIL with M1/M2 markers, and the overall survival of cancer patients.

Results

TRAIL increased the expression of M1 markers at both mRNA and protein levels while decreasing the expression of M2 markers at the mRNA level in human macrophages. TRAIL also shifted M2 macrophages towards an M1 phenotype. Our data showed that both DR4 and DR5 death receptors play a role in macrophage polarization. Furthermore, TRAIL enhanced the cytotoxicity of macrophages against the AML cancer cells in vitro. Finally, TRAIL expression was positively correlated with increased expression of M1 markers in the tumors from ovarian and sarcoma cancer patients and longer overall survival in cases with high, but not low, tumor macrophage content.

Conclusions

TRAIL promotes the polarization of human macrophages toward a proinflammatory M1 phenotype via both DR4 and DR5. Our study defines TRAIL as a new regulator of macrophage polarization and suggests that targeting DRs can enhance the anti-tumorigenic response of macrophages in the tumor microenvironment by increasing M1 polarization.

Shared Biological Pathways and Processes in Patients with Intellectual Disability: A Multicenter Study

BACKGROUND

 Although the underlying genetic causes of intellectual disability (ID) continue to be rapidly identified, the biological pathways and processes that could be targets for a potential molecular therapy are not yet known. This study aimed to identify ID-related shared pathways and processes utilizing enrichment analyses.

METHOD

 In this multicenter study, causative genes of patients with ID were used as input for Disease Ontology (DO), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes enrichment analysis.

RESULTS

 Genetic test results of 720 patients from 27 centers were obtained. Patients with chromosomal deletion/duplication, non-ID genes, novel genes, and results with changes in more than one gene were excluded. A total of 558 patients with 341 different causative genes were included in the study. Pathway-based enrichment analysis of the ID-related genes via ClusterProfiler revealed 18 shared pathways, with lysine degradation and nicotine addiction being the most common. The most common of the 25 overrepresented DO terms was ID. The most frequently overrepresented GO biological process, cellular component, and molecular function terms were regulation of membrane potential, ion channel complex, and voltage-gated ion channel activity/voltage-gated channel activity, respectively.

CONCLUSION

 Lysine degradation, nicotine addiction, and thyroid hormone signaling pathways are well-suited to be research areas for the discovery of new targeted therapies in ID patients.

Differential Occupancy and Regulatory Interactions of KDM6A in Bladder Cell Lines

Epigenetic deregulation is a critical theme which needs further investigation in bladder cancer research. One of the most highly mutated genes in bladder cancer is KDM6A, which functions as an H3K27 demethylase and is one of the MLL3/4 complexes. To decipher the role of KDM6A in normal versus tumor settings, we identified the genomic landscape of KDM6A in normal, immortalized, and cancerous bladder cells. Our results showed differential KDM6A occupancy in the genes involved in cell differentiation, chromatin organization, and Notch signaling depending on the cell type and the mutation status of KDM6A. Transcription factor motif analysis revealed HES1 to be enriched at KDM6A peaks identified in the T24 bladder cancer cell line; moreover, it has a truncating mutation in KDM6A and lacks a demethylase domain. Our co-immunoprecipitation experiments revealed TLE co-repressors and HES1 as potential truncated and wild-type KDM6A interactors. With the aid of structural modeling, we explored how truncated KDM6A could interact with TLE and HES1, as well as RUNX and HHEX transcription factors. These structures provide a solid means of studying the functions of KDM6A independently of its demethylase activity. Collectively, our work provides important contributions to the understanding of KDM6A malfunction in bladder cancer.

FLI1 and FRA1 transcription factors drive the transcriptional regulatory networks characterizing muscle invasive bladder cancer

Bladder cancer is mostly present in the form of urothelium carcinoma, causing over 150,000 deaths each year. Its histopathological classification as muscle invasive (MIBC) and non-muscle invasive (NMIBC) is the most prominent aspect, affecting the prognosis and progression of this disease. In this study, we defined the active regulatory landscape of MIBC and NMIBC cell lines using H3K27ac ChIP-seq and used an integrative approach to combine our findings with existing data. Our analysis revealed FRA1 and FLI1 as two critical transcription factors differentially regulating MIBC regulatory landscape. We show that FRA1 and FLI1 regulate the genes involved in epithelial cell migration and cell junction organization. Knock-down of FRA1 and FLI1 in MIBC revealed the downregulation of several EMT-related genes such as MAP4K4 and FLOT1. Further, ChIP-SICAP performed for FRA1 and FLI1 enabled us to infer chromatin binding partners of these transcription factors and link this information with their target genes. Finally, we show that knock-down of FRA1 and FLI1 result in significant reduction of invasion capacity of MIBC cells towards muscle microenvironment using IC-CHIP assays. Our results collectively highlight the role of these transcription factors in selection and design of targeted options for treatment of MIBC.

An in silico approach to the identification of diagnostic and prognostic markers in low-grade gliomas

Low-grade gliomas (LGG) are central nervous system Grade I tumors, and as they progress they are becoming one of the deadliest brain tumors. There is still great need for timely and accurate diagnosis and prognosis of LGG. Herein, we aimed to identify diagnostic and prognostic biomarkers associated with LGG, by employing diverse computational approaches. For this purpose, differential gene expression analysis on high-throughput transcriptomics data of LGG versus corresponding healthy brain tissue, derived from TCGA and GTEx, respectively, was performed. Weighted gene co-expression network analysis of the detected differentially expressed genes was carried out in order to identify modules of co-expressed genes significantly correlated with LGG clinical traits. The genes comprising these modules were further used to construct gene co-expression and protein-protein interaction networks. Based on the network analyses, we derived a consensus of eighteen hub genes, namely, CD74, CD86, CDC25A, CYBB, HLA-DMA, ITGB2, KIF11, KIFC1, LAPTM5, LMNB1, MKI67, NCKAP1L, NUSAP1, SLC7A7, TBXAS1, TOP2A, TYROBP, and WDFY4. All detected hub genes were up-regulated in LGG, and were also associated with unfavorable prognosis in LGG patients. The findings of this study could be applicable in the clinical setting for diagnosing and monitoring LGG.

"In the light of evolution:" keratins as exceptional tumor biomarkers

Keratins (KRTs) are the intermediate filament-forming proteins of epithelial cells, classified, according to their physicochemical properties, into "soft" and "hard" keratins. They have a key role in several aspects of cancer pathophysiology, including cancer cell invasion and metastasis, and several members of the KRT family serve as diagnostic or prognostic markers. The human genome contains both, functional KRT genes and non-functional KRT pseudogenes, arranged in two uninterrupted clusters on chromosomes 12 and 17. This characteristic renders KRTs ideal for evolutionary studies. Herein, comprehensive phylogenetic analyses of KRT homologous proteins in the genomes of major taxonomic divisions were performed, so as to fill a gap in knowledge regarding the functional implications of keratins in cancer biology among tumor-bearing species. The differential expression profiles of KRTs in diverse types of cancers were investigated by analyzing high-throughput data, as well. Several KRT genes, including the phylogenetically conserved ones, were found to be deregulated across several cancer types and to participate in a common protein-protein interaction network. This indicates that, at least in cancer-bearing species, these genes might have been under similar evolutionary pressure, perhaps to support the same important function(s). In addition, semantic relations between KRTs and cancer were detected through extensive text mining. Therefore, by applying an integrative in silico pipeline, the evolutionary history of KRTs was reconstructed in the context of cancer, and the potential of using non-mammalian species as model organisms in functional studies on human cancer-associated KRT genes was uncovered.

