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The human breast cancer resistance protein (BCRP), one of the members of the large ATP binding cassette (ABC) transporter superfamily, is crucial for resistance against chemotherapeutic agents. Currently, it has been emerged as one of the best biological targets for the designing of small molecule drugs capable of eliminating multidrug resistance in breast cancer. In order to gain insights into the relationship between the molecular structure of compounds and the ABCG2 inhibition, a multi-QSAR approach using different methods was performed on a dataset of 294 ABCG2 inhibitors with diverse scaffolds. The best models obtained by different chemometric methods have the following statistical characteristics: Monte Carlo Optimization-based QSAR (sensitivity = 0.905, specificity = 0.6255, accuracy = 0.756, and MCC = 0.545), Bayesian classification model (sensitivity = 0.735, specificity = 0.775, and concordance = 0.757); structural and physicochemical interpretation analysis-random forest method (balance accuracy = 0.750, sensitivity = 0.810, and specificity = 0.700). Additionally, structural fingerprints modulating the ABCG2 inhibitory properties were identified from the best models of each method and also validated with each other. The current modelling study is an attempt to get a deep insight into the different important structural fingerprints modulating ABCG2 inhibition.

Regulatory elements control gene expression through transcription initiation (promoters) and by enhancing transcription at distant regions (enhancers). Accurate identification of regulatory elements is fundamental for annotating genomes and understanding gene expression patterns. While there are many attempts to develop computational promoter and enhancer identification methods, reliable tools to analyze long genomic sequences are still lacking. Prediction methods often perform poorly on the genome-wide scale because the number of negatives is much higher than that in the training sets. To address this issue, we propose a dynamic negative set updating scheme with a two-model approach, using one model for scanning the genome and the other one for testing candidate positions. The developed method achieves good genome-level performance and maintains robust performance when applied to other vertebrate species, without re-training. Moreover, the unannotated predicted regulatory regions made on the human genome are enriched for disease-associated variants, suggesting them to be potentially true regulatory elements rather than false positives. We validated high scoring “false positive” predictions using reporter assay and all tested candidates were successfully validated, demonstrating the ability of our method to discover novel human regulatory regions., Author summary Identification of regulatory elements (promoters and enhancers) is important for understanding gene expression patterns. The set of promoters and enhancers is not complete for non-model organisms and even for the human genome there are still unannotated regions, such as alternative promoters for the known genes or promoters that are only expressed in a small fraction of cells or under specific conditions. Despite the development of experimental techniques, the regulatory regions annotation remains expensive and laborious and computational methods can speed up this process by providing candidates for the validation. We developed an easy-to-use tool capable of regulatory regions annotation in eukaryotic genomes. The developed method reduces the number of false positives made by including difficult samples in the training set. The method consists of two deep learning models, where one model scans the genome and identifies putative regulatory regions while the other model pinpoints the Transcription Start Site (TSS) location within the identified region. The predicted regions were validated using reporter assay, finding previously unknown regulatory regions in the human genome. The trained model achieved good genome-wide performance and was supported by meaningful extracted biological features.

U2 snRNP is an essential component of the spliceosome. It is responsible for branch point recognition in the spliceosome A-complex via base-pairing of U2 snRNA with an intron to form the branch helix. Small molecule inhibitors target the SF3B component of the U2 snRNP and interfere with A-complex formation during spliceosome assembly. We previously found that the first SF3B inhibited-complex is less stable than A-complex and hypothesized that SF3B inhibitors interfere with U2 snRNA secondary structure changes required to form the branch helix. Using RNA chemical modifiers, we probed U2 snRNA structure in A-complex and SF3B inhibited splicing complexes. The reactivity pattern for U2 snRNA in the SF3B inhibited-complex is indistinguishable from that of A-complex, suggesting that they have the same secondary structure conformation, including the branch helix. This observation suggests SF3B inhibited-complex instability does not stem from an alternate RNA conformation and instead points to the inhibitors interfering with protein component interactions that normally stabilize U2 snRNP’s association with an intron. In addition, we probed U2 snRNA in the free U2 snRNP in the presence of SF3B inhibitor and again saw no differences. However, increased protection of nucleotides upstream of Stem I in the absence of SF3A and SF3B proteins suggests a change of secondary structure at the very 5′ end of U2 snRNA. Chemical probing of synthetic U2 snRNA in the absence of proteins results in similar protections and predicts a previously uncharacterized extension of Stem I. Because this stem must be disrupted for SF3A and SF3B proteins to stably join the snRNP, the structure has the potential to influence snRNP assembly and recycling after spliceosome disassembly.

DNA topoisomerases are required to resolve DNA topological stress. Despite this essential role, abortive topoisomerase activity generates aberrant protein-linked DNA breaks, jeopardising genome stability. Here, to understand the genomic distribution and mechanisms underpinning topoisomerase-induced DNA breaks, we map Top2 DNA cleavage with strand-specific nucleotide resolution across the S. cerevisiae and human genomes—and use the meiotic Spo11 protein to validate the broad applicability of this method to explore the role of diverse topoisomerase family members. Our data characterises Mre11-dependent repair in yeast and defines two strikingly different fractions of Top2 activity in humans: tightly localised CTCF-proximal, and broadly distributed transcription-proximal, the latter correlated with gene length and expression. Moreover, single nucleotide accuracy reveals the influence primary DNA sequence has upon Top2 cleavage—distinguishing sites likely to form canonical DNA double-strand breaks (DSBs) from those predisposed to form strand-biased DNA single-strand breaks (SSBs) induced by etoposide (VP16) in vivo., Topoisomerase 2 (Top2) is known to resolve DNA topological stress through double strand breaks (DSBs), yet Top2 inhibition has been reported to result in a significant amount of single-strand breaks (SSBs). Here the authors develop CC-seq—a method that allows direct mapping of both Top2-linked SSBs and DSBs—and reveal a significant impact of primary DNA sequence on Top2 directed cleavage.

