Showing papers in "Current Genomics in 2020"
TL;DR: In this paper, a metagenomic analysis of microbial population engaged in the plastic biodegradation is recommended to decipher the microbial community structure and to predict their biode degradation potential in situ.
Abstract: Since the last few decades, the promiscuous and uncontrolled use of plastics led to the accumulation of millions of tons of plastic waste in the terrestrial and marine environment. It elevated the risk of environmental pollution and climate change. The concern arises more due to the reckless and unscientific disposal of plastics containing high molecular weight polymers, viz., polystyrene, polyamide, polyvinylchloride, polypropylene, polyurethane, and polyethylene, etc. which are very difficult to degrade. Thus, the focus is now paid to search for efficient, eco-friendly, low-cost waste management technology. Of them, degradation of non-degradable synthetic polymer using diverse microbial agents, viz., bacteria, fungi, and other extremophiles become an emerging option. So far, very few microbial agents and their secreted enzymes have been identified and characterized for plastic degradation, but with low efficiency. It might be due to the predominance of uncultured microbial species, which consequently remain unexplored from the respective plastic degrading milieu. To overcome this problem, metagenomic analysis of microbial population engaged in the plastic biodegradation is advisable to decipher the microbial community structure and to predict their biodegradation potential in situ. Advancements in sequencing technologies and bioinformatics analysis allow the rapid metagenome screening that helps in the identification of total microbial community and also opens up the scope for mining genes or enzymes (hydrolases, laccase, etc.) engaged in polymer degradation. Further, the extraction of the core microbial population and their adaptation, fitness, and survivability can also be deciphered through comparative metagenomic study. It will help to engineer the microbial community and their metabolic activity to speed up the degradation process.
50 citations
TL;DR: Two single-cell sequencing platforms are compared: BD Rhapsody and 10x Genomics Chromium, including their different mechanisms and some scRNA-seq results obtained with them.
Abstract: The cell is the unit of life for all organisms, and all cells are certainly not the same So the technology to generate transcription expression or genomic DNA profiles from single cells is crucial Since its establishment in 2009, single-cell RNA sequencing (scRNA-seq) has emerged as a major driver of progress in biomedical research During the last three years, several new single-cell sequencing platforms have emerged Yet there are only a few systematic comparisons of the advantages and limitations of these commonly used platforms Here we compare two single-cell sequencing platforms: BD Rhapsody and 10x Genomics Chromium, including their different mechanisms and some scRNA-seq results obtained with them
38 citations
TL;DR: A review of recent works performed in building omics strategies that decipher the interactions between plant-microbiome aims to explore advances in the study of Arabidopsis as an important avenue to serve as a baseline tool to create models that help in scrutinizing various factors that contribute to the elaborate relationship between plants and microbes.
Abstract: Introduction Plants do not grow in isolation, rather they are hosts to a variety of microbes in their natural environments. While, few thrive in the plants for their own benefit, others may have a direct impact on plants in a symbiotic manner. Unraveling plant-microbe interactions is a critical component in recognizing the positive and negative impacts of microbes on plants. Also, by affecting the environment around plants, microbes may indirectly influence plants. The progress in sequencing technologies in the genomics era and several omics tools has accelerated in biological science. Studying the complex nature of plant-microbe interactions can offer several strategies to increase the productivity of plants in an environmentally friendly manner by providing better insights. This review brings forward the recent works performed in building omics strategies that decipher the interactions between plant-microbiome. At the same time, it further explores other associated mutually beneficial aspects of plant-microbe interactions such as plant growth promotion, nitrogen fixation, stress suppressions in crops and bioremediation; as well as provides better insights on metabolic interactions between microbes and plants through omics approaches. It also aims to explore advances in the study of Arabidopsis as an important avenue to serve as a baseline tool to create models that help in scrutinizing various factors that contribute to the elaborate relationship between plants and microbes. Causal relationships between plants and microbes can be established through systematic gnotobiotic experimental studies to test hypotheses on biologically derived interactions. Conclusion This review will cover recent advances in the study of plant-microbe interactions keeping in view the advantages of these interactions in improving nutrient uptake and plant health.
36 citations
TL;DR: A main goal is to provide an inclusive view of the existing sequence-based computational prediction of PPIs, and the state-of-the-art bioinformatics approaches, working principles, and their performances.
Abstract: Protein-protein interactions (PPIs) are the physical connections between two or more proteins via electrostatic forces or hydrophobic effects Identification of the PPIs is pivotal, which contributes to many biological processes including protein function, disease incidence, and therapy design The experimental identification of PPIs via high-throughput technology is time-consuming and expensive Bioinformatics approaches are expected to solve such restrictions In this review, our main goal is to provide an inclusive view of the existing sequence-based computational prediction of PPIs Initially, we briefly introduce the currently available PPI databases and then review the state-of-the-art bioinformatics approaches, working principles, and their performances Finally, we discuss the caveats and future perspective of the next generation algorithms for the prediction of PPIs
25 citations
TL;DR: The results obtained by the proposed tool show that this method may meet the future demand of hydroxylysine sites with a better prediction rate over the existing methods.