PlanTEnrichment: A How-to Guide on Rapid Identification of Transposable Elements Associated with Regions of Interest in Select Plant Genomes

Transposable elements (TEs) are repeat elements that can relocate or create novel copies of themselves in the genome and contribute to genomic complexity and expansion, via events such as chromosome recombination or regulation of gene expression. However, given the large number of such repeats across the genome, identifying repeats of interest can be a challenge in even well-annotated genomes, especially in more complex, TE-rich plant genomes. Here, we describe a protocol for PlanTEnrichment, a database we created comprising information on 11 plant genomes to analyze stress-associated TEs using publicly available data. By selecting a genome and providing a list of genes or genomic regions whose TE associations the user wants to identify, the user can rapidly obtain TE subfamilies found near the provided regions, as well as their superfamily and class, and the enrichment values of the repeats. The results also provide the locations of individual repeat instances found, alongside the input regions or genes they are associated with, and a bar graph of the top ten most significant repeat subfamilies identified. PlanTEnrichment is freely available at http://tools.ibg.deu.edu.tr/plantenrichment/ and can be used by researchers with rudimentary or no proficiency in computational analysis of TE elements, allowing for expedience in the identification of TEs of interest and helping further our understanding of the potential contributions of TEs in plant genomes.

A Counterintuitive Neutrophil-Mediated Pattern in COVID-19 Patients Revealed through Transcriptomics Analysis

The COVID-19 pandemic has persisted for almost three years. However, the mechanisms linked to the SARS-CoV-2 effect on tissues and disease severity have not been fully elucidated. Since the onset of the pandemic, a plethora of high-throughput data related to the host transcriptional response to SARS-CoV-2 infections has been generated. To this end, the aim of this study was to assess the effect of SARS-CoV-2 infections on circulating and organ tissue immune responses. We profited from the publicly accessible gene expression data of the blood and soft tissues by employing an integrated computational methodology, including bioinformatics, machine learning, and natural language processing in the relevant transcriptomics data. COVID-19 pathophysiology and severity have mainly been associated with macrophage-elicited responses and a characteristic "cytokine storm". Our counterintuitive findings suggested that the COVID-19 pathogenesis could also be mediated through neutrophil abundance and an exacerbated suppression of the immune system, leading eventually to uncontrolled viral dissemination and host cytotoxicity. The findings of this study elucidated new physiological functions of neutrophils, as well as tentative pathways to be explored in asymptomatic-, ethnicity- and locality-, or staging-associated studies.

Identification of neoplasm-specific signatures of miRNA interactions by employing a systems biology approach

MicroRNAs represent major regulatory components of the disease epigenome and they constitute powerful biomarkers for the accurate diagnosis and prognosis of various diseases, including cancers. The advent of high-throughput technologies facilitated the generation of a vast amount of miRNA-cancer association data. Computational approaches have been utilized widely to effectively analyze and interpret these data towards the identification of miRNA signatures for diverse types of cancers. Herein, a novel computational workflow was applied to discover core sets of miRNA interactions for the major groups of neoplastic diseases by employing network-based methods. To this end, miRNA-cancer association data from four comprehensive publicly available resources were utilized for constructing miRNA-centered networks for each major group of neoplasms. The corresponding miRNA-miRNA interactions were inferred based on shared functionally related target genes. The topological attributes of the generated networks were investigated in order to detect clusters of highly interconnected miRNAs that form core modules in each network. Those modules that exhibited the highest degree of mutual exclusivity were selected from each graph. In this way, neoplasm-specific miRNA modules were identified that could represent potential signatures for the corresponding diseases.

Repeat expression is linked to patient survival and exhibits single nucleotide variation in pancreatic cancer revealing LTR70:r.879A>G

Despite an overwhelming number of cancer literature reporting the links between patient survival and the expression levels of genes or mutations/single nucleotide variations (SNVs) on them, there is only limited information on repeat elements, which make at least half the human genome. Here, we analysed RNA-seq data obtained from primary pancreatic cancer tissues of 51 patients and revealed that two transposons, HERVI-int and X6A_LINE, showed an upregulation trend in the patients who lived shorter, along with 56 other potential repeats which were linked to survival. We also detected expressed single nucleotide variations (SNVs) on repeats, among which LTR70:r.879A>G stands out with the effect of its presence on this particular repeat's expression levels and a significant link to overall patient survival. Interestingly, the expression of LTR70:r.879A>G correlated with different cancer genes in comparison to its reference version highlighting the involvement of BRAF and Fumerate Hydratase with this expressed SNV. This is one of the first studies revealing possible links between repeat expression and survival in cancer and it warrants further research in this avenue.

Alteration of miRNAs in Small Neuron-Derived Extracellular Vesicles of Alzheimer's Disease Patients and the Effect of Extracellular Vesicles on Microglial Immune Responses

Alzheimer's disease (AD) is one of the most severe neurodegenerative diseases observed in the elderly population. Although the hallmarks of AD have been identified, the methods for its definitive diagnosis and treatment are still lacking. Extracellular vesicles (EVs) have become a promising source for biomarkers since the identification of their content. EVs are released from multiple cell types and, when released from neurons, they pass from the brain to the blood with their cargo molecules. Hence, neuron-specific EV-resident microRNAs (miRNAs) are promising biomarkers for diagnosis of AD. This study aimed to identify altered miRNA content in small neuron-derived extracellular vesicles (sNDEVs) isolated from AD patients and healthy individuals. Furthermore, we examined the role of sNDEV-resident miRNAs in neuron-glia cellular interaction to understand their role in AD propagation. We identified 10 differentially expressed miRNAs in the sNDEVs of patients via next-generation sequencing and validated the most dysregulated miRNA, let-7e, with qRT-PCR. Let-7e was significantly increased in the sNDEVs of AD patients compared with those of healthy controls in a larger cohort. First, we evaluated the diagnostic utility of let-7e via ROC curve analysis, which revealed an AUC value of 0.9214. We found that IL-6 gene expression was increased in human microglia after treatment with sNDEVs of AD patients with a high amount of let-7e. Our study suggests that sNDEV-resident let-7e is a potential biomarker for AD diagnosis, and that AD patient-derived sNDEVs induce a neuroinflammatory response in microglia.