Deep neural networks have demonstrated improved performance at predicting the sequence specificities of DNA- and RNA-binding proteins compared to previous methods that rely on k-mers and position weight matrices. To gain insights into why a DNN makes a given prediction, model interpretability methods, such as attribution methods, can be employed to identify motif-like representations along a given sequence. Because explanations are given on an individual sequence basis and can vary substantially across sequences, deducing generalizable trends across the dataset and quantifying their effect size remains a challenge. Here we introduce global importance analysis (GIA), a model interpretability method that quantifies the population-level effect size that putative patterns have on model predictions. GIA provides an avenue to quantitatively test hypotheses of putative patterns and their interactions with other patterns, as well as map out specific functions the network has learned. As a case study, we demonstrate the utility of GIA on the computational task of predicting RNA-protein interactions from sequence. We first introduce a convolutional network, we call ResidualBind, and benchmark its performance against previous methods on RNAcompete data. Using GIA, we then demonstrate that in addition to sequence motifs, ResidualBind learns a model that considers the number of motifs, their spacing, and sequence context, such as RNA secondary structure and GC-bias., Author summary Although deep neural networks are becoming widely applied in genomics, it remains unclear why they make a given prediction. For model interpretability, attribution methods reveal the independent importance of single nucleotide variants in a given sequence on model predictions. While the resultant attribution maps can help to identify representations of motifs, it remains challenging to identify generalizable patterns across the dataset and to quantify their effect size on model predictions. Here, we introduce an interpretability method called global importance analysis (GIA) to quantify the effect size that putative patterns have on model predictions across a population of sequences. GIA provides a natural follow up to current interpretability methods to quantitatively test hypotheses of putative patterns (and their interactions with other patterns). As a case study, we demonstrate how it can be used for the computational task of predicting RNA-protein interactions and show that deep learning models can learn not only sequence motifs, but also the number of motifs, their spacing, and sequence context, such as RNA secondary structure and GC-bias.

ViroMatch is an automated pipeline that takes metagenomic sequencing reads as input and performs iterative nucleotide and translated nucleotide mapping to identify viral sequences. We provide a Docker image for ViroMatch, so that users will not have to install dependencies.

Faithful replication of the entire genome requires replication forks to copy large contiguous tracts of DNA, and sites of persistent replication fork stalling present a major threat to genome stability. Understanding the distribution of sites at which replication forks stall, and the ensuing fork processing events, requires genome-wide methods that profile replication fork position and the formation of recombinogenic DNA ends. Here, we describe Transferase-Activated End Ligation sequencing (TrAEL-seq), a method that captures single-stranded DNA 3′ ends genome-wide and with base pair resolution. TrAEL-seq labels both DNA breaks and replication forks, providing genome-wide maps of replication fork progression and fork stalling sites in yeast and mammalian cells. Replication maps are similar to those obtained by Okazaki fragment sequencing; however, TrAEL-seq is performed on asynchronous populations of wild-type cells without incorporation of labels, cell sorting, or biochemical purification of replication intermediates, rendering TrAEL-seq far simpler and more widely applicable than existing replication fork direction profiling methods. The specificity of TrAEL-seq for DNA 3′ ends also allows accurate detection of double-strand break sites after the initiation of DNA end resection, which we demonstrate by genome-wide mapping of meiotic double-strand break hotspots in a dmc1Δ mutant that is competent for end resection but not strand invasion. Overall, TrAEL-seq provides a flexible and robust methodology with high sensitivity and resolution for studying DNA replication and repair, which will be of significant use in determining mechanisms of genome instability., TrAEL-seq provides genome-wide base pair resolution maps of exposed DNA 3’ ends; this reveals replication fork stalling and normal replication profiles in asynchronous, unlabelled wildtype cell populations, along with the sites of resected DNA breaks.

Recombination is proposed to be critical for coronavirus (CoV) diversity and emergence of SARS-CoV-2 and other zoonotic CoVs. While RNA recombination is required during normal CoV replication, the mechanisms and determinants of CoV recombination are not known. CoVs encode an RNA proofreading exoribonuclease (nsp14-ExoN) that is distinct from the CoV polymerase and is responsible for high-fidelity RNA synthesis, resistance to nucleoside analogues, immune evasion, and virulence. Here, we demonstrate that CoVs, including SARS-CoV-2, MERS-CoV, and the model CoV murine hepatitis virus (MHV), generate extensive and diverse recombination products during replication in culture. We show that the MHV nsp14-ExoN is required for native recombination, and that inactivation of ExoN results in decreased recombination frequency and altered recombination products. These results add yet another critical function to nsp14-ExoN, highlight the uniqueness of the evolved coronavirus replicase, and further emphasize nsp14-ExoN as a central, completely conserved, and vulnerable target for inhibitors and attenuation of SARS-CoV-2 and future emerging zoonotic CoVs., Author summary Recombination is an essential part of normal coronavirus replication, required for the generation of the sub-genomic mRNAs as well as defective viral genome (DVGs) and is also implicated in novel strain emergence. However, the molecular mechanisms and determinants of RNA recombination in CoVs are unknown. Here, we compare recombination in 3 divergent beta-coronaviruses; murine hepatitis virus (MHV), MERS-CoV, and SARS-CoV-2. We show that they have striking similarities in the populations of RNA produced and in the sequences surrounding recombination junctions. Further, we demonstrate that the coronavirus proofreading exoribonuclease (nsp14-ExoN) is required to maintain the rates and loci of recombination generated during infection. These data suggest that recombination and the coronavirus exoribonuclease are conserved and important determinants of replication that may be targeted for inhibition and attenuation to control the ongoing pandemic of SARS-CoV-2 and prevent future outbreaks of novel coronaviruses.

