Random Nucleotide Sequences Research Articles

Selection of a de novo gene that can promote survival of Escherichiacoli by modulating protein homeostasis pathways

Cellular novelty can emerge when non-functional loci become functional genes in a process termed de novo gene birth. But how proteins with random amino acid sequences beneficially integrate into existing cellular pathways remains poorly understood. We screened ~108 genes, generated from random nucleotide sequences and devoid of homology to natural genes, for their ability to rescue growth arrest of Escherichia coli cells producing the ribonuclease toxin MazF. We identified ~2,000 genes that could promote growth, probably by reducing transcription from the promoter driving toxin expression. Additionally, one random protein, named Random antitoxin of MazF (RamF), modulated protein homeostasis by interacting with chaperones, leading to MazF proteolysis and a consequent loss of its toxicity. Finally, we demonstrate that random proteins can improve during evolution by identifying beneficial mutations that turned RamF into a more efficient inhibitor. Our work provides a mechanistic basis for how de novo gene birth can produce functional proteins that effectively benefit cells evolving under stress.

Proving sequence aligners can guarantee accuracy in almost O(m log n) time through an average-case analysis of the seed-chain-extend heuristic.

Seed-chain-extend with k-mer seeds is a powerful heuristic technique for sequence alignment used by modern sequence aligners. Although effective in practice for both runtime and accuracy, theoretical guarantees on the resulting alignment do not exist for seed-chain-extend. In this work, we give the first rigorous bounds for the efficacy of seed-chain-extend with k-mers in expectation Assume we are given a random nucleotide sequence of length ∼n that is indexed (or seeded) and a mutated substring of length ∼m ≤ n with mutation rate θ < 0.206. We prove that we can find a k = Θ(log n) for the k-mer size such that the expected runtime of seed-chain-extend under optimal linear-gap cost chaining and quadratic time gap extension is O(mn f (θ) log n), where f(θ) < 2.43 · θ holds as a loose bound. The alignment also turns out to be good; we prove that more than [Formula: see text] fraction of the homologous bases is recoverable under an optimal chain. We also show that our bounds work when k-mers are sketched, that is, only a subset of all k-mers is selected, and that sketching reduces chaining time without increasing alignment time or decreasing accuracy too much, justifying the effectiveness of sketching as a practical speedup in sequence alignment. We verify our results in simulation and on real noisy long-read data and show that our theoretical runtimes can predict real runtimes accurately. We conjecture that our bounds can be improved further, and in particular, f(θ) can be further reduced.

Applying Unique Molecular Indices with an Extensive All-in-One Forensic SNP Panel for Improved Genotype Accuracy and Sensitivity

One of the major challenges in forensic genetics is being able to detect very small amounts of DNA. Massively parallel sequencing (MPS) enables sensitive detection; however, genotype errors may exist and could interfere with the interpretation. Common errors in MPS-based analysis are often induced during PCR or sequencing. Unique molecular indices (UMIs) are short random nucleotide sequences ligated to each template molecule prior to amplification. Applying UMIs can improve the limit of detection by enabling accurate counting of initial template molecules and removal of erroneous data. In this study, we applied the FORCE panel, which includes ~5500 SNPs, with a QIAseq Targeted DNA Custom Panel (Qiagen), including UMIs. Our main objective was to investigate whether UMIs can enhance the sensitivity and accuracy of forensic genotyping and to evaluate the overall assay performance. We analyzed the data both with and without the UMI information, and the results showed that both genotype accuracy and sensitivity were improved when applying UMIs. The results showed very high genotype accuracies (>99%) for both reference DNA and challenging samples, down to 125 pg. To conclude, we show successful assay performance for several forensic applications and improvements in forensic genotyping when applying UMIs.

Shepherd: accurate clustering for correcting DNA barcode errors.

MotivationDNA barcodes are short, random nucleotide sequences introduced into cell populations to track the relative counts of hundreds of thousands of individual lineages over time. Lineage tracking is widely applied, e.g. to understand evolutionary dynamics in microbial populations and the progression of breast cancer in humans. Barcode sequences are unknown upon insertion and must be identified using next-generation sequencing technology, which is error prone. In this study, we frame the barcode error correction task as a clustering problem with the aim to identify true barcode sequences from noisy sequencing data. We present Shepherd, a novel clustering method that is based on an indexing system of barcode sequences using k-mers, and a Bayesian statistical test incorporating a substitution error rate to distinguish true from error sequences.ResultsWhen benchmarking with synthetic data, Shepherd provides barcode count estimates that are significantly more accurate than state-of-the-art methods, producing 10–150 times fewer spurious lineages. For empirical data, Shepherd produces results that are consistent with the improvements seen on synthetic data. These improvements enable higher resolution lineage tracking and more accurate estimates of biologically relevant quantities, e.g. the detection of small effect mutations.Availability and implementationA Python implementation of Shepherd is freely available at: https://www.github.com/Nik-Tavakolian/Shepherd.Supplementary information Supplementary data are available at Bioinformatics online.

Effects of the Expression of Random Sequence Clones on Growth and Transcriptome Regulation in Escherichia coli.

Comparative genomic analyses have provided evidence that new genetic functions can emerge out of random nucleotide sequences. Here, we apply a direct experimental approach to study the effects of plasmids harboring random sequence inserts under the control of an inducible promoter. Based on data from previously described experiments dealing with the growth of clones within whole libraries, we extracted specific clones that had shown either negative, neutral or positive effects on relative cell growth. We analyzed these individually with respect to growth characteristics and the impact on the transcriptome. We find that candidate clones for negative peptides lead to growth arrest by eliciting a general stress response. Overexpression of positive clones, on the other hand, does not change the exponential growth rates of hosts, and they show a growth advantage over a neutral clone when tested in direct competition experiments. Transcriptomic changes in positive clones are relatively moderate and specific to each clone. We conclude from our experiments that random sequence peptides are indeed a suitable source for the de novo evolution of genetic functions.

The Effects of Sequence Length and Composition of Random Sequence Peptides on the Growth of E. coli Cells.

We study the potential for the de novo evolution of genes from random nucleotide sequences using libraries of E. coli expressing random sequence peptides. We assess the effects of such peptides on cell growth by monitoring frequency changes in individual clones in a complex library through four serial passages. Using a new analysis pipeline that allows the tracing of peptides of all lengths, we find that over half of the peptides have consistent effects on cell growth. Across nine different experiments, around 16% of clones increase in frequency and 36% decrease, with some variation between individual experiments. Shorter peptides (8–20 residues), are more likely to increase in frequency, longer ones are more likely to decrease. GC content, amino acid composition, intrinsic disorder, and aggregation propensity show slightly different patterns between peptide groups. Sequences that increase in frequency tend to be more disordered with lower aggregation propensity. This coincides with