[TAp73α is Upregulated in the Most Common Human Cancers]

The transcription factor p73 is a member of the p53 tumor suppressor gene family and one of the key regulators of apoptosis. TP73 gene encodes two protein isoforms classes with diverse functions, TAp73 and DNp73, and TAp73 expression in tumor tissues is altered. Unlike the TP53 gene, TP73 is not mutated in cancers. Here, we sought to explore the expression of p73 isoforms across eight major cancer types using the publicly available data deposited at the GDC data portal and the TSVdb database. Our results showed that TAp73α is overexpressed in breast invasive carcinoma, stomach adenocarcinoma, lung squamous cell carcinoma, colon adenocarcinoma, and esophageal carcinoma tumors, whereas the expression of DNp73 isoforms is downregulated in breast invasive carcinoma (DNp73α,β,γ), Prostate Adenocarcinoma (DNp73β), Lung Adenocarcinoma (DNp73α), Lung Squamous Cell Carcinoma (DNp73α) tumors. In summary, this study revealed that TAp73α has higher expression than the DNp73 isoforms in several cancer types.

Transcriptome Dynamics of Human Neuronal Differentiation From iPSC

The generation and use of induced pluripotent stem cells (iPSCs) in order to obtain all differentiated adult cell morphologies without requiring embryonic stem cells is one of the most important discoveries in molecular biology. Among the uses of iPSCs is the generation of neuron cells and organoids to study the biological cues underlying neuronal and brain development, in addition to neurological diseases. These iPSC-derived neuronal differentiation models allow us to examine the gene regulatory factors involved in such processes. Among these regulatory factors are long non-coding RNAs (lncRNAs), genes that are transcribed from the genome and have key biological functions in establishing phenotypes, but are frequently not included in studies focusing on protein coding genes. Here, we provide a comprehensive analysis and overview of the coding and non-coding transcriptome during multiple stages of the iPSC-derived neuronal differentiation process using RNA-seq. We identify previously unannotated lncRNAs via genome-guided de novo transcriptome assembly, and the distinct characteristics of the transcriptome during each stage, including differentially expressed and stage specific genes. We further identify key genes of the human neuronal differentiation network, representing novel candidates likely to have critical roles in neurogenesis using coexpression network analysis. Our findings provide a valuable resource for future studies on neuronal differentiation.

Identification of differentially expressed genomic repeats in primary hepatocellular carcinoma and their potential links to biological processes and survival

Hepatocellular carcinoma (HCC) is one of the deadliest cancers. Research on HCC so far primarily focused on genes and provided limited information on genomic repeats, which constitute more than half of the human genome and contribute to genomic stability. In line with this, repeat dysregulation was significantly shown to be pathological in various cancers and other diseases. In this study, we aimed to determine the full repeat expression profile of HCC for the first time. We utilised two independent RNA-seq datasets obtained from primary HCC tumours with matched normal tissues of 20 and 17 HCC patients, respectively. We quantified repeat expressions and analysed their differential expression. We also identified repeats that are cooperatively expressed with genes by constructing a gene coexpression network. Our results indicated that HCC tumours in both datasets harbour 24 differentially expressed repeats and even more elements were coexpressed with genes involved in various metabolic pathways. We discovered that two L1 elements (L1M3b, L1M3de) were downregulated and a handful of HERV subfamily repeats (HERV-Fc1-int, HERV3-int, HERVE_a-int, HERVK11D-int, HERVK14C-int, HERVL18-int) were upregulated with the exception of HERV1_LTRc, which was downregulated. Various LTR elements (LTR32, LTR9, LTR4, LTR52-int, LTR70) and MER elements (MER11C, MER11D, MER57C1, MER9a1, MER74C) were implicated along with few other subtypes including Charlie12, MLT2A2, Tigger15a, Tigger 17b. The only satellite repeat differentially expressed in both datasets was GSATII, whose expression was upregulated in 33 (>90%) out of 37 patients. Notably, GSATII expression correlated with HCC survival genes. Elements discovered here promise future studies to be considered for biomarker and HCC therapy research. The coexpression pattern of the GSATII satellite with HCC survival genes and the fact that it has been upregulated in the vast majority of patients make this repeat particularly stand out for HCC.

Evaluation of ATAD2 as a Potential Target in Hepatocellular Carcinoma

PURPOSE

Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related death worldwide with lack of effective systemic chemotherapy. In this study, we aimed to evaluate the value of ATPase family AAA domain-containing protein 2 (ATAD2) as a biomarker and potential therapeutic target for HCC.

METHODS

The expression of ATAD2 was tested in different HCC patient cohorts by immunohistochemistry and comparative transcriptional analysis. The co-expression of ATAD2 and proliferation markers was compared during liver regeneration and malignancy with different bioinformatics tools. The cellular effects of ATAD2 inactivation in liver malignancy was tested on cell cycle, apoptosis, and colony formation ability as well as tumor formation using RNA interference. The genes affected by ATAD2 inactivation in three different HCC cell lines were identified by global gene expression profiling and bioinformatics tools.

RESULTS

ATAD2 overexpression is closely correlated with HCC tumor stage. There was gradual increase from dysplasia, well-differentiated and poorly-differentiated HCC, respectively. We also observed transient upregulation of ATAD2 expression during rat liver regeneration in parallel to changes in Ki-67 expression. ATAD2 knockdown resulted in apoptosis and decreased cell survival in vitro and decreased tumor formation in some HCC cell lines. However, three other HCC cell lines tested were not affected. Similarly, gene expression response to ATAD2 inactivation in different HCC cell lines was highly heterogeneous.

CONCLUSIONS

ATAD2 is a potential proliferation marker for liver regeneration and HCC. It may also serve as a therapeutic target despite heterogeneous response of malignant cells.

In Silico Methods for the Identification of Diagnostic and Favorable Prognostic Markers in Acute Myeloid Leukemia

Acute myeloid leukemia (AML), the most common type of acute leukemia in adults, is mainly asymptomatic at early stages and progresses/recurs rapidly and frequently. These attributes necessitate the identification of biomarkers for timely diagnosis and accurate prognosis. In this study, differential gene expression analysis was performed on large-scale transcriptomics data of AML patients versus corresponding normal tissue. Weighted gene co-expression network analysis was conducted to construct networks of co-expressed genes, and detect gene modules. Finally, hub genes were identified from selected modules by applying network-based methods. This robust and integrative bioinformatics approach revealed a set of twenty-four genes, mainly related to cell cycle and immune response, the diagnostic significance of which was subsequently compared against two independent gene expression datasets. Furthermore, based on a recent notion suggesting that molecular characteristics of a few, unusual patients with exceptionally favorable survival can provide insights for improving the outcome of individuals with more typical disease trajectories, we defined groups of long-term survivors in AML patient cohorts and compared their transcriptomes versus the general population to infer favorable prognostic signatures. These findings could have potential applications in the clinical setting, in particular, in diagnosis and prognosis of AML.