In this study, optical technology is considered as SA issues' solution with the potential ability to increase the speed, overcome memory-limitation, reduce power consumption, and increase output accuracy. So we examine the effect of bio-data encoding and the creation of input images on the pattern-recognition error-rate at the output of optical Vander-lugt correlator. Moreover, we present a genetic algorithm-based coding approach, named as GAC, to minimize output noises of cross-correlating data. As a case study, we adopt the proposed coding approach within a correlation-based optical architecture for counting k-mers in a DNA string. As verified by the simulations on Salmonella whole-genome, we can improve sensitivity and speed more than 86% and 81%, respectively, compared to BLAST by using coding set generated by GAC method fed to the proposed optical correlator system. Moreover, we present a comprehensive report on the impact of 1D and 2D cross-correlation approaches, as-well-as various coding parameters on the output noise, which motivate the system designers to customize the coding sets within the optical setup.

Reverse translation of polypeptide sequences to expressible mRNA constructs is a NP-hard combinatorial optimization problem. Each amino acid in the protein sequence can be represented by as many as six codons, and the process of selecting the combination that maximizes probability of expression is termed codon optimization. This work investigates the potential impact of leveraging quantum computing technology for codon optimization. A Quantum Annealer (QA) is compared to a standard genetic algorithm (GA) programmed with the same objective function. The QA is found to be competitive in identifying optimal solutions. The utility of gate-based systems is also evaluated using a simulator resulting in the finding that while current generations of devices lack the hardware requirements, in terms of both qubit count and connectivity, to solve realistic problems, future generation devices may be highly efficient.

One of the great challenges in the field of bioinformatics is how to locate the accurate protein-coding regions in a given DNA sequence. The accurate identification of the protein-coding region is useful in many applications. For instance; it helps in characterizing new proteins, drug designing, and also in revealing the evolutionary background of a particular organism. DSP based techniques are quite popular in the protein-coding region identification. The first essential step of the DSP based exon prediction technique is to convert the base sequences into the numerical sequence. The choice of the numerical mapping scheme affects how well the characteristic feature of the DNA sequence is reflected in the numerical domain which helps in finding the accurate location of exons. In the last two decades, numbers of mapping schemes have been successfully used for exon prediction. However, locating the short length exon is still a difficult task. In this paper, we have proposed a tri-nucleotide mapping scheme that exploits the residual volume property of amino acid to encode the given DNA sequence. It is obtained that the proposed tri-nucleotide mapping scheme provides better results than other mapping schemes in case of short length exon detection.

In continuation of our earlier work (Doi: 10.1080/07391102.2019.1661876), a statistically validated and robust Bayesian model was developed on a large diverse set of HDAC8 inhibitors. The training set comprised of 676 small molecules and 293 compounds were considered as test set molecules. The findings of this analysis will help to explore some major directions regarding the HDAC8 inhibitor designing approach. Acrylamide (G1-G3, G9), N-substituted 2-phenylimidazole (G4-G8, G9, G12-G13, G16-G19), benzimidazole (G10-G11), piperidine substituted pyrrole (G13-G14) groups, alkyl/aryl amide (G15) and aryloxy carboxamide (G20) fingerprints were found to play a crucial role in HDAC8 inhibitory activity whereas -CH-N=CH- (B1, B4-B6, B14) motif, benzamide (B2-B3, B9-B13, B16-B17) groups and heptazepine (B7-B8, B15, B18-B20) group were found to influence negatively the HDAC8 inhibitory activity. The importance of such fingerprints was further validated by the HDAC8 enzyme and related inhibitor interactions at the receptor level. These results are in close agreement with those of our previous work that validate each other. Moreover, this comparative learning may enrich future endeavours regarding the designing strategy of HDAC8 inhibitors.

RNA modification mapping by mass spectrometry (MS) is based on the use of specific ribonucleases (RNases) that generate short oligonucleotide digestion products which are further separated by nano-liquid chromatography and analyzed by MS and MS/MS. Recent developments in MS instrumentation allow the possibility to deeply explore posttranscriptional modifications. Notably, development of nano-liquid chromatography and nano-electrospray drastically increases the detection sensitivity and allows the identification and sequencing of RNA digested fragments separated and extracted from two-dimensional polyacrylamide gels, as long as the mapping and characterization of ribonucleotide modifications.

MicroRNAs (miRNA) are small noncoding RNA sequences consisting of about 22 nucleotides that are involved in the regulation of almost 60% of mammalian genes. Presently, there are very limited approaches for the visualization of miRNA locations present inside cells to support the elucidation of pathways and mechanisms behind miRNA function, transport, and biogenesis. MIRLocator, a state-of-the-art tool for the prediction of subcellular localization of miRNAs makes use of a sequence-to-sequence model along with pretrained k-mer embeddings. Existing pretrained k-mer embedding generation methodologies focus on the extraction of semantics of k-mers. However, in RNA sequences, positional information of nucleotides is more important because distinct positions of the four nucleotides define the function of an RNA molecule. Considering the importance of the nucleotide position, we propose a novel approach (kmerPR2vec) which is a fusion of positional information of k-mers with randomly initialized neural k-mer embeddings. In contrast to existing k-mer-based representation, the proposed kmerPR2vec representation is much more rich in terms of semantic information and has more discriminative power. Using novel kmerPR2vec representation, we further present an end-to-end system (MirLocPredictor) which couples the discriminative power of kmerPR2vec with Convolutional Neural Networks (CNNs) for miRNA subcellular location prediction. The effectiveness of the proposed kmerPR2vec approach is evaluated with deep learning-based topologies (i.e., Convolutional Neural Networks (CNN) and Recurrent Neural Network (RNN)) and by using 9 different evaluation measures. Analysis of the results reveals that MirLocPredictor outperform state-of-the-art methods with a significant margin of 18% and 19% in terms of precision and recall.