Abstract: Introduction Hydroxylation is one of the most important post-translational modifications (PTM) in cellular functions and is linked to various diseases. The addition of one of the hydroxyl groups (OH) to the lysine sites produces hydroxylysine when undergoes chemical modification. Methods The method which is used in this study for identifying hydroxylysine sites based on powerful mathematical and statistical methodology incorporating the sequence-order effect and composition of each object within protein sequences. This predictor is called "iHyd-LysSite (EPSV)" (identifying hydroxylysine sites by extracting enhanced position and sequence variant technique). The prediction of hydroxylysine sites by experimental methods is difficult, laborious and highly expensive. In silico technique is an alternative approach to identify hydroxylysine sites in proteins. Results The experimental results require that the predictive model should have high sensitivity and specificity values and must be more accurate. The self-consistency, independent, 10-fold cross-validation and jackknife tests are performed for validation purposes. These tests are resulted by using three renowned classifiers, Neural Networks (NN), Random Forest (RF) and Support Vector Machine (SVM) with the demanding prediction rate. The overall predictive outcomes are extraordinarily superior to the results obtained by previous predictors. The proposed model contributed an excellent prediction rate in the system for NN, RF, and SVM classifiers. The sensitivity and specificity results using all these classifiers for jackknife test are 96.08%, 94.99%, 98.16% and 97.52%, 98.52%, 80.95%. Conclusion The results obtained by the proposed tool show that this method may meet the future demand of hydroxylysine sites with a better prediction rate over the existing methods.
25 citations
TL;DR: A computational framework, WITMSG, dedicated to the large-scale prediction of intronic m6A RNA methylation sites in humans has been proposed here for the first time and outperformed competing approaches in 10-fold cross-validation and when tested on independent datasets.
Abstract: Introduction N 6-methyladenosine (m6A) is one of the most widely studied epigenetic modifications. It plays important roles in various biological processes, such as splicing, RNA localization and degradation, many of which are related to the functions of introns. Although a number of computational approaches have been proposed to predict the m6A sites in different species, none of them were optimized for intronic m6A sites. As existing experimental data overwhelmingly relied on polyA selection in sample preparation and the intronic RNAs are usually underrepresented in the captured RNA library, the accuracy of general m6A sites prediction approaches is limited for intronic m6A sites prediction task. Methodology A computational framework, WITMSG, dedicated to the large-scale prediction of intronic m6A RNA methylation sites in humans has been proposed here for the first time. Based on the random forest algorithm and using only known intronic m6A sites as the training data, WITMSG takes advantage of both conventional sequence features and a variety of genomic characteristics for improved prediction performance of intron-specific m6A sites. Results and conclusion It has been observed that WITMSG outperformed competing approaches (trained with all the m6A sites or intronic m6A sites only) in 10-fold cross-validation (AUC: 0.940) and when tested on independent datasets (AUC: 0.946). WITMSG was also applied intronome-wide in humans to predict all possible intronic m6A sites, and the prediction results are freely accessible at http://rnamd.com/intron/.
21 citations
TL;DR: Some of the recent advancements made in important bioengineering technologies to develop engineered microbial systems for enhanced pigments production using agri-food wastes biomass/by-products as substrates in a sustainable way are reviewed.
Abstract: Agri-food waste biomass is the most abundant organic waste and has high valorisation potential for sustainable bioproducts development. These wastes are not only recyclable in nature but are also rich sources of bioactive carbohydrates, peptides, pigments, polyphenols, vitamins, natural antioxidants, etc. Bioconversion of agri-food waste to value-added products is very important towards zero waste and circular economy concepts. To reduce the environmental burden, food researchers are seeking strategies to utilize this waste for microbial pigments production and further biotechnological exploitation in functional foods or value-added products. Microbes are valuable sources for a range of bioactive molecules, including microbial pigments production through fermentation and/or utilisation of waste. Here, we have reviewed some of the recent advancements made in important bioengineering technologies to develop engineered microbial systems for enhanced pigments production using agri-food wastes biomass/by-products as substrates in a sustainable way.
21 citations
TL;DR: This study proposed a novel computational predictor termed ERT-m6Apred, which predicts Saccharomyces cerevisiae m6A sites with higher accuracy, thus facilitating biological hypothesis generation and experimental validations.
