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Subclinical life style disease can cause endothelial dysfunction associated with perfusion abnormalities and reduced vascular compliance. Subclinical elevated beta type natriuretic peptide (BNP) has been associated with altered fluid shift from extra to intracellular space during acute hypoxia. Therefore we measured vascular response and BNP levels during acute hypoxia to study endothelial functions among healthy individuals.Individuals were exposed to acute normobaric hypoxia of FiO2 = 0.15 for one hour in supine position and their pulmonary and systemic vascular response to hypoxia was compared. Individuals were divided into two groups based on either no response (Group 1) or rise in systolic pulmonary artery pressure to hypoxia (Group 2) and their BNP levels were compared.BNP was raised after hypoxia exposure in group 2 only from 18.52 ± 7 to 21.56 ± 10.82 picogram/ml, p 0.05. Group 2 also showed an increase in mean arterial pressure and no fall in total body water in response to acute hypoxia indicating decreased endothelial function compared to Group 1.Rise in pulmonary artery pressure and BNP level in response to acute normobaric hypoxia indicates reduced endothelial function and can be used to screen subclinical lifestyle disease among healthy population.

Summary Typical RNA sequencing (RNA-Seq) analyses are performed either at the gene level by summing all reads from the same locus, assuming that all transcripts from a gene make a protein or at the transcript level, assuming that each transcript displays unique function. However, these assumptions are flawed, as a gene can code for different types of transcripts and different transcripts are capable of synthesizing similar, different or no protein. As a consequence, functional changes are not well illustrated by either gene or transcript analyses. We propose to improve RNA-Seq analyses by grouping the transcripts based on their similar functions. We developed FuSe to predict functional similarities using the primary and secondary structure of proteins. To estimate the likelihood of proteins with similar functions, FuSe computes two confidence scores: knowledge (KS) and discovery (DS) for protein pairs. Overlapping protein pairs exhibiting high confidence are grouped to form ‘similar function protein groups’ and expression is calculated for each functional group. The impact of using FuSe is demonstrated on in vitro cells exposed to paracetamol, which highlight genes responsible for cell adhesion and glycogen regulation which were earlier shown to be not differentially expressed with traditional analysis methods. Availability and implementation The source code is available at https://github.com/rajinder4489/FuSe. Data for APAP exposure are available in the BioStudies database (http://www.ebi.ac.uk/biostudies) under accession numbers S-HECA143, S-HECA(158) and S-HECA139. Supplementary information Supplementary data are available at Bioinformatics online.

Background Hepatocellular carcinoma (HCC) is one of the leading causes of cancer death in the world owing to limitations in its prognosis. The current prognosis approaches include radiological examination and detection of serum biomarkers, however, both have limited efficiency and are ineffective in early prognosis. Due to such limitations, we propose to use RNA-Seq data for evaluating putative higher accuracy biomarkers at the transcript level that could help in early prognosis. Methods To identify such potential transcript biomarkers, RNA-Seq data for healthy liver and various HCC cell models were subjected to five different machine learning algorithms: random forest, K-nearest neighbor, Naïve Bayes, support vector machine, and neural networks. Various metrics, namely sensitivity, specificity, MCC, informedness, and AUC-ROC (except for support vector machine) were evaluated. The algorithms that produced the highest values for all metrics were chosen to extract the top features that were subjected to recursive feature elimination. Through recursive feature elimination, the least number of features were obtained to differentiate between the healthy and HCC cell models. Results From the metrics used, it is demonstrated that the efficiency of the known protein biomarkers for HCC is comparatively lower than complete transcriptomics data. Among the different machine learning algorithms, random forest and support vector machine demonstrated the best performance. Using recursive feature elimination on top features of random forest and support vector machine three transcripts were selected that had an accuracy of 0.97 and kappa of 0.93. Of the three transcripts, two were protein coding (PARP2–202 and SPON2–203) and one was a non-coding transcript (CYREN-211). Lastly, we demonstrated that these three selected transcripts outperformed randomly taken three transcripts (15,000 combinations), hence were not chance findings, and could then be an interesting candidate for new HCC biomarker development. Conclusion Using RNA-Seq data combined with machine learning approaches can aid in finding novel transcript biomarkers. The three biomarkers identified: PARP2–202, SPON2–203, and CYREN-211, presented the highest accuracy among all other transcripts in differentiating the healthy and HCC cell models. The machine learning pipeline developed in this study can be used for any RNA-Seq dataset to find novel transcript biomarkers. Code: www.github.com/rajinder4489/ML_biomarkers