The assay for transposase-accessible chromatin followed by sequencing (ATAC-seq) is an inexpensive protocol for measuring open chromatin regions. ATAC-seq is also relatively simple and requires fewer cells than many other high-throughput sequencing protocols. Therefore, it is tractable in numerous settings where other high throughput assays are challenging to impossible. Hence it is important to understand the limits of what can be inferred from ATAC-seq data. In this work, we leverage ATAC-seq to predict the presence of nascent transcription. Nascent transcription assays are the current gold standard for identifying regions of active transcription, including markers for functional transcription factor (TF) binding. We combine mapped short reads from ATAC-seq with the underlying peak sequence, to determine regions of active transcription genome-wide. We show that a hybrid signal/sequence representation classified using recurrent neural networks (RNNs) can identify these regions across different cell types.

Influenza A virus nucleoprotein (NP) association with viral RNA (vRNA) is essential for packaging, but the pattern of NP binding to vRNA is unclear. Here we applied photoactivatable ribonucleoside enhanced cross-linking and immunoprecipitation (PAR-CLIP) to assess the native-state of NP–vRNA interactions in infected human cells. NP binds short fragments of RNA (~12 nucleotides) non-uniformly and without apparent sequence specificity. Moreover, NP binding is reduced at specific locations within the viral genome, including regions previously identified as required for viral genome segment packaging. Synonymous mutations designed to alter the predicted RNA structures in these low-NP-binding regions impact genome packaging and result in virus attenuation, whereas control mutations or mutagenesis of NP-bound regions have no effect. Finally, we demonstrate that the sequence conservation of low-NP-binding regions is required in multiple genome segments for propagation of diverse mammalian and avian IAV in host cells., Influenza A virus packaging depends on interactions between nucleoprotein (NP) and viral RNA (vRNA), but the pattern of NP binding is unclear. Using PAR-CLIP, Williams et al. here show that NP binds vRNA non-uniformly and that RNA structures in low-NP binding regions are important for packaging.

Fowlpox (FP) is an economically important viral disease of commercial poultry. The fowlpox virus (FPV) is primarily characterised by immunoblotting, restriction enzyme analysis in combination with PCR, and/or nucleotide sequencing of amplicons. Whole-genome sequencing (WGS) of FPV directly from clinical specimens prevents the risk of potential genome modifications associated within vitroculturing of the virus. Only one study has sequenced FPV genomes directly from clinical samples using Nanopore sequencing, however, the study didn’t compare the sequences against Illumina sequencing or laboratory propagated sequences. Here, the suitability of WGS for strain identification of FPV directly from cutaneous tissue was evaluated, using a combination of Illumina and Nanopore sequencing technologies. Sequencing results were compared with the sequence obtained from FPV grown in chorioallantoic membranes (CAMs) of chicken embryos. Complete genome sequence of FPV was obtained directly from affected comb tissue using a map to reference approach. FPV sequence from cutaneous tissue was highly similar to that of the virus grown in CAMs with a nucleotide identity of 99.8%. Detailed polymorphism analysis revealed the presence of a highly comparable number of single nucleotide polymorphisms (SNPs) in the two sequences when compared to the reference genome, providing essentially the same strain identification information. Comparative genome analysis of the map to reference consensus sequences from the two genomes revealed that this field isolate had the highest nucleotide identity of 99.5% with an FPV strain from the USA (Fowlpox virus isolate, FWPV-MN00.2, MH709124) and 98.8% identity with the Australian FPV vaccine strain (FWPV-S, MW142017). Sequencing results showed that WGS directly from cutaneous tissues is not only rapid and cost-effective but also provides essentially the same strain identification information asin-vitrogrown virus, thus circumventingin vitroculturing.

MHC-I associated peptides (MAPs) play a central role in the elimination of virus-infected and neoplastic cells by CD8 T cells. However, accurately predicting the MAP repertoire remains difficult, because only a fraction of the transcriptome generates MAPs. In this study, we investigated whether codon arrangement (usage and placement) regulates MAP biogenesis. We developed an artificial neural network called Codon Arrangement MAP Predictor (CAMAP), predicting MAP presentation solely from mRNA sequences flanking the MAP-coding codons (MCCs), while excluding the MCC per se. CAMAP predictions were significantly more accurate when using original codon sequences than shuffled codon sequences which reflect amino acid usage. Furthermore, predictions were independent of mRNA expression and MAP binding affinity to MHC-I molecules and applied to several cell types and species. Combining MAP ligand scores, transcript expression level and CAMAP scores was particularly useful to increase MAP prediction accuracy. Using an in vitro assay, we showed that varying the synonymous codons in the regions flanking the MCCs (without changing the amino acid sequence) resulted in significant modulation of MAP presentation at the cell surface. Taken together, our results demonstrate the role of codon arrangement in the regulation of MAP presentation and support integration of both translational and post-translational events in predictive algorithms to ameliorate modeling of the immunopeptidome., Author summary MHC-I associated peptides (MAPs) are small fragments of intracellular proteins presented at the surface of cells and used by the immune system to detect and eliminate cancerous or virus-infected cells. While it is theoretically possible to predict which portions of the intracellular proteins will be naturally processed by the cells to ultimately reach the surface, current methodologies have prohibitively high false discovery rates. Here we introduce an artificial neural network called Codon Arrangement MAP Predictor (CAMAP) which integrates information from mRNA-to-protein translation to other factors regulating MAP biogenesis (e.g. MAP ligand score and transcript expression levels) to improve MAP prediction accuracy. While most MAP predictive approaches focus on MAP sequences per se, CAMAP’s novelty is to analyze the MAP-flanking mRNA sequences, thereby providing completely independent information for MAP prediction. We show on several datasets that the integration of CAMAP scores with other known factors involved in MAP presentation (i.e. MAP ligand score and mRNA expression) significantly improves MAP prediction accuracy, and further validate CAMAP learned features using an in-vitro assay. These findings may have major implications for the design of vaccines against cancers and viruses, and in times of pandemics could accelerate the identification of relevant MAPs of viral origins.