Mining Natural Products with Anticancer Biological Activity through a Systems Biology Approach

Natural products, like turmeric, are considered powerful antioxidants which exhibit tumor-inhibiting activity and chemoradioprotective properties. Nowadays, there is a great demand for developing novel, affordable, efficacious, and effective anticancer drugs from natural resources. In the present study, we have employed a stringent in silico methodology to mine and finally propose a number of natural products, retrieved from the biomedical literature. Our main target was the systematic search of anticancer products as anticancer agents compatible to the human organism for future use. In this case and due to the great plethora of such products, we have followed stringent bioinformatics methodologies. Our results taken together suggest that natural products of a great diverse may exert cytotoxic effects in a maximum of the studied cancer cell lines. These natural compounds and active ingredients could possibly be combined to exert potential chemopreventive effects. Furthermore, in order to substantiate our findings and their application potency at a systems biology level, we have developed a representative, user-friendly, publicly accessible biodatabase, NaturaProDB, containing the retrieved natural resources, their active ingredients/fractional mixtures, the types of cancers that they affect, and the corresponding experimentally verified target genes.

Alternative splicing and gene co-expression network-based analysis of dizygotic twins with autism-spectrum disorder and their parents

Autism spectrum disorder (ASD) is a neurodevelopmental disorder with high heritability, however, understanding the complexity of the underlying genetic basis has proven to be a challenging task. We hypothesized that dissecting the aberrations in alternative splicing (AS) and their effects on expression networks might provide insight. Therefore, we performed AS and co-expression analyses of total RNA isolated from Peripheral Blood Mononuclear Cells (PBMCs) of two pairs of dizygotic (DZ) twins with non-syndromic autism and their parents. We identified 183 differential AS events in 146 genes, seven of them being Simons Foundation Autism Research Initiative (SFARI) Category 1-3 genes, three of which had previously been reported to be alternatively spliced in ASD post-mortem brains. Gene co-expression analysis identified 7 modules with 513 genes, 5 of which were SFARI Category 1 or Category 2 genes. Among differentially AS genes within the modules, ZNF322 and NR4A1 could be potentially interesting targets for further investigations.

Investigating Molecular Determinants of Cancer Cell Resistance to Ionizing Radiation Through an Integrative Bioinformatics Approach

Eradication of cancer cells through exposure to high doses of ionizing radiation (IR) is a widely used therapeutic strategy in the clinical setting. However, in many cases, cancer cells can develop remarkable resistance to radiation. Radioresistance represents a prominent obstacle in the effective treatment of cancer. Therefore, elucidation of the molecular mechanisms and pathways related to radioresistance in cancer cells is of paramount importance. In the present study, an integrative bioinformatics approach was applied to three publicly available RNA sequencing and microarray transcriptome datasets of human cancer cells of different tissue origins treated with ionizing radiation. These data were investigated in order to identify genes with a significantly altered expression between radioresistant and corresponding radiosensitive cancer cells. Through rigorous statistical and biological analyses, 36 genes were identified as potential biomarkers of radioresistance. These genes, which are primarily implicated in DNA damage repair, oxidative stress, cell pro-survival, and apoptotic pathways, could serve as potential diagnostic/prognostic markers cancer cell resistance to radiation treatment, as well as for therapy outcome and cancer patient survival. In addition, our findings could be potentially utilized in the laboratory and clinical setting for enhancing cancer cell susceptibility to radiation therapy protocols.

TAp73β Can Promote Hepatocellular Carcinoma Dedifferentiation

Simple Summary

Hepatocellular carcinoma (HCC) is a highly complex and heterogeneous type of cancer. Hepatocyte dedifferentiation is one of the important steps in the development of HCC. However, its molecular mechanisms are not well known. In this study, we report that transcriptionally active TAp73 isoforms are overexpressed in HCC. We also show that TAp73β suppresses the expression of the hepatocyte markers including CYP3A4, AFP, ALB, HNF4α, while increasing the expression of several cholangiocyte markers in HCC cell lines. In conclusion, this report reveals a pro-oncogenic role for TAp73β in liver cancer.

Abstract

Hepatocyte dedifferentiation is a major source of hepatocellular carcinoma (HCC), but its mechanisms are unknown. We explored the p73 expression in HCC tumors and studied the effects of transcriptionally active p73β (TAp73β) in HCC cells. Expression profiles of p73 and patient clinical data were collected from the Genomic Data Commons (GDC) data portal and the TSVdb database, respectively. Global gene expression profiles were determined by pan-genomic 54K microarrays. The Gene Set Enrichment Analysis method was used to identify TAp73β-regulated gene sets. The effects of TAp73 isoforms were analyzed in monolayer cell culture, 3D-cell culture and xenograft models in zebrafish using western blot, flow cytometry, fluorescence imaging, real-time polymerase chain reaction (RT-PCR), immunohistochemistry and morphological examination. TAp73 isoforms were significantly upregulated in HCC, and high p73 expression correlated with poor patient survival. The induced expression of TAp73β caused landscape expression changes in genes involved in growth signaling, cell cycle, stress response, immunity, metabolism and development. Hep3B cells overexpressing TAp73β had lost hepatocyte lineage biomarkers including ALB, CYP3A4, AFP, HNF4α. In contrast, TAp73β upregulated genes promoting cholangiocyte lineage such as YAP, JAG1 and ZO-1, accompanied with an increase in metastatic ability. Our findings suggest that TAp73β may promote malignant dedifferentiation of HCC cells.

In Silico Identification of Stress-Associated Transposable Elements in Arabidopsis thaliana Using Public Transcriptome Data

Transposable elements (TEs) have been associated with stress response in many plants, making them a key target of study. However, the high variability, genomic repeat-heavy nature, and widely noncoding character of TEs have made them difficult to study using non-specialized methods, whether experimental or computational. In this chapter, we introduce two computational workflows to analyze transposable elements using publicly available transcriptome data. In the first of these methods, we identify TEs, which show differential expression under salt stress using sample transcriptome libraries that includes noncoding transcripts. In the second, we identify protein-coding genes with differential expression under the same conditions, and determine which TEs are enriched in the promoter regions of these stress-related genes.