Cancer is a huge problem of disease globally. Today, the percentage of people die from cancer is more than a combination of various diseases. In females, most common types of malignancies that occur are breast and cervical. The present focus has been shifted on medicinal plants as a form of therapy and there is a constant need to identify new therapeutic agents. Choerospondias axillaris (C. axillaris), an underutilized fruit, has been used in the remedy of various diseases. In the present communication, we evaluated the molecular mechanism of C. axillaris methanol extract in regulating cell death in human breast cancer cells (MDA-MB-231).Methanol extract of C. axillaris was prepared and compounds were screened by Gas chromatography-mass spectrometry. The effect of fruit extract was determined on MDA-MB-231 cells by MTT ((3-(4, 5-dimethylthiazol-2-yl)-2, 5-diphenyltetrazolium bromide) assay and to analyse the molecular mechanism of human breast cancer cells after treating with fruit extract, protein profiling study was performed by two-dimensional gel electrophoresis.A total 9 differentially expressed proteins were identified by matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF-MS/MS) analysis. Among 9 identified proteins, synphilin-1 protein was found to be significantly downregulated, validated by western blot and RT-qPCR analysis. Possible interacting partners of synphilin-1 (SNCAIP) were analyzed for their possible role in cancer by the in-silico method.Our data implicate that the presence of bioactive compound(s) in C. axillaris fruits might play an important role in inhibiting the proliferation of breast carcinoma cells and Synphilin-1 protein may play a role of apoptotic function.

The liver plays an important role in xenobiotic metabolism and represents a primary target for toxic substances. Many different in vitro cell models have been developed in the past decades. In this study, we used RNA-sequencing (RNA-Seq) to analyze the following human in vitro liver cell models in comparison to human liver tissue: cancer-derived cell lines (HepG2, HepaRG 3D), induced pluripotent stem cell-derived hepatocyte-like cells (iPSC-HLCs), cancerous human liver-derived assays (hPCLiS, human precision cut liver slices), non-cancerous human liver-derived assays (PHH, primary human hepatocytes) and 3D liver microtissues. First, using CellNet, we analyzed whether these liver in vitro cell models were indeed classified as liver, based on their baseline expression profile and gene regulatory networks (GRN). More comprehensive analyses using non-differentially expressed genes (non-DEGs) and differential transcript usage (DTU) were applied to assess the coverage for important liver pathways. Through different analyses, we noticed that 3D liver microtissues exhibited a high similarity with in vivo liver, in terms of CellNet (C/T score: 0.98), non-DEGs (10,363) and pathway coverage (highest for 19 out of 20 liver specific pathways shown) at the beginning of the incubation period (0 h) followed by a decrease during long-term incubation for 168 and 336 h. PHH also showed a high degree of similarity with human liver tissue and allowed stable conditions for a short-term cultivation period of 24 h. Using the same metrics, HepG2 cells illustrated the lowest similarity (C/T: 0.51, non-DEGs: 5623, and pathways coverage: least for 7 out of 20) with human liver tissue. The HepG2 are widely used in hepatotoxicity studies, however, due to their lower similarity, they should be used with caution. HepaRG models, iPSC-HLCs, and hPCLiS ranged clearly behind microtissues and PHH but showed higher similarity to human liver tissue than HepG2 cells. In conclusion, this study offers a resource of RNA-Seq data of several biological replicates of human liver cell models in vitro compared to human liver tissue. Electronic supplementary material The online version of this article (10.1007/s00204-020-02937-6) contains supplementary material, which is available to authorized users.