Abstract: Introduction N6-methyladenosine (m6A) is one of the most common post-transcriptional modifications in RNA, which has been related to several biological processes. The accurate prediction of m6A sites from RNA sequences is one of the challenging tasks in computational biology. Several computational methods utilizing machine-learning algorithms have been proposed that accelerate in silico screening of m6A sites, thereby drastically reducing the experimental time and labor costs involved. Methodology In this study, we proposed a novel computational predictor termed ERT-m6Apred, for the accurate prediction of m6A sites. To identify the feature encodings with more discriminative capability, we applied a two-step feature selection technique on seven different feature encodings and identified the corresponding optimal feature set. Results Subsequently, performance comparison of the corresponding optimal feature set-based extremely randomized tree model revealed that Pseudo k-tuple composition encoding, which includes 14 physicochemical properties significantly outperformed other encodings. Moreover, ERT-m6Apred achieved an accuracy of 78.84% during cross-validation analysis, which is comparatively better than recently reported predictors. Conclusion In summary, ERT-m6Apred predicts Saccharomyces cerevisiae m6A sites with higher accuracy, thus facilitating biological hypothesis generation and experimental validations.
20 citations
TL;DR: The perspective of integrating genetic approach with bioremediation is crucial to evaluate connexions among microbial communities, plant communities and ecosystem processes with a focus on improving phytoremediations of contaminated sites.
Abstract: Background Accretion of organic and inorganic contaminants in soil interferes in the food chain, thereby posing a serious threat to the ecosystem and adversely affecting crop productivity and human life Both endophytic and rhizospheric microbial communities are responsible for the biodegradation of toxic organic compounds and have the capability to enhance the uptake of heavy metals by plants via phytoremediation approaches The diverse set of metabolic genes encoding for the production of biosurfactants and biofilms, specific enzymes for degrading plant polymers, modification of cell surface hydrophobicity and various detoxification pathways for the organic pollutants, plays a significant role in bacterial driven bioremediation Various genetic engineering approaches have been demonstrated to modulate the activity of specific microbial species in order to enhance their detoxification potential Certain rhizospheric bacterial communities are genetically modified to produce specific enzymes that play a role in degrading toxic pollutants Few studies suggest that the overexpression of extracellular enzymes secreted by plant, fungi or rhizospheric microbes can improve the degradation of specific organic pollutants in the soil Plants and microbes dwell synergistically, where microbes draw benefit by nutrient acquisition from root exudates whereas they assist in plant growth and survival by producing certain plant growth promoting metabolites, nitrogen fixation, phosphate solubilization, auxin production, siderophore production, and inhibition or suppression of plant pathogens Thus, the plant-microbe interaction establishes the foundation of the soil nutrient cycle as well as decreases soil toxicity by the removal of harmful pollutants Conclusion The perspective of integrating genetic approach with bioremediation is crucial to evaluate connexions among microbial communities, plant communities and ecosystem processes with a focus on improving phytoremediation of contaminated sites
18 citations
TL;DR: The studies provide solid evidence that miRNAs are detectable in serum, blood plasma, saliva, urine, and stool, and that they present easy-to-acquire biomarkers with strong diagnostic, prognostic and predictive potential.
Abstract: Pancreatic cancer (PaC) is one of the most lethal cancers, with an increasing global incidence rate. Unfavorable prognosis largely results from associated difficulties in early diagnosis and the absence of prognostic and predictive biomarkers that would enable an individualized therapeutic approach. In fact, PaC prognosis has not improved for years, even though much efforts and resources have been devoted to PaC research, and the multimodal management of PaC patients has been used in clinical practice. It is thus imperative to develop optimal biomarkers, which would increase diagnostic precision and improve the post-diagnostic management of PaC patients. Current trends in biomarker research envisage the unique opportunity of cell-free microRNAs (miRNAs) present in circulation to become a convenient, non-invasive tool for accurate diagnosis, prognosis and prediction of response to treatment. This review analyzes studies focused on cell-free miRNAs in PaC. The studies provide solid evidence that miRNAs are detectable in serum, blood plasma, saliva, urine, and stool, and that they present easy-to-acquire biomarkers with strong diagnostic, prognostic and predictive potential.
17 citations
TL;DR: This review made an attempt to point out the unique features of extremophiles, particularly thermophiles and psychrophiles, at the structural, genomic and proteomic levels, which allow for functionality at harsh conditions focusing on the temperature tolerance by them.
Abstract: The concurrence of microorganisms in niches that are hostile like extremes of temperature, pH, salt concentration and high pressure depends upon novel molecular mechanisms to enhance the stability of their proteins, nucleic acids, lipids and cell membranes. The structural, physiological and genomic features of extremophiles that make them capable of withstanding extremely selective environmental conditions are particularly fascinating. Highly stable enzymes exhibiting several industrial and biotechnological properties are being isolated and purified from these extremophiles. Successful gene cloning of the purified extremozymes in the mesophilic hosts has already been done. Various extremozymes such as amylase, lipase, xylanase, cellulase and protease from thermophiles, halothermophiles and psychrophiles are of industrial interests due to their enhanced stability at forbidding conditions. In this review, we made an attempt to point out the unique features of extremophiles, particularly thermophiles and psychrophiles, at the structural, genomic and proteomic levels, which allow for functionality at harsh conditions focusing on the temperature tolerance by them.