Differential expression of full-length and NH2 terminally truncated FAM134B isoforms in normal physiology and cancer

Selective autophagy of the endoplasmic reticulum (ER), namely ER-phagy, is mediated by ER-localized receptors, which are recognized and sequestered by GABARAP/LC3B-decorated phagophores and transferred to lysosomes for degradation. Being one such receptor, FAM134B plays critical roles in cellular processes such as protein quality control and neuronal survival. FAM134B has also been associated with different cancers, although its exact role remains elusive. We report here that the FAM134B gene encodes not one but at least two different protein isoforms: the full-length and the NH₂ terminally truncated forms. Their relative expression shows extreme variation, both within normal tissues and among cancer types. Expression of full-length FAM134B is restricted to the brain, testis, spleen, and prostate. In contrast, NH₂ terminally truncated FAM134B is dominant in the heart, skeletal muscle, kidney, pancreas, and liver. We compared wild-type and knockout mice to study the role of the Fam134b gene in starvation. NH₂ terminally truncated FAM134B-2 was induced in the liver, skeletal muscle, and heart but not in the pancreas and stomach following starvation. Upon starvation, Fam134b^-/- mice differed from wild-type mice by less weight loss and less hyperaminoacidemic and hypocalcemic response but increased levels of serum albumin, total serum proteins, and α-amylase. Interestingly, either NH₂ terminally truncated FAM134B or both isoforms were downregulated in liver, lung, and colon cancers. In contrast, upregulation was observed in stomach and chromophobe kidney cancers.NEW & NOTEWORTHY We reported tissues expressing FAM134B-2 such as the kidney, muscle, heart, and pancreas, some of which exhibit stimulated expression upon nutrient starvation. We also demonstrated the effect of Fam134b deletion during ad libitum and starvation conditions. Resistance to weight loss and hypocalcemia, accompanied by an increase in serum albumin and α-amylase levels, indicate critical roles of Fam134b in physiology. Furthermore, the differential expression of FAM134B isoforms was shown to be significantly dysregulated in human cancers.

Mutations of SARS-CoV-2 nsp14 exhibit strong association with increased genome-wide mutation load

SARS-CoV-2 is a betacoronavirus responsible for COVID-19, a pandemic with global impact that first emerged in late 2019. Since then, the viral genome has shown considerable variance as the disease spread across the world, in part due to the zoonotic origins of the virus and the human host adaptation process. As a virus with an RNA genome that codes for its own genomic replication proteins, mutations in these proteins can significantly impact the variance rate of the genome, affecting both the survival and infection rate of the virus, and attempts at combating the disease. In this study, we analyzed the mutation densities of viral isolates carrying frequently observed mutations for four proteins in the RNA synthesis complex over time in comparison to wildtype isolates. Our observations suggest mutations in nsp14, an error-correcting exonuclease protein, have the strongest association with increased mutation load without selective pressure and across the genome, compared to nsp7, nsp8 and nsp12, which form the core polymerase complex. We propose nsp14 as a priority research target for understanding genomic variance rate in SARS-CoV-2 isolates and nsp14 mutations as potential predictors for high mutability strains.

Mutation density changes in SARS-CoV-2 are related to the pandemic stage but to a lesser extent in the dominant strain with mutations in spike and RdRp

Since its emergence in Wuhan, China in late 2019, the origin and evolution of SARS-CoV-2 have been among the most debated issues related to COVID-19. Throughout its spread around the world, the viral genome continued acquiring new mutations and some of them became widespread. Among them, 14408 C>T and 23403 A>G mutations in RdRp and S, respectively, became dominant in Europe and the US, which led to debates regarding their effects on the mutability and transmissibility of the virus. In this study, we aimed to investigate possible differences between time-dependent variation of mutation densities (MDe) of viral strains that carry these two mutations and those that do not. Our analyses at the genome and gene level led to two important findings: First, time-dependent changes in the average MDe of circulating SARS-CoV-2 genomes showed different characteristics before and after the beginning of April, when daily new case numbers started levelling off. Second, this pattern was much delayed or even non-existent for the "mutant" (MT) strain that harbored both 14408 C>T and 23403 A>G mutations. Although these differences were not limited to a few hotspots, it is intriguing that the MDe increase is most evident in two critical genes, S and Orf1ab, which are also the genes that harbor the defining mutations of the MT genotype. The nature of these unexpected relationships warrants further research.

RdRp mutations are associated with SARS-CoV-2 genome evolution

COVID-19, caused by the novel SARS-CoV-2 virus, started in China in late 2019, and soon became a global pandemic. With the help of thousands of viral genome sequences that have been accumulating, it has become possible to track the evolution of the viral genome over time as it spread across the world. An important question that still needs to be answered is whether any of the common mutations affect the viral properties, and therefore the disease characteristics. Therefore, we sought to understand the effects of mutations in RNA-dependent RNA polymerase (RdRp), particularly the common 14408C>T mutation, on mutation rate and viral spread. By focusing on mutations in the slowly evolving M or E genes, we aimed to minimize the effects of selective pressure. Our results indicate that 14408C>T mutation increases the mutation rate, while the third-most common RdRp mutation, 15324C>T, has the opposite effect. It is possible that 14408C>T mutation may have contributed to the dominance of its co-mutations in Europe and elsewhere.

TEffectR: an R package for studying the potential effects of transposable elements on gene expression with linear regression model

Introduction

Recent studies highlight the crucial regulatory roles of transposable elements (TEs) on proximal gene expression in distinct biological contexts such as disease and development. However, computational tools extracting potential TE -proximal gene expression associations from RNA-sequencing data are still missing.

Implementation

Herein, we developed a novel R package, using a linear regression model, for studying the potential influence of TE species on proximal gene expression from a given RNA-sequencing data set. Our R package, namely TEffectR, makes use of publicly available RepeatMasker TE and Ensembl gene annotations as well as several functions of other R-packages. It calculates total read counts of TEs from sorted and indexed genome aligned BAM files provided by the user, and determines statistically significant relations between TE expression and the transcription of nearby genes under diverse biological conditions.

Availability

TEffectR is freely available at https://github.com/karakulahg/TEffectR along with a handy tutorial as exemplified by the analysis of RNA-sequencing data including normal and tumour tissue specimens obtained from breast cancer patients.

Dysregulated expression of repetitive DNA in ER+/HER2- breast cancer

Limited studies on breast cancer indicated pathogenic changes in the expressions of some repeat elements. A global analysis was much needed within this context to distinguish the most significant repeats from more than a thousand repeat motifs. Utilising a previously presented RNA-seq dataset, we studied expression changes of all repeats in ER+/HER2- human breast tumour samples obtained from 22 patients in comparison to matched normal tissues. Fifty six (56) repeat subtypes including satellites and transposons were found to be differentially expressed and most of them were novel for breast cancer. HERVKC4-int and HERV1_LTRc, whose expressions correlated well with that of the estrogen receptor gene ESR1, were upregulated at the highest level. REP522 and D20S16 satellites were also significantly upregulated along with insignificant increases in the expressions of other satellites including HSATI and BSR/beta. Interestingly, expressions of REP522 and D20S16 correlated with many key breast cancer pathway (e.g. BRCA1, BRCA2, AKT1, MTOR, KRAS) and survival genes; possibly highlighting their importance in the carcinogenesis of breast. Additional differentially expressed elements such as L1P and various MER transposons also exhibited a similar pattern. Finally, our repeat enrichment analysis on the promoters of differentially expressed genes revealed further links between additional repeats and nearby genes.