Acrylamide (ACR) is a known neurotoxicant which crosses the blood–brain barrier, passes the placenta and has been detected in breast milk. Hence, early-life exposure to ACR could lead to developmental neurotoxicity. The aim of this study was to elucidate if non-cytotoxic concentrations of ACR alter neuronal differentiation by studying gene expression of markers significant for neurodevelopment in the human neuroblastoma SH-SY5Y cell model. Firstly, by using RNASeq we identified two relevant pathways that are activated during 9 days of retinoic acid (RA) induced differentiation i.e. RA receptor (RAR) activation and the cAMP response element-binding protein (CREB) signalling pathways. Next, by qPCR we showed that 1 and 70 µM ACR after 9 days exposure alter the expression of 13 out of 36 genes in the RAR activation pathway and 18 out of 47 in the CREB signalling pathway. Furthermore, the expression of established neuronal markers i.e. BDNF, STXBP2, STX3, TGFB1 and CHAT were down-regulated. Decreased protein expression of BDNF and altered ratio of phosphorylated CREB to total CREB were confirmed by western blot. Our results reveal that micromolar concentrations of ACR sustain proliferation, decrease neurite outgrowth and interfere with signalling pathways involved in neuronal differentiation in the SH-SY5Y cell model.

Many brilliant minds are at work to decipher the biological labyrinth and as a result immense amount of information about biological entities and their relationships is getting accumulated in the form of published literature (Hunter and Cohen, 2006). To cater the needs of a researcher, many tools are designed to perform tasks of Named Entity Recognition (NER), Information Retrieval (IR), and Information Extraction (IE) viz. A Combined Clinical Concept Annotator (Kang et al., 2012), BANNER (Leaman and Gonzalez, 2008), Biblio-MetReS (Usie et al., 2014), BioTextQuest+ (Papanikolaou et al., 2014), BIOSMILE Web Search (Dai et al., 2008), E3Miner (Lee et al., 2008), EBIMed (Rebholz-Schuhmann et al., 2007), eFIP (Arighi et al., 2011), FACTA+ (Tsuruoka et al., 2008), GNSuite1, iHOP (Hoffmann and Valencia, 2004), MyMiner (Salgado et al., 2012), RLIMS-P(Hu et al., 2005), Anni (Jelier et al., 2008), CoPub (Frijters et al., 2008), MedScan (Novichkova et al., 2003), PPInterFinder (Raja et al., 2012), pGenN (Ding et al., 2015), SciMiner (Hur et al., 2009), BIGNER (Li et al., 2009), hybrid named entity tagger (Raja et al., 2014), and more such tools can be obtained from BIONLP resource2 and in detail analysis of many NLP tools is given by Krallinger et al. (2008) and Fleuren and Alkema (2015). Table ​Table11 gives an informational and statistical insight into some of these literature mining tools, shedding light on their efficiency translated by statistical parameters viz. F-score, recall, and precision. Many tools are domain specific like kinase family specific but still calls for human intervention for exactitude and thus limit their usage. Moreover, the data output formats are sometimes too vague as name highlighting; to be put to use for bigger literature searches. Table 1 Informational (viz. data used, parameters for evaluation and working platform) and statistical (viz. f-value, recall and precision) insights for a few literature mining tools with their brief description and links to the tools' home page. The naming ambiguity in scientific literature is one of the major concerns for NER and sentence structure for IR and IE. Presently, NER tools need to maintain a comprehensive dictionary of all names, aliases and web-repository specific IDs or have their AI (Artificial Intelligence) defined algorithms trained on many test data sets. Many such dictionaries are available but the list is ever-increasing and so is the training data set. This results into investing more money, time and effort in obtaining a comprehensive list of names, aliases and IDs. A very comprehensive work on NLP can be found on BioNLP3. The availability of manpower or intellect is huge but there is acute scarcity of funds (Bourne et al., 2015), so we have to device optimized approaches to take care of the issues discussed in subsequent section.

The mouth is the major portal of entry to the body and is equipped with formidable mechanisms for sensing the environment and defending against toxins or invading pathogens. The oral cavity can be considered as a window to the body because many important systemic disorders manifest in the oral cavity. The dentist is frequently the first medical person to encounter such disorders. These oral manifestations must be properly recognized if the patient is to receive appropriate diagnosis and referral for treatment.

SYNOPTIC ABSTRACTThis paper considers the problem of simultaneous predicted response and prediction of average value of the study variable in a linear regression model when some prior exact restrictions are available, which bind the regression coefficients. The performance properties of the predictors based on restricted least squares and improved estimators are analyzed. A comparison of these predictors with respect to risk as the performance criterion is then presented.