Recent advances in the scale and diversity of population genomic datasets for bacteria now provide the potential for genome-wide patterns of co-evolution to be studied at the resolution of individual bases. Here we describe a new statistical method, genomeDCA, which uses recent advances in computational structural biology to identify the polymorphic loci under the strongest co-evolutionary pressures. We apply genomeDCA to two large population data sets representing the major human pathogens Streptococcus pneumoniae (pneumococcus) and Streptococcus pyogenes (group A Streptococcus). For pneumococcus we identified 5,199 putative epistatic interactions between 1,936 sites. Over three-quarters of the links were between sites within the pbp2x, pbp1a and pbp2b genes, the sequences of which are critical in determining non-susceptibility to beta-lactam antibiotics. A network-based analysis found these genes were also coupled to that encoding dihydrofolate reductase, changes to which underlie trimethoprim resistance. Distinct from these antibiotic resistance genes, a large network component of 384 protein coding sequences encompassed many genes critical in basic cellular functions, while another distinct component included genes associated with virulence. The group A Streptococcus (GAS) data set population represents a clonal population with relatively little genetic variation and a high level of linkage disequilibrium across the genome. Despite this, we were able to pinpoint two RNA pseudouridine synthases, which were each strongly linked to a separate set of loci across the chromosome, representing biologically plausible targets of co-selection. The population genomic analysis method applied here identifies statistically significantly co-evolving locus pairs, potentially arising from fitness selection interdependence reflecting underlying protein-protein interactions, or genes whose product activities contribute to the same phenotype. This discovery approach greatly enhances the future potential of epistasis analysis for systems biology, and can complement genome-wide association studies as a means of formulating hypotheses for targeted experimental work., Author summary Epistatic interactions between polymorphisms in DNA are recognized as important drivers of evolution in numerous organisms. Study of epistasis in bacteria has been hampered by the lack of densely sampled population genomic data, suitable statistical models and inference algorithms sufficiently powered for extremely high-dimensional parameter spaces. We introduce the first model-based method for genome-wide epistasis analysis and use two of the largest available bacterial population genome data sets on Streptococcus pneumoniae (the pneumococcus) and Streptococcus pyogenes (group A Streptococcus) to demonstrate its potential for biological discovery. Our approach reveals interacting networks of resistance, virulence and core machinery genes in the pneumococcus, which highlights putative candidates for novel drug targets. We also discover a number of plausible targets of co-selection in S. pyogenes linked to RNA pseudouridine synthases. Our method significantly enhances the future potential of epistasis analysis for systems biology, and can complement genome-wide association studies as a means of formulating hypotheses for targeted experimental work.

Precise promoter annotation is required for understanding the mechanistic basis of transcription initiation. In the context of complex genomes, such as herpesviruses where there is extensive genic overlap, identification of transcription start sites (TSSs) is particularly problematic and cannot be comprehensively accessed by standard RNA sequencing approaches. Kaposi's sarcoma-associated herpesvirus (KSHV) is an oncogenic gammaherpesvirus and the etiological agent of Kaposi’s sarcoma and the B cell lymphoma primary effusion lymphoma (PEL). Here, we leverage RNA annotation and mapping of promoters for analysis of gene expression (RAMPAGE) and define KSHV TSSs transcriptome-wide and at nucleotide resolution in two widely used models of KSHV infection, namely iSLK.219 cells and the PEL cell line TREx-BCBL1-RTA. By mapping TSSs over a 96 h time course of reactivation we confirm 48 of 50 previously identified TSSs. Moreover, we identify over 100 novel transcription start site clusters (TSCs) in each cell line. Our analyses identified cell-type specific differences in TSC positions as well as promoter strength, and defined motifs within viral core promoters. Collectively, by defining TSSs at high resolution we have greatly expanded the transcriptional landscape of the KSHV genome and identified transcriptional control mechanisms at play during KSHV lytic reactivation., Author summary Kaposi's sarcoma-associated herpesvirus (KSHV) is an oncogenic gammaherpesvirus and the etiological agent of Kaposi’s sarcoma and the B cell lymphoma primary effusion lymphoma (PEL). Despite identification of the virus over 20 years ago there is still an incomplete understanding of how many RNAs are transcribed from the viral genome and the location these RNAs are derived from. To fill this gap in knowledge we determined the landscape of transcription initiation on the KSHV genome. Our analyses more than tripled the number of known TSCs and thus viral-expressed RNAs. Furthermore, we identified key sequence features associated with the regulation of viral transcription start sites. This study provides the first transcriptome-wide characterization of KSHV transcription initiation sites as well as a framework for future studies to define functions of novel viral transcripts and viral gene regulatory elements.

Trade-offs constrain the improvement of performance of multiple traits simultaneously. Such trade-offs define Pareto fronts, which represent a set of optimal individuals that cannot be improved in any one trait without reducing performance in another. Surprisingly, experimental evolution often yields genotypes with improved performance in all measured traits, perhaps indicating an absence of trade-offs at least in the short term. Here we densely sample adaptive mutations in Saccharomyces cerevisiae to ask whether first-step adaptive mutations result in trade-offs during the growth cycle. We isolated thousands of adaptive clones evolved under carefully chosen conditions and quantified their performances in each part of the growth cycle. We too find that some first-step adaptive mutations can improve all traits to a modest extent. However, our dense sampling allowed us to identify trade-offs and establish the existence of Pareto fronts between fermentation and respiration, and between respiration and stationary phases. Moreover, we establish that no single mutation in the ancestral genome can circumvent the detected trade-offs. Finally, we sequenced hundreds of these adaptive clones, revealing new targets of adaptation and defining the genetic basis of the identified trade-offs.