Expression dynamics of repetitive DNA in early human embryonic development

BACKGROUND

The last decade witnessed a number of genome-wide studies on human pre-implantation, which mostly focused on genes and provided only limited information on repeats, excluding the satellites. Considering the fact that repeats constitute a large portion of our genome with reported links to human physiology and disease, a thorough understanding of their spatiotemporal regulation during human embryogenesis will give invaluable clues on chromatin dynamics across time and space. Therefore, we performed a detailed expression analysis of all repetitive DNA elements including the satellites across stages of human pre-implantation and embryonic stem cells.

RESULTS

We uncovered stage-specific expressions of more than a thousand repeat elements whose expressions fluctuated with a mild global decrease at the blastocyst stage. Most satellites were highly expressed at the 4-cell level and expressions of ACRO1 and D20S16 specifically peaked at this point. Whereas all members of the SVA elements were highly upregulated at 8-cell and morula stages, other transposons and small RNA repeats exhibited a high level of variation among their specific subtypes. Our repeat enrichment analysis in gene promoters coupled with expression correlations highlighted potential links between repeat expressions and nearby genes, emphasising mostly 8-cell and morula specific genes together with SVA_D, LTR5_Hs and LTR70 transposons. The DNA methylation analysis further complemented the understanding on the mechanistic aspects of the repeatome's regulation per se and revealed critical stages where DNA methylation levels are negatively correlating with repeat expression.

CONCLUSIONS

Taken together, our study shows that specific expression patterns are not exclusive to genes and long non-coding RNAs but the repeatome also exhibits an intriguingly dynamic pattern at the global scale. Repeats identified in this study; particularly satellites, which were historically associated with heterochromatin, and those with potential links to nearby gene expression provide valuable insights into the understanding of key events in genomic regulation and warrant further research in epigenetics, genomics and developmental biology.

C-Reactive Protein and Platelet-Lymphocyte Ratio as Potential Tumor Markers in Low-Alpha-Fetoprotein Hepatocellular Carcinoma

The hepatocellular carcinoma (HCC) tumor marker alpha-fetoprotein (AFP) is only elevated in about half of the HCC patients, limiting its usefulness in following the effects of therapy or screening. New markers are needed. It has been previously noted that the inflammation markers C-reactive protein (CRP) and platelet-lymphocyte ratio (PLR) are prognostically important and may reflect HCC aggressiveness. We therefore examined these 2 markers in a low-AFP HCC cohort and found that for HCCs > 2 cm, both markers significantly rise with an increasing maximum tumor diameter (MTD). We calculated the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and Youden index value for each marker, and their area-under-the-curve values for each MTD group. Patients were dichotomized into 2 groups based on the CRP and PLR from the receiver-operating characteristic curve analysis. In the logistic regression models of the 4 different MTD patient groups, CRP and PLR levels were statistically significant to estimate MTD in univariate logistic regression models of MTD groups > 2 cm. CRP and PLR were then combined, and the combination was statistically significant to estimate MTD groups of 3-, 4-, and 5-cm cutoffs. CRP and PLR thus have potential as tumor markers for low-AFP HCC patients, and possibly for screening.

NRL-Regulated Transcriptome Dynamics of Developing Rod Photoreceptors

Gene regulatory networks (GRNs) guiding differentiation of cell types and cell assemblies in the nervous system are poorly understood because of inherent complexities and interdependence of signaling pathways. Here, we report transcriptome dynamics of differentiating rod photoreceptors in the mammalian retina. Given that the transcription factor NRL determines rod cell fate, we performed expression profiling of developing NRL-positive (rods) and NRL-negative (S-cone-like) mouse photoreceptors. We identified a large-scale, sharp transition in the transcriptome landscape between postnatal days 6 and 10 concordant with rod morphogenesis. Rod-specific temporal DNA methylation corroborated gene expression patterns. De novo assembly and alternative splicing analyses revealed previously unannotated rod-enriched transcripts and the role of NRL in transcript maturation. Furthermore, we defined the relationship of NRL with other transcriptional regulators and downstream cognate effectors. Our studies provide the framework for comprehensive system-level analysis of the GRN underlying the development of a single sensory neuron, the rod photoreceptor.

RTFAdb: A database of computationally predicted associations between retrotransposons and transcription factors in the human and mouse genomes

In recent years, retrotransposons have gained increasing attention as a source of binding motifs for transcription factors (TFs). Despite the substantial roles of these mobile genetic elements in the regulation of gene expression, a comprehensive resource enabling the investigation of retrotransposon species that are bound by TFs is still lacking. Herein, I introduce for the first time a novel database called RTFAdb, which allows exploring computationally predicted associations between retrotransposons and TFs in diverse cell lines and tissues of human and mouse. My database, using over 3.000 TF ChIP-seq binding profiles collected from human and mouse samples, makes possible searching more than 1.500 retrotransposon species in the binding sites of a total of 596 TFs. RTFAdb is freely available at http://tools.ibg.deu.edu.tr/rtfa/ and has the potential to offer novel insights into mammalian transcriptional networks by providing an additional layer of information regarding the regulatory roles of retrotransposons.

Regulation of Noncoding Transcriptome in Developing Photoreceptors by Rod Differentiation Factor NRL

Purpose

Transcriptome analysis by next generation sequencing allows qualitative and quantitative profiling of expression patterns associated with development and disease. However, most transcribed sequences do not encode proteins, and little is known about the functional relevance of noncoding (nc) transcriptome in neuronal subtypes. The goal of this study was to perform a comprehensive analysis of long noncoding (lncRNAs) and antisense (asRNAs) RNAs expressed in mouse retinal photoreceptors.

Methods

Transcriptomic profiles were generated at six developmental time points from flow-sorted Nrlp-GFP (rods) and Nrlp-GFP;Nrl^−/− (S-cone like) mouse photoreceptors. Bioinformatic analysis was performed to identify novel noncoding transcripts and assess their regulation by rod differentiation factor neural retina leucine zipper (NRL). In situ hybridization (ISH) was used for validation and cellular localization.

Results

NcRNA profiles demonstrated dynamic yet specific expression signature and coexpression clusters during rod development. In addition to currently annotated 586 lncRNAs and 454 asRNAs, we identified 1037 lncRNAs and 243 asRNAs by de novo assembly. Of these, 119 lncRNAs showed altered expression in the absence of NRL and included NRL binding sites in their promoter/enhancer regions. ISH studies validated the expression of 24 lncRNAs (including 12 previously unannotated) and 4 asRNAs in photoreceptors. Coexpression analysis demonstrated 63 functional modules and 209 significant antisense-gene correlations, allowing us to predict possible role of these lncRNAs in rods.

Conclusions

Our studies reveal coregulation of coding and noncoding transcripts in rod photoreceptors by NRL and establish the framework for deciphering the function of ncRNAs during retinal development.