High-throughput small RNA (sRNA) sequencing technology enables an entirely new perspective for plant microRNA (miRNA) research and has immense potential to unravel regulatory networks. Novel insights gained through data mining in publically available rich resource of sRNA data will help in designing biotechnology-based approaches for crop improvement to enhance plant yield and nutritional value. Bioinformatics resources enabling meta-analysis of miRNA expression across multiple plant species are still evolving. Here, we report PmiRExAt, a new online database resource that caters plant miRNA expression atlas. The web-based repository comprises of miRNA expression profile and query tool for 1859 wheat, 2330 rice and 283 maize miRNA. The database interface offers open and easy access to miRNA expression profile and helps in identifying tissue preferential, differential and constitutively expressing miRNAs. A feature enabling expression study of conserved miRNA across multiple species is also implemented. Custom expression analysis feature enables expression analysis of novel miRNA in total 117 datasets. New sRNA dataset can also be uploaded for analysing miRNA expression profiles for 73 plant species. PmiRExAt application program interface, a simple object access protocol web service allows other programmers to remotely invoke the methods written for doing programmatic search operations on PmiRExAt database. Database URL: http://pmirexat.nabi.res.in.

A micro-mechanical approach is used to discover a necessary and sufficient condition predicting the superiority of two solid phases firmly bonded together at the interfaces. A lower bound for the time of rupture of the composite is also presented in terms of time of rupture of its constituents. The mixture is assumed to be homogeneous on a microscopic scale, but not necessarily isotropic. However, the two phases themselves are assumed to be isotropic and homogeneous. The stress and the strain fields throughout the mixture are assumed to be microscopically non-homogeneous.

For examining the intricate biological processes concerned with colorectal cancer (CRC), a systems biology approach integrating several biological components and other influencing factors is essential to understand. We performed a comprehensive system level analysis for CRC which assisted in unravelling crucial network components and many regulatory elements through a coordinated view. Using this integrative approach, the perceptive of complexity hidden in a biological phenomenon is extensively simplified. The microarray analyses facilitated differential expression of 631 significant genes employed in the progression of disease and supplied interesting associated up and down regulated genes like jun, fos and mapk1. The transcriptional regulation of these genes was deliberated widely by examining transcription factors such as hnf4, nr2f1, znf219 and dr1 which directly influence the expression. Further, interactions of these genes/proteins were evaluated and crucial network motifs were detected to associate with the pathophysiology of CRC. The available standard statistical parameters such as z-score, p-value and significance profile were explored for the identification of key signatures from CRC pathway whereas a few novel parameters representing over-represented structures were also designed in the study. The applied approach revealed 5 key genes i.e. kras, araf, pik3r5, ralgds and akt3 via our novel designed parameters illustrating high statistical significance. These novel parameters can assist in scrutinizing candidate markers for diseases having known biological pathways. Further, investigating and targeting these proposed genes for experimental validations, instead being spellbound by the complicated pathway will certainly endow valuable insight in a well-timed systematic understanding of CRC.

The renal proximal tubule is responsible for re-absorption of the majority of the glomerular filtrate and its proper function is necessary for whole-body homeostasis. Aging, certain diseases and chemical-induced toxicity are factors that contribute to proximal tubule injury and chronic kidney disease progression. To better understand these processes, it would be advantageous to generate renal tissues from human induced pluripotent stem cells (iPSC). Here, we report the differentiation and characterization of iPSC lines into proximal tubular-like cells (PTL). The protocol is a step wise exposure of small molecules and growth factors, including the GSK3 inhibitor (CHIR99021), the retinoic acid receptor activator (TTNPB), FGF9 and EGF, to drive iPSC to PTL via cell stages representing characteristics of early stages of renal development. Genome-wide RNA sequencing showed that PTL clustered within a kidney phenotype. PTL expressed proximal tubular-specific markers, including megalin (LRP2), showed a polarized phenotype, and were responsive to parathyroid hormone. PTL could take up albumin and exhibited ABCB1 transport activity. The phenotype was stable for up to 7 days and was maintained after passaging. This protocol will form the basis of an optimized strategy for molecular investigations using iPSC derived PTL.