Restriction enzymes have been identified in the early 1950s of the past century and have quickly become key players in the molecular biology of DNA. Forty years ago, the scientists whose pioneering work had explored the activity and sequence specificity of these enzymes, contributing to the definition of their enormous potential as tools for DNA characterization, mapping and manipulation, were awarded the Nobel Prize. In this short review, we celebrate the history of these enzymes in the light of their many different uses, as these proteins have accompanied the history of DNA for over 50 years representing active witnesses of major steps in the field.

Tradeoffs constrain the improvement of performance of multiple traits simultaneously. Such tradeoffs define Pareto fronts, which represent a set of optimal individuals that cannot be improved in any one trait without reducing performance in another. Surprisingly, experimental evolution often yields genotypes with improved performance in all measured traits, perhaps indicating an absence of tradeoffs at least in the short-term. Here we densely sample adaptive mutations inS. cerevisiaeto ask whether first-step adaptive mutations result in tradeoffs during the growth cycle. We isolated thousands of adaptive clones evolved under carefully chosen conditions and quantified their performances in each part of the growth cycle. We too find that some first-step adaptive mutations can improve all traits to a modest extent. However, our dense sampling allowed us to identify tradeoffs and establish the existence of Pareto fronts between fermentation and respiration, and between respiration and stationary phases. Moreover, we establish that no single mutation in the ancestral genome can circumvent the detected tradeoffs. Finally, we sequenced hundreds of these adaptive clones, revealing novel targets of adaptation and defining the genetic basis of the identified tradeoffs.

Leishmaniasis and Chagas disease are endemic in many countries, and re-emerging in the developed countries. A rapid and accurate diagnosis is important for early treatment for reducing the duration of infection as well as for preventing further potential health complications. In this work, we have developed a novel colorimetric molecular assay that integrates nucleic acid analysis by dynamic chemistry (ChemNAT) with reverse dot-blot hybridization in an array format for a rapid and easy discrimination of Leishmania major and Trypanosoma cruzi. The assay consists of a singleplex PCR step that amplifies a highly homologous DNA sequence which encodes for the RNA component of the large ribosome subunit. The amplicons of the two different parasites differ between them by single nucleotide variations, known as “Single Nucleotide Fingerprint” (SNF) markers. The SNF markers can be easily identified by naked eye using a novel micro Spin-Tube device "Spin-Tube", as each of them creates a specific spot pattern. Moreover, the direct use of ribosomal RNA without requiring the PCR pre-amplification step is also feasible, further increasing the simplicity of the assay. The molecular assay delivers sensitivity capable of identifying up to 8.7 copies per μL with single mismatch specificity. The Spin-Tube thus represents an innovative solution providing benefits in terms of time, cost, and simplicity, all of which are crucial for the diagnosis of infectious disease in developing countries., This research work has received funding from Junta de Andalucía, Consejería de Economía e Innovación (project number 2012-BIO1778), the Spanish Ministerio de Economía y Competitividad (Grants CTQ2012-34778, BIO2016-80519-R, FPI Grant BES-2013- 063020). This research was partially supported by the 7th European Community Framework Program (FP7-PEOPLE-2012-CIG-Project Number 322276).

Mucormycosis—an emergent, deadly fungal infection—is difficult to treat, in part because the causative species demonstrate broad clinical antifungal resistance. However, the mechanisms underlying drug resistance in these infections remain poorly understood. Our previous work demonstrated that one major agent of mucormycosis, Mucor circinelloides, can develop resistance to the antifungal agents FK506 and rapamycin through a novel, transient RNA interference-dependent mechanism known as epimutation. Epimutations silence the drug target gene and are selected by drug exposure; the target gene is re-expressed and sensitivity is restored following passage without drug. This silencing process involves generation of small RNA (sRNA) against the target gene via core RNAi pathway proteins. To further elucidate the role of epimutation in the broad antifungal resistance of Mucor, epimutants were isolated that confer resistance to another antifungal agent, 5-fluoroorotic acid (5-FOA). We identified epimutant strains that exhibit resistance to 5-FOA without mutations in PyrF or PyrG, enzymes which convert 5-FOA into the active toxic form. Using sRNA hybridization as well as sRNA library analysis, we demonstrate that these epimutants harbor sRNA against either pyrF or pyrG, and further show that this sRNA is lost after reversion to drug sensitivity. We conclude that epimutation is a mechanism capable of targeting multiple genes, enabling Mucor to develop resistance to a variety of antifungal agents. Elucidation of the role of RNAi in epimutation affords a fuller understanding of mucormycosis. Furthermore, it improves our understanding of fungal pathogenesis and adaptation to stresses, including the evolution of drug resistance., Author summary The emerging infection mucormycosis causes high mortality in part because the major causative fungi, including Mucor circinelloides, are resistant to most clinically available antifungal drugs. We previously discovered an RNA interference-based resistance mechanism, epimutation, through which M. circinelloides develops transient resistance to the antifungal agent FK506 by altering endogenous RNA expression. We further characterize this novel mechanism by isolating epimutations in two genes that confer resistance to another antifungal agent, 5-fluoroorotic acid. Thus, we demonstrate epimutation can induce resistance to multiple antifungals by targeting a variety of genes. These results reveal epimutation plays a broad role enabling rapid and reversible fungal responses to environmental stresses, including drug exposure, and controlling antifungal drug resistance and RNA expression. As resistance to antifungals emerges, a deeper understanding of the causative mechanisms is crucial for improving treatment.