Discovery and Annotation of Plant Endogenous Target Mimicry Sequences from Public Transcriptome Libraries: A Case Study of Prunus persica

Novel transcript discovery through RNA sequencing has substantially improved our understanding of the transcriptome dynamics of biological systems. Endogenous target mimicry (eTM) transcripts, a novel class of regulatory molecules, bind to their target microRNAs (miRNAs) by base pairing and block their biological activity. The objective of this study was to provide a computational analysis framework for the prediction of putative eTM sequences in plants, and as an example, to discover previously un-annotated eTMs in Prunus persica (peach) transcriptome. Therefore, two public peach transcriptome libraries downloaded from Sequence Read Archive (SRA) and a previously published set of long non-coding RNAs (lncRNAs) were investigated with multi-step analysis pipeline, and 44 putative eTMs were found. Additionally, an eTM-miRNA-mRNA regulatory network module associated with peach fruit organ development was built via integration of the miRNA target information and predicted eTM-miRNA interactions. My findings suggest that one of the most widely expressed miRNA families among diverse plant species, miR156, might be potentially sponged by seven putative eTMs. Besides, the study indicates eTMs potentially play roles in the regulation of development processes in peach fruit via targeting specific miRNAs. In conclusion, by following the step-by step instructions provided in this study, novel eTMs can be identified and annotated effectively in public plant transcriptome libraries.

PeTMbase: A Database of Plant Endogenous Target Mimics (eTMs)

MicroRNAs (miRNA) are small endogenous RNA molecules, which regulate target gene expression at post-transcriptional level. Besides, miRNA activity can be controlled by a newly discovered regulatory mechanism called endogenous target mimicry (eTM). In target mimicry, eTMs bind to the corresponding miRNAs to block the binding of specific transcript leading to increase mRNA expression. Thus, miRNA-eTM-target-mRNA regulation modules involving a wide range of biological processes; an increasing need for a comprehensive eTM database arose. Except miRSponge with limited number of Arabidopsis eTM data no available database and/or repository was developed and released for plant eTMs yet. Here, we present an online plant eTM database, called PeTMbase (http://petmbase.org), with a highly efficient search tool. To establish the repository a number of identified eTMs was obtained utilizing from high-throughput RNA-sequencing data of 11 plant species. Each transcriptome libraries is first mapped to corresponding plant genome, then long non-coding RNA (lncRNA) transcripts are characterized. Furthermore, additional lncRNAs retrieved from GREENC and PNRD were incorporated into the lncRNA catalog. Then, utilizing the lncRNA and miRNA sources a total of 2,728 eTMs were successfully predicted. Our regularly updated database, PeTMbase, provides high quality information regarding miRNA:eTM modules and will aid functional genomics studies particularly, on miRNA regulatory networks.

Differential Expression of Hyperhydricity Responsive Peach MicroRNAs

Hyperhydricity is a syndrome that causes morpho-physiological malformations in tissue culture plantlets. Micro-RNAs (miRNA) are small non-coding RNAs that play important regulatory roles in plant development, stress response, and adaptation to environmental conditions. In this study, differential expression analysis indicated that miRNAs play an underlying role in the responses to the hyperhydricity syndrome in peach Prunus persica (L.) leaves. 24 known and three novel potential miRNAs were characterized in hyperhydric and non-hyperhydric transcriptome libraries. The miRNA-target transcript analyses indicated that transport, plant cuticle development, intracellular part, and stress response are regulated by miRNAs in hyperhydric leaves. It is also suggested that miR5021 and miRnovel2 might play critical regulatory roles in hyperhydricity regarding miRNA-based response to stress. This study went one step further to advance understanding of molecular miRNA-based regulatory mechanisms regarding responses to hyperhydricity in peach.

sandflyDST: a dynamic web-based decision support tool for the morphological identification of sandflies present in Anatolia and mainland Europe, and user study

Species identification of sandflies is mainly performed according to morphological characters using classical written identification keys. This study introduces a new web-based decision support tool (sandflyDST) for guiding the morphological identification of sandfly species present in Anatolia and mainland Europe and classified in the Phlebotomus and Sergentomyia genera (both: Diptera: Psychodidae). The current version of the tool consists of 111 questions and 36 drawings obtained from classical written keys, and 107 photographs for the quick and easy identification of 26 species of the genus Phlebotomus and four species of the genus Sergentomyia. The tool guides users through a decision tree using yes/no questions about the morphological characters of the specimen. The tool was applied by 30 individuals, who then completed study questionnaires. The results of subsequent analyses indicated that the usability (x‾SUSScore=75.4) and users' level of appreciation (86.6%) of the tool were quite high; almost all of the participants considered recommending the tool to others. The tool may also be useful in training new entomologists and maintaining their level of expertise. This is a dynamic tool and can be improved or upgraded according to feedback. The tool is now available online at http://parasitology.ege.edu.tr/sandflyDST/index.php.

Next generation sequencing technology and genomewide data analysis: Perspectives for retinal research

The advent of high throughput next generation sequencing (NGS) has accelerated the pace of discovery of disease-associated genetic variants and genomewide profiling of expressed sequences and epigenetic marks, thereby permitting systems-based analyses of ocular development and disease. Rapid evolution of NGS and associated methodologies presents significant challenges in acquisition, management, and analysis of large data sets and for extracting biologically or clinically relevant information. Here we illustrate the basic design of commonly used NGS-based methods, specifically whole exome sequencing, transcriptome, and epigenome profiling, and provide recommendations for data analyses. We briefly discuss systems biology approaches for integrating multiple data sets to elucidate gene regulatory or disease networks. While we provide examples from the retina, the NGS guidelines reviewed here are applicable to other tissues/cell types as well.

StatXFinder: a web-based self-directed tool that provides appropriate statistical test selection for biomedical researchers in their scientific studies

The improper use of statistical methods is common in analyzing and interpreting research data in biological and medical sciences. The objective of this study was to develop a decision support tool encompassing the commonly used statistical tests in biomedical research by combining and updating the present decision trees for appropriate statistical test selection. First, the decision trees in textbooks, published articles, and online resources were scrutinized, and a more comprehensive unified one was devised via the integration of 10 distinct decision trees. The questions also in the decision steps were revised by simplifying and enriching of the questions with examples. Then, our decision tree was implemented into the web environment and the tool titled StatXFinder was developed. Finally, usability and satisfaction questionnaires were applied to the users of the tool, and StatXFinder was reorganized in line with the feedback obtained from these questionnaires. StatXFinder provides users with decision support in the selection of 85 distinct parametric and non-parametric statistical tests by directing 44 different yes–no questions. The accuracy rate of the statistical test recommendations obtained by 36 participants, with the cases applied, were 83.3 % for “difficult” tests, and 88.9 % for “easy” tests. The mean system usability score of the tool was found 87.43 ± 10.01 (minimum: 70—maximum: 100). A statistically significant difference could not be seen between total system usability score and participants’ attributes (p value >0.05). The User Satisfaction Questionnaire showed that 97.2 % of the participants appreciated the tool, and almost all of the participants (35 of 36) thought of recommending the tool to the others. In conclusion, StatXFinder, can be utilized as an instructional and guiding tool for biomedical researchers with limited statistics knowledge. StatXFinder is freely available at http://webb.deu.edu.tr/tb/statxfinder.