TL;DR: The application of ML approaches in miRNA discovery and target prediction with functions and future prospective is described and the implementation of a new era of computational methodologies in this direction would initiate further advanced levels of discoveries in mi RNA.
Abstract: MicroRNA (miRNA) is a small non-coding molecule that is involved in gene regulation and RNA silencing by complementary on their targets. Experimental methods for target prediction can be time-consuming and expensive. Thus, the application of the computational approach is implicated to enlighten these complications with experimental studies. However, there is still a need for an optimized approach in miRNA biology. Therefore, machine learning (ML) would initiate a new era of research in miRNA biology towards potential diseases biomarker. In this article, we described the application of ML approaches in miRNA discovery and target prediction with functions and future prospective. The implementation of a new era of computational methodologies in this direction would initiate further advanced levels of discoveries in miRNA.
TL;DR: Li et al. as discussed by the authors developed piRNAPred, an integrated framework for piRNA prediction employing hybrid features like k-mer nucleotide composition, secondary structure, thermodynamic and physicochemical properties.
Abstract: Rationale PIWI-interacting RNAs (piRNAs) are a recently-discovered class of small non-coding RNAs (ncRNAs) with a length of 21-35 nucleotides. They play a role in gene expression regulation, transposon silencing, and viral infection inhibition. Once considered as "dark matter" of ncRNAs, piRNAs emerged as important players in multiple cellular functions in different organisms. However, our knowledge of piRNAs is still very limited as many piRNAs have not been yet identified due to lack of robust computational predictive tools. Methods To identify novel piRNAs, we developed piRNAPred, an integrated framework for piRNA prediction employing hybrid features like k-mer nucleotide composition, secondary structure, thermodynamic and physicochemical properties. A non-redundant dataset (D3349 or D1684p+1665n) comprising 1684 experimentally verified piRNAs and 1665 non-piRNA sequences was obtained from piRBase and NONCODE, respectively. These sequences were subjected to the computation of various sequence-structure based features in binary format and trained using different machine learning techniques, of which support vector machine (SVM) performed the best. Results During the ten-fold cross-validation approach (10-CV), piRNAPred achieved an overall accuracy of 98.60% with Mathews correlation coefficient (MCC) of 0.97 and receiver operating characteristic (ROC) of 0.99. Furthermore, we achieved a dimensionality reduction of feature space using an attribute selected classifier. Conclusion We obtained the highest performance in accurately predicting piRNAs as compared to the current state-of-the-art piRNA predictors. In conclusion, piRNAPred would be helpful to expand the piRNA repertoire, and provide new insights on piRNA functions.
TL;DR: Besides identifying the initial causative factors, it will be important to illustrate the cascade of events that determines the fraction of the genome to convey altered methylation patterns specific towards each cancer type.
Abstract: Nearly three decades ago, the association between Bladder cancer (BC) and DNA methylation has initially been reported. Indeed, in the recent years, the mechanism connecting these two has gained deeper insights. Still, the mediocre performance of DNA methylation markers in the clinics raises the major concern. Strikingly, whether it is the inter-individual methylation variations or the paucity of knowledge about methylation fingerprints lying within histologically distinct subtypes of BC requires critical discussion. In the future, besides identifying the initial causative factors, it will be important to illustrate the cascade of events that determines the fraction of the genome to convey altered methylation patterns specific towards each cancer type.
TL;DR: This mini review will provide an overview of microbial degradation metabolic pathways for bioremediation along with the molecular and physiological properties of diverse extremophiles from variety of habitats.
Abstract: Microorganisms that are capable of live and adapt in hostile habitats of different environmental factors such as extremes temperature, salinity, nutrient availability and pressure are known as extremophiles. Exposure to xenobiotic compounds is global concern influencing the world population as a health hazard. Hence their removal is warranted using biological means that is very sustainable, potentially cost-effective and eco-friendly. Due to adaptation in extreme environments and unique defense mechanisms, they are receiving more attention for the bioremediation of the xenobiotic compounds. They possess robust enzymatic and biocatalytic systems that make them suitable for the effective removal of pollutants from the contaminated environment. Additionally, the extremophiles act as microfactories having specific genetic and biotechnological potential for the production of biomolecules. This mini review will provide an overview of microbial degradation metabolic pathways for bioremediation along with the molecular and physiological properties of diverse extremophiles from variety of habitats. Furthermore, the factors affecting the bioremediation process is also summarized.
TL;DR: The beneficial effects of engineered microorganisms through integrated sustainable agriculture production practices to improve the soil microbial health in order to increase crop productivity are discussed.
Abstract: Background Enhanced agricultural production is essential for increasing demand of the growing world population. At the same time, to combat the adverse effects caused by conventional agriculture practices to the environment along with the impact on human health and food security, a sustainable and healthy agricultural production needs to be practiced using beneficial microorganisms for enhanced yield. It is quite challenging because these microorganisms have rich biosynthetic repositories to produce biomolecules of interest; however, the intensive research in allied sectors and emerging genetic tools for improved microbial consortia are accepting new approaches that are helpful to farmers and agriculturists to meet the ever-increasing demand of sustainable food production. An important advancement is improved strain development via genetically engineered microbial systems (GEMS) as well as genetically modified microorganisms (GMOs) possessing known and upgraded functional characteristics to promote sustainable agriculture and food security. With the development of novel technologies such as DNA automated synthesis, sequencing and influential computational tools, molecular biology has entered the systems biology and synthetic biology era. More recently, CRISPR/Cas has been engineered to be an important tool in genetic engineering for various applications in the agri sector. The research in sustainable agriculture is progressing tremendously through GMOs/GEMS for their potential use in biofertilizers and as biopesticides. Conclusion In this review, we discuss the beneficial effects of engineered microorganisms through integrated sustainable agriculture production practices to improve the soil microbial health in order to increase crop productivity.
TL;DR: Changes in genes encoding ion channels or proteins regulating their functioning may increase the risk of migraines and correlate with clinical features of disease, e.g. age of onset and attack frequency.
Abstract: Background Migraine is a polygenetic disease, considered as a channelopathy. The dysregulation of ion functioning due to genetic changes may activate the trigeminovascular system and induce migraine attack both migraine with aura (MA) and without aura (MO). Objectives The aim of the study was to analyze the following variants of genes encoding ion channels and associated protein: c.3199G>A SCN1A, c.56G>A SCN2A, c.28A>G and c.328T>C KCNK18, c.3053A>G TRPA1, c.31-1811C>T STX1A in migraine patients. Patients and methods The study included 170 migraine patients and 173 controls. HRMA and Sanger sequencing were used for genotyping. Meta-analysis was performed for c.28A>G, c.328T>C KCNK18, and c.31-1811C>T STX1A. Results AA genotype of c.56G>A SCN2A was found only in migraine patients. Patients with c.328T>C KCNK18 mutation had an increased risk of developing migraine before the age of 18. Moreover, individuals with AA/TC haplotype of KCNK18 had higher attack frequency than those with AA/TT (p T STX1A was more frequent in MA patients than MO (p G TRPA1 polymorphism was more common in patients with migraine onset before the age of 15 (p T STX1A and c.3199G>A SCN1A before the age of 10 (p T STX1A polymorphism with migraine overall (OR=1.22, p=0.0086), MA, and MO. No association was found for c.28A>G KCNK18, c.328T>C KCNK18, and migraine overall. Conclusion Changes in genes encoding ion channels or proteins regulating their functioning may increase the risk of migraines and correlate with clinical features of disease, e.g. age of onset and attack frequency.
TL;DR: It is concluded that scRNA-seq should be combined with other sequencing methods in single-cell studies (e.g., CITE-seq).
Abstract: With the development of single-cell mRNA sequencing (scRNA-seq), researchers have attempted to identify new methods for performing in-depth studies of immune cells. However, the discrepancies between the mRNA levels and the levels of surface proteins have confused many researchers. Here, we report a significant and interesting phenomenon in which the mRNA and protein expression levels were mismatched in immune cells. We concluded that scRNA-seq should be combined with other sequencing methods in single-cell studies (e.g., CITE-seq). The simultaneous assessment of both mRNA and protein expression will enhance the precision and credibility of the results.
TL;DR: A bacterial strain AH-40 was isolated from crude oil polluted soil by enrichment technique in mineral basal salts medium supplemented with phenanthrene (PAH) as a sole carbon and energy source and degraded 97% of 150 mgphenanthrene L-1 within 15 days, which is faster than previously reported pure cultures.
Abstract: Background Petroleum polycyclic aromatic hydrocarbons (PAHs) are known to be toxic and carcinogenic for humans and their contamination of soils and water is of great environmental concern. Identification of the key microorganisms that play a role in pollutant degradation processes is relevant to the development of optimal in situ bioremediation strategies. Objective Detection of the ability of Pseudomonas fluorescens AH-40 to consume phenanthrene as a sole carbon source and determining the variation in the concentration of both nahAC and C23O catabolic genes during 15 days of the incubation period. Methods In the current study, a bacterial strain AH-40 was isolated from crude oil polluted soil by enrichment technique in mineral basal salts (MBS) medium supplemented with phenanthrene (PAH) as a sole carbon and energy source. The isolated strain was genetically identified based on 16S rDNA sequence analysis. The degradation of PAHs by this strain was confirmed by HPLC analysis. The detection and quantification of naphthalene dioxygenase (nahAc) and catechol 2,3-dioxygenase (C23O) genes, which play a critical role during the mineralization of PAHs in the liquid bacterial culture were achieved by quantitative PCR. Results Strain AH-40 was identified as pseudomonas fluorescens. It degraded 97% of 150 mg phenanthrene L-1 within 15 days, which is faster than previously reported pure cultures. The copy numbers of chromosomal encoding catabolic genes nahAc and C23O increased during the process of phenanthrene degradation. Conclusion nahAc and C23O genes are the main marker genes for phenanthrene degradation by strain AH-40. P. fluorescence AH-40 could be recommended for bioremediation of phenanthrene contaminated site.
TL;DR: It is found that no splicing prediction tools appear to be capable of reliably distinguishing those +2T>C variants that generate wild-type transcripts from those that do not, and the challenges that deep learning-based tools face in attempting to accurately identify splice-altering variants are highlighted.
Abstract: Introduction: 5' splice site GT>GC or +2T>C variants have been frequently reported to cause human genetic disease and are routinely scored as pathogenic splicing mutations. However, we have recently demonstrated that such variants in human disease genes may not invariably be pathogenic. Moreover, we found that no splicing prediction tools appear to be capable of reliably distinguishing those +2T>C variants that generate wild-type transcripts from those that do not.
Methodology: Herein, we evaluated the performance of a novel deep learning-based tool, SpliceAI, in the context of three datasets of +2T>C variants, all of which had been characterized functionally in terms of their impact on pre-mRNA splicing. The first two datasets refer to our recently described “in vivo” dataset of 45 known disease-causing +2T>C variants and the “in vitro” dataset of 103 +2T>C substitutions subjected to full-length gene splicing assay. The third dataset comprised 12 BRCA1 +2T>C variants that were recently analyzed by saturation genome editing.
Results: Comparison of the SpliceAI-predicted and experimentally obtained functional impact assessments of these variants (and smaller datasets of +2T>A and +2T>G variants) revealed that although SpliceAI performed rather better than other prediction tools, it was still far from perfect. A key issue was that the impact of those +2T>C (and +2T>A) variants that generated wild-type transcripts represents a quantitative change that can vary from barely detectable to an almost full expression of wild-type transcripts, with wild-type transcripts often co-existing with aberrantly spliced transcripts.
Conclusion: Our findings highlight the challenges that we still face in attempting to accurately identify splice-altering variants.
TL;DR: This review gives an insight into the recent advances that have been made in the plant-microbe interaction study in the post-genomic era and the application of those for enhancing agricultural production.
Abstract: Plant-microbe interactions are both symbiotic and antagonistic, and the knowledge of both these interactions is equally important for the progress of agricultural practice and produce. This review gives an insight into the recent advances that have been made in the plant-microbe interaction study in the post-genomic era and the application of those for enhancing agricultural production. Adoption of next-generation sequencing (NGS) and marker assisted selection of resistant genes in plants, equipped with cloning and recombination techniques, has progressed the techniques for the development of resistant plant varieties by leaps and bounds. Genome-wide association studies (GWAS) of both plants and microbes have made the selection of desirable traits in plants and manipulation of the genomes of both plants and microbes effortless and less time-consuming. Stress tolerance in plants has been shown to be accentuated by association of certain microorganisms with the plant, the study and application of the same have helped develop stress-resistant varieties of crops. Beneficial microbes associated with plants are being extensively used for the development of microbial consortia that can be applied directly to the plants or the soil. Next-generation sequencing approaches have made it possible to identify the function of microbes associated in the plant microbiome that are both culturable and non-culturable, thus opening up new doors and possibilities for the use of these huge resources of microbes that can have a potential impact on agriculture.
TL;DR: The present review updates the prevailing knowledge in the deployment of CRISPR/CAS9 techniques to understand plant-microbe interactions, genes edited for the development of fungal, bacterial and viral disease resistance, to elucidate the nodulation processes, plant growth promotion, and future implications in agriculture.
Abstract: Plant-microbe interactions can be either beneficial or harmful depending on the nature of the interaction. Multifaceted benefits of plant-associated microbes in crops are well documented. Specifically, the management of plant diseases using beneficial microbes is considered to be eco-friendly and the best alternative for sustainable agriculture. Diseases caused by various phytopathogens are responsible for a significant reduction in crop yield and cause substantial economic losses globally. In an ecosystem, there is always an equally daunting challenge for the establishment of disease and development of resistance by pathogens and plants, respectively. In particular, comprehending the complete view of the complex biological systems of plant-pathogen interactions, co-evolution and plant growth promotions (PGP) at both genetic and molecular levels requires novel approaches to decipher the function of genes involved in their interaction. The Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)/Cas9 (CRISPR-associated protein 9) is a fast, emerging, precise, eco-friendly and efficient tool to address the challenges in agriculture and decipher plant-microbe interaction in crops. Nowadays, the CRISPR/CAS9 approach is receiving major attention in the field of functional genomics and crop improvement. Consequently, the present review updates the prevailing knowledge in the deployment of CRISPR/CAS9 techniques to understand plant-microbe interactions, genes edited for the development of fungal, bacterial and viral disease resistance, to elucidate the nodulation processes, plant growth promotion, and future implications in agriculture. Further, CRISPR/CAS9 would be a new tool for the management of plant diseases and increasing productivity for climate resilience farming.
TL;DR: The exosomal ncRNAs [hsa-miRNA-1298, lncRNA-RP11-583F2.2] had better sensitivity and specificity than alpha-fetoprotein (AFP) in HCC diagnosis.
Abstract: Aim The aim of this study was to explore the expression of exosomal non-coding RNAs (ncRNAs) in the sera of patients with HCC versus control. Methods Firstly, Bioinformatics analysis was conducted to retrieve ncRNAs specific to HCC (hsa-miRNA-1298 and lncRNA-RP11-583F2.2). Afterwards, extraction and characterization of exosomes were performed. We measured the expression of the chosen exosomal RNAs by reverse transcriptase quantitative real-time PCR in sera of 60 patients with HCC, 42 patients with chronic hepatitis C (CHC) infection and 18 healthy normal volunteers. Results The exosomal ncRNAs [hsa-miRNA-1298, lncRNA-RP11-583F2.2] had better sensitivity and specificity than alpha-fetoprotein (AFP) in HCC diagnosis. Conclusion The exosomal hsa-miRNA-1298, lncRNA-RP11-583F2.2 can be potential biomarkers for HCC diagnosis.
TL;DR: Several factors including insomnia, circadian disruption, obesity, and intermittent hypoxia in obstructive sleep apnea are contributing risk factors for increased risk of several types of cancers.
Abstract: Background Sleep disorders have emerged as potential cancer risk factors. Objective This review discusses the relationships between sleep, obesity, and breathing disorders with concomitant risks of developing cancer. Results Sleep disorders result in abnormal expression of clock genes, decreased immunity, and melatonin release disruption. Therefore, these disorders may contribute to cancer development. Moreover, in sleep breathing disorder, which is frequently experienced by obese persons, the sufferer experiences intermittent hypoxia that may stimulate cancer cell proliferation. Discussion During short- or long- duration sleep, sleep-wake rhythm disruption may occur. Insomnia and obstructive sleep apnea increase cancer risks. In short sleepers, an increased risk of stomach cancer, esophageal squamous cell cancer, and breast cancer was observed. Among long sleepers (>9 hours), the risk of some hematologic malignancies is elevated. Conclusion Several factors including insomnia, circadian disruption, obesity, and intermittent hypoxia in obstructive sleep apnea are contributing risk factors for increased risk of several types of cancers. However, further studies are needed to determine the more significant of these risk factors and their interactions.
TL;DR: This study reported the genome organization, evolutionary characteristics and expression profile of the bHLH family in barley, which provide the targets for further functional analysis, but also facilitate better understanding of the regulatory network b HLH genes involved in stress tolerance in barley.
Abstract: Background The basic helix-loop-helix (bHLH) transcription factor is one of the most important gene families in plants, playing a key role in diverse metabolic, physiological, and developmental processes. Although it has been well characterized in many plants, the significance of the bHLH family in barley is not well understood at present. Methods Through a genome-wide search against the updated barley reference genome, the genomic organization, evolution and expression of the bHLH family in barley were systematically analyzed. Results We identified 141 bHLHs in the barley genome (HvbHLHs) and further classified them into 24 subfamilies based on phylogenetic analysis. It was found that HvbHLHs in the same subfamily shared a similar conserved motif composition and exon-intron structures. Chromosome distribution and gene duplication analysis revealed that segmental duplication mainly contributed to the expansion of HvbHLHs and the duplicated genes were subjected to strong purifying selection. Furthermore, expression analysis revealed that HvbHLHs were widely expressed in different tissues and also involved in response to diverse abiotic stresses. The co-expression network was further analyzed to underpin the regulatory function of HvbHLHs. Finally, 25 genes were selected for qRT-PCR validation, the expression profiles of HvbHLHs showed diverse patterns, demonstrating their potential roles in relation to stress tolerance regulation. Conclusion This study reported the genome organization, evolutionary characteristics and expression profile of the bHLH family in barley, which not only provide the targets for further functional analysis, but also facilitate better understanding of the regulatory network bHLH genes involved in stress tolerance in barley.
TL;DR: An overview of single-cell RNA sequencing applications in immunology and a prospect of future directions is provided.
Abstract: The complex immune system is involved in multiple pathological processes. Single-cell RNA sequencing (scRNA-seq) is able to analyze complex cell mixtures correct to a single cell and single molecule, thus is qualified to analyze immune reactions in several diseases. In recent years, scRNA-seq has been applied in many researching fields and has presented many innovative results. In this review, we intend to provide an overview of single-cell RNA sequencing applications in immunology and a prospect of future directions.
TL;DR: The plant pathogen resistance protein is described and how these proteins regulate host immunity during plant–virus interactions are discussed, which are crucial in antiviral defences.
Abstract: Viruses are obligate parasites that exist in an inactive state until they enter the host body. Upon entry, viruses become active and start replicating by using the host cell machinery. All plant viruses can augment their transmission, thus powering their detrimental effects on the host plant. To diminish infection and diseases caused by viruses, the plant has a defence mechanism known as pathogenesis-related biochemicals, which are metabolites and proteins. Proteins that ultimately prevent pathogenic diseases are called R proteins. Several plant R genes (that confirm resistance) and avirulence protein (Avr) (pathogen Avr gene-encoded proteins [effector/elicitor proteins involved in pathogenicity]) molecules have been identified. The recognition of such a factor results in the plant defence mechanism. During plant viral infection, the replication and expression of a viral molecule lead to a series of a hypersensitive response (HR) and affect the host plant's immunity (pathogen-associated molecular pattern-triggered immunity and effector-triggered immunity). Avr protein renders the host RNA silencing mechanism and its innate immunity, chiefly known as silencing suppressors towards the plant defensive machinery. This is a strong reply to the plant defensive machinery by harmful plant viruses. In this review, we describe the plant pathogen resistance protein and how these proteins regulate host immunity during plant-virus interactions. Furthermore, we have discussed regarding ribosome-inactivating proteins, ubiquitin proteasome system, translation repression (nuclear shuttle protein interacting kinase 1), DNA methylation, dominant resistance genes, and autophagy-mediated protein degradation, which are crucial in antiviral defences.
TL;DR: The latest progress and future prospects of single-cell sequencing technology in the field of stem cells is discussed.
Abstract: Variation and heterogeneity between cells are the basic characteristics of stem cells Traditional sequencing analysis methods often cover up this difference Single-cell sequencing technology refers to the technology of high-throughput sequencing analysis of genomes at the single-cell level It can effectively analyze cell heterogeneity and identify a small number of cell populations With the continuous progress of cell sorting, nucleic acid extraction and other technologies, single-cell sequencing technology has also made great progress Encouraging new discoveries have been made in stem cell research, including pluripotent stem cells, tissue-specific stem cells and cancer stem cells In this review, we discuss the latest progress and future prospects of single-cell sequencing technology in the field of stem cells
TL;DR: A major proportion of DMD subjects (80%) could be diagnosed by the MLPA technique and the data generated from this study may be beneficial for strengthening of mutation database in the North Indian population.
Abstract: Background Duchenne Muscular Dystrophy (DMD) is a progressive, fatal neuromuscular disorder caused by mutations in the DMD gene. Emerging antisense oligomer based exon skipping therapy provides hope for the restoration of the reading frame. Objectives Population-based DMD mutation database may enable exon skipping to be used for the benefit of patients. Hence, we planned this study to identify DMD gene variants in North Indian DMD cases. Methods A total of 100 DMD cases were recruited and Multiplex ligation-dependent probe amplification (MLPA) analysis was performed to obtain the deletion and duplication profile. Results Copy number variations (deletion/duplication) were found in 80.85% of unrelated DMD cases. Sixty-eight percent of cases were found to have variations in the distal hotspot region (Exon 45-55) of the DMD gene. Exon 44/45 variations were found to be the most prominent among single exon variations, whereas exon 49/50 was found to be the most frequently mutated locations in single/multiple exon variations. As per Leiden databases, 86.84% cases harboured out-of-frame mutations. Domain wise investigation revealed that 68% of mutations were localized in the region of spectrin repeats. Dp140 isoform was predicted to be absent in 62/76 (81.57%) cases. A total of 45/80 (56.25%) and 23/80 (28.70%) DMD subjects were predicted to be amenable to exon 51 and exon 45 skipping trials, respectively. Conclusion A major proportion of DMD subjects (80%) could be diagnosed by the MLPA technique. The data generated from our study may be beneficial for strengthening of mutation database in the North Indian population.
TL;DR: This work aims to present a complete survey of the existing ML-predictors for microbial phosphorylation, covering a variety of important aspects for developing a successful predictor, including operating ML algorithms, feature selection methods, window size, and software utility.
Abstract: A variety of protein post-translational modifications has been identified that control many cellular functions. Phosphorylation studies in mycobacterial organisms have shown critical importance in diverse biological processes, such as intercellular communication and cell division. Recent technical advances in high-precision mass spectrometry have determined a large number of microbial phosphorylated proteins and phosphorylation sites throughout the proteome analysis. Identification of phosphorylated proteins with specific modified residues through experimentation is often labor-intensive, costly and time-consuming. All these limitations could be overcome through the application of machine learning (ML) approaches. However, only a limited number of computational phosphorylation site prediction tools have been developed so far. This work aims to present a complete survey of the existing ML-predictors for microbial phosphorylation. We cover a variety of important aspects for developing a successful predictor, including operating ML algorithms, feature selection methods, window size, and software utility. Initially, we review the currently available phosphorylation site databases of the microbiome, the state-of-the-art ML approaches, working principles, and their performances. Lastly, we discuss the limitations and future directions of the computational ML methods for the prediction of phosphorylation.