The development of procedures for the efficient removal or inactivation of noroviruses from contaminated oysters is of great interest in oyster production. However, there is a critical limitation for evaluating the depuration efficacy of presently available procedures, as no suitable cell culture system currently exists to cultivate noroviruses. Thus, we applied a next-generation sequencing (NGS) technique to characterize norovirus genotypes in pre- and post-depurated oysters. As a result, we revealed the diversity of noroviruses in pre- and post-depurated oysters. Although the applied depuration procedure could reduce the number of bacterial agents to the level recommended by the Japanese Ministry of Health, Labour and Welfare, no significant changes were observed in the detection rate and the proportion of norovirus group (G) I and GII genotypes. To our knowledge, this is the first report to evaluate the profile of noroviruses in pre- and post-depurated oysters, specifically with respect to norovirus removal, using NGS; the findings imply that the removal of noroviruses from oysters through depuration is not presently sufficient. Further studies are needed to develop a more suitable depuration procedure for removing and/or inactivating noroviruses from contaminated oysters.

Pre-mRNA splicing is critical for achieving required amounts of a transcript at a given time and for regulating production of encoded protein. A given pre-mRNA may be spliced in many ways, or not at all, giving rise to multiple gene products. Numerous splicing factors are recruited to pre-mRNA splice sites to ensure proper splicing. One such factor, the 60 kDa poly(U)-binding splicing factor (PUF60), is recruited to sites that are not always spliced, but rather function as alternative splice sites. In this study, we characterized the interaction of PUF60 with a splice site from the adenovirus major late promoter (the AdML 3' splice site, AdML3’). We found that the PUF60–AdML3’ dissociation constants are in the micromolar range, with the binding affinity predominantly provided by PUF60’s two central RNA recognition motifs (RRMs). A 1.95 Å crystal structure of the two PUF60 RRMs in complex with AdML3’ revealed a dimeric organization placing two stretches of nucleic acid tracts in opposing directionalities, which can cause looping of nucleic acid and explain how PUF60 affects pre-mRNA geometry to effect splicing. Solution characterization of this complex by light-scattering and UV/Vis spectroscopy suggested a potential 2:1 (PUF602:AdML3’) stoichiometry, consistent with the crystal structure. This work defines the sequence specificity of the alternative splicing factor PUF60 at the pre-mRNA 3’ splice site. Our observations suggest that control of pre-mRNA directionality is important in the early stage of spliceosome assembly, and advance our understanding of the molecular mechanism by which alternative and constitutive splicing factors differentiate among 3’ splice sites.

Functional non-coding (fnc)RNAs are nucleotide sequences of varied lengths, structures, and mechanisms that ubiquitously influence gene expression and translation, genome stability and dynamics, and human health and disease. Here, to shed light on their functional determinants, we seek to exploit the evolutionary record of variation and divergence read from sequence comparisons. The approach follows the phylogenetic Evolutionary Trace (ET) paradigm, first developed and extensively validated on proteins. We assigned a relative rank of importance to every base in a study of 1070 functional RNAs, including the ribosome, and observed evolutionary patterns strikingly similar to those seen in proteins, namely, (1) the top-ranked bases clustered in secondary and tertiary structures. (2) In turn, these clusters mapped functional regions for catalysis, binding proteins and drugs, post-transcriptional modification, and deleterious mutations. (3) Moreover, the quantitative quality of these clusters correlated with the identification of functional regions. (4) As a result of this correlation, smoother structural distributions of evolutionary important nucleotides improved functional site predictions. Thus, in practice, phylogenetic analysis can broadly identify functional determinants in RNA sequences and functional sites in RNA structures, and reveal details on the basis of RNA molecular functions. As example of application, we report several previously undocumented and potentially functional ET nucleotide clusters in the ribosome. This work is broadly relevant to studies of structure-function in ribonucleic acids. Additionally, this generalization of ET shows that evolutionary constraints among sequence, structure, and function are similar in structured RNA and proteins. RNA ET is currently available as part of the ET command-line package, and will be available as a web-server., Author summary Traditionally, RNA has been delegated to the role of an intermediate between DNA and proteins. However, we now recognize that RNAs are broadly functional beyond their role in translation, and that a number of diverse classes exist. Because functional, non-coding RNAs are prevalent in biology and impact human health, it is important to better understand their functional determinants. However, the classical solution to this problem, targeted mutagenesis, is time-consuming and scales poorly. We propose an alternative computational approach to this problem, the Evolutionary Trace method. Previously developed and validated for proteins, Evolutionary Trace examines evolutionary history of a molecule and predicts evolutionarily important residues in the sequence. We apply Evolutionary Trace to a set of diverse RNAs, and find that the evolutionarily important nucleotides cluster on the three-dimensional structure, and that these clusters closely overlap functional sites. We also find that the clustering property can be used to refine and improve predictions. These findings are in close agreement with our observations of Evolutionary Trace in proteins, and suggest that structured functional RNAs and proteins evolve under similar constraints. In practice, the approach is to be used by RNA researches seeking insight into their molecule of interest, and the Evolutionary Trace program, along with a working example, is available at https://github.com/LichtargeLab/RNA_ET_ms.

Legionella spp. are the cause of a severe bacterial pneumonia known as Legionnaires' disease (LD). In some cases, current genetic subtyping methods cannot resolve LD outbreaks caused by common, potentially endemic L. pneumophila (Lp) sequence types (ST), which complicates laboratory investigations and environmental source attribution. In the United States (US), ST1 is the most prevalent clinical and environmental Lp sequence type. In order to characterize the ST1 population, we sequenced 289 outbreak and non-outbreak associated clinical and environmental ST1 and ST1-variant Lp strains from the US and, together with international isolate sequences, explored their genetic and geographic diversity. The ST1 population was highly conserved at the nucleotide level; 98% of core nucleotide positions were invariant and environmental isolates unassociated with human disease (n = 99) contained ~65% more nucleotide diversity compared to clinical-sporadic (n = 139) or outbreak-associated (n = 28) ST1 subgroups. The accessory pangenome of environmental isolates was also ~30-60% larger than other subgroups and was enriched for transposition and conjugative transfer-associated elements. Up to ~10% of US ST1 genetic variation could be explained by geographic origin, but considerable genetic conservation existed among strains isolated from geographically distant states and from different decades. These findings provide new insight into the ST1 population structure and establish a foundation for interpreting genetic relationships among ST1 strains; these data may also inform future analyses for improved outbreak investigations.

Crown rust disease caused by the fungus Puccinia coronata f. sp. avenae (Pca) is a major production constraint of oat in North America, Europe, and Australia. There are over 100 genes effective against one or more Pca races, but only a handful of seedling resistance (Pc) genes have been mapped to a known chromosomal location. The goal of the present study was to use linkage mapping to identify the genomic location of the Pc53 gene, and to produce a list of linked SNPs with potential as molecular markers for marker assisted breeding. The Pc53 gene was placed on the linkage group Mrg08 at 82.4 cM using F5-derived recombinant inbred lines (RILs) from a cross between the Pc53 carrier 6-112-1-15 (PI 311624) and the susceptible cultivar Otana. The map location was validated using RILs from a cross between 6-112-1-15 and the Pc50 differential line. Single nucleotide polymorphism marker GMI_ES02_c14533_567 was the closest to Pc53. A major seedling resistance gene 'PcKM' and QTL QcC.Core.08.1, QCr.Core.08.2, QCr.Core.08.3 and QCr.cdl9-12D were previously reported on Mrg08. QPc.Core.08.1 and PcKM were mapped to within 1 cM of Pc53; but previous virulence studies have indicated separate identities. The chromosomal location of Pc53 and SNPs linked with it will facilitate the utilization of Pc53 in oat breeding programs.

Protein structural vibrations impact biology by steering the structure to functional intermediate states; enhancing tunneling events; and optimizing energy transfer. Strong water absorption and a broad continuous vibrational density of states have prevented optical identification of these vibrations. Recently spectroscopic signatures that change with functional state were measured using anisotropic terahertz microscopy. The technique however has complex sample positioning requirements and long measurement times, limiting access for the biomolecular community. Here we demonstrate that a simplified system increases spectroscopic structure to dynamically fingerprint biomacromolecules with a factor of 6 reduction in data acquisition time. Using this technique, polarization varying anisotropy terahertz microscopy, we show sensitivity to inhibitor binding and unique vibrational spectra for several proteins and an RNA G-quadruplex. The technique’s sensitivity to anisotropic absorbance and birefringence provides rapid assessment of macromolecular dynamics that impact biology.

Proinsulin is a misfolding-prone protein, making its biosynthesis in the endoplasmic reticulum (ER) a stressful event. Pancreatic β-cells overcome ER stress by activating the unfolded protein response (UPR) and reducing insulin production. This suggests that β-cells transition between periods of high insulin biosynthesis and UPR-mediated recovery from cellular stress. We now report the pseudotime ordering of single β-cells from humans without diabetes detected by large-scale RNA sequencing. We identified major states with 1) low UPR and low insulin gene expression, 2) low UPR and high insulin gene expression, or 3) high UPR and low insulin gene expression. The latter state was enriched for proliferating cells. Stressed human β-cells do not dedifferentiate and show little propensity for apoptosis. These data suggest that human β-cells transition between states with high rates of biosynthesis to fulfill the body’s insulin requirements to maintain normal blood glucose levels and UPR-mediated recovery from ER stress due to high insulin production.

A plasmid pCY-CTX carrying a phage-like backbone from an extensively drug-resistant Enterobacter cloacae strain Guangzhou-ECL001 (previously known as CY01) was identified in this study. By Illumina MiSeq 2 × 250-bp paired-end sequencing, de novo assembly, and PCR, full sequence of pCY-CTX was obtained. Plasmid pCY-CTX was a circular plasmid with a length of 116,700 bp, harboring 136 putative open reading frames with the average G + C content of 50.8%. The backbone of pCY-CTX showed high identity to previously reported phage-like plasmid pHCM2 and phage SSU5. In addition, pCY-CTX contained a distinctive ISEcp1-mediated Tn2 region with two resistance genes blaTEM-1 and blaCTX-M-3. Transposition unit “ISEcp1- blaCTX-M-3- orf477” was inserted into the Tn2 structure, dividing Tn2 into two parts. This represents the first identification of a plasmid carrying a phage-like backbone and a distinctive ISEcp1-mediated Tn2 region within blaTEM-1 and blaCTX-M-3 in clinical E. cloacae. The finding of phage-like regions...

Practical application of enzymatic nucleic acids has received more attention in recent years. Understanding the mechanism of catalysis and availability of information on potentials and limitations of these enzymes expands their application scope. A general approach for characterization of functional macromolecules including enzymatic nucleic acids is to perturb a specific set of condition and to follow the perturbation effect by biophysical and biochemical methods. This chapter reviews several perturbation strategies for functional nucleic acids, including deletion, mutation, and modifications of backbone and nucleobases, and consequent kinetic analysis, spectroscopic investigations, and probing assays. In addition to single point mutation and modifications, different combinatorial approaches for perturbation interference analysis provide reliable high amounts of data in a time-effective manner. The chapter compares various combinatorial perturbation interference analysis methods, that is, combinatorial mutation interference analysis (CoMA), nucleotide analogue interference mapping for RNA and DNA (NAIM and dNAIM), chemical and enzymatic combinatorial nucleoside deletion scanning (NDS), and dimethyl sulfate interference (DMSi).