CorRECTreatment: a web-based decision support tool for rectal cancer treatment that uses the analytic hierarchy process and decision tree

BACKGROUND

The selection of appropriate rectal cancer treatment is a complex multi-criteria decision making process, in which clinical decision support systems might be used to assist and enrich physicians' decision making.

OBJECTIVE

The objective of the study was to develop a web-based clinical decision support tool for physicians in the selection of potentially beneficial treatment options for patients with rectal cancer.

METHODS

The updated decision model contained 8 and 10 criteria in the first and second steps respectively. The decision support model, developed in our previous study by combining the Analytic Hierarchy Process (AHP) method which determines the priority of criteria and decision tree that formed using these priorities, was updated and applied to 388 patients data collected retrospectively. Later, a web-based decision support tool named corRECTreatment was developed. The compatibility of the treatment recommendations by the expert opinion and the decision support tool was examined for its consistency. Two surgeons were requested to recommend a treatment and an overall survival value for the treatment among 20 different cases that we selected and turned into a scenario among the most common and rare treatment options in the patient data set.

RESULTS

In the AHP analyses of the criteria, it was found that the matrices, generated for both decision steps, were consistent (consistency ratio<0.1). Depending on the decisions of experts, the consistency value for the most frequent cases was found to be 80% for the first decision step and 100% for the second decision step. Similarly, for rare cases consistency was 50% for the first decision step and 80% for the second decision step.

CONCLUSIONS

The decision model and corRECTreatment, developed by applying these on real patient data, are expected to provide potential users with decision support in rectal cancer treatment processes and facilitate them in making projections about treatment options.

Towards a web-based decision support tool for selecting appropriate statistical test in medical and biological sciences

Statistical hypothesis testing is an essential component of biological and medical studies for making inferences and estimations from the collected data in the study; however, the misuse of statistical tests is widely common. In order to prevent possible errors in convenient statistical test selection, it is currently possible to consult available test selection algorithms developed for various purposes. However, the lack of an algorithm presenting the most common statistical tests used in biomedical research in a single flowchart causes several problems such as shifting users among the algorithms, poor decision support in test selection and lack of satisfaction of potential users. Herein, we demonstrated a unified flowchart; covers mostly used statistical tests in biomedical domain, to provide decision aid to non-statistician users while choosing the appropriate statistical test for testing their hypothesis. We also discuss some of the findings while we are integrating the flowcharts into each other to develop a single but more comprehensive decision algorithm.

Computer based extraction of phenoptypic features of human congenital anomalies from the digital literature with natural language processing techniques

The lack of laboratory tests for the diagnosis of most of the congenital anomalies renders the physical examination of the case crucial for the diagnosis of the anomaly; and the cases in the diagnostic phase are mostly being evaluated in the light of the literature knowledge. In this respect, for accurate diagnosis, ,it is of great importance to provide the decision maker with decision support by presenting the literature knowledge about a particular case. Here, we demonstrated a methodology for automated scanning and determining of the phenotypic features from the case reports related to congenital anomalies in the literature with text and natural language processing methods, and we created a framework of an information source for a potential diagnostic decision support system for congenital anomalies.

A data-driven living review for pharmacogenomic decision support in cancer treatment

With drastically decreasing costs of genetic sequencing, it has become feasible to use individual genetic markers to optimize treatment selection in cancer therapy. However, it is still difficult for medical practitioners to integrate these new kinds of data into clinical routine, since available information is growing rapidly. We demonstrate how a blend of manual curation and automated data extraction and evidence synthesis can be used to generate a 'living review', a summarization of current evidence on cancer classification, corresponding genetic markers, genetic tests and treatment options that can be used by clinicians to refine treatment choices. In contrast to a classical review, this automated 'living review' offers the opportunity of automatically updating core content when available data changes, making it easier to keep an overview of the best current evidence. We discuss some of the findings we made while creating a prototype of a 'living review' for colorectal cancer pharmacotherapy.

Early biventricular molecular responses to an acute myocardial infarction

BACKGROUND

Acute myocardial infarction (AMI) remains as one of the most common lethal diseases in the world and therefore it is necessary to understand its effect on molecular basis. Genome-wide microarray analysis provides us to predict potential biomarkers and signaling pathways for this purpose.

OBJECTIVES

The aim of this study is to understand the molecular basis of the immediate right ventricular cellular response to left ventricular AMI.

MATERIAL AND METHODS

A rat model of left anterior descending coronary artery ligation was used to assess the effect of left ventricular AMI on both the right ventricle as a remote zone and the left ventricle as an ischemic/infarct zone. Microarray technology was applied to detect the gene expression. Gene Ontology and KEGG pathways analysis were done to identify effected pathways and related genes.

RESULTS

We found that immune response, cell chemotaxis, inflammation, cytoskeleton organization are significantly deregulated in ischemic zone as early response within 30 min. Unexpectedly, there were several affected signaling pathways such as cell chemotaxis, regulation of endothelial cell proliferation, and regulation of caveolea regulation of anti-apoptosis, regulation of cytoskeleton organization and cell adhesion on the remote zone in the right ventricle.

CONCLUSION

This data demonstrates that there is an immediate molecular response in both ventricles after an AMI. Although the ischemia did not histologically involve the right ventricle; there is a clear molecular response to the infarct in the left ventricle. This provides us new insights to understand molecular mechanisms behind AMI and to find more effective drug targets.

mESAdb: microRNA expression and sequence analysis database

microRNA expression and sequence analysis database (http://konulab.fen.bilkent.edu.tr/mirna/) (mESAdb) is a regularly updated database for the multivariate analysis of sequences and expression of microRNAs from multiple taxa. mESAdb is modular and has a user interface implemented in PHP and JavaScript and coupled with statistical analysis and visualization packages written for the R language. The database primarily comprises mature microRNA sequences and their target data, along with selected human, mouse and zebrafish expression data sets. mESAdb analysis modules allow (i) mining of microRNA expression data sets for subsets of microRNAs selected manually or by motif; (ii) pair-wise multivariate analysis of expression data sets within and between taxa; and (iii) association of microRNA subsets with annotation databases, HUGE Navigator, KEGG and GO. The use of existing and customized R packages facilitates future addition of data sets and analysis tools. Furthermore, the ability to upload and analyze user-specified data sets makes mESAdb an interactive and expandable analysis tool for microRNA sequence and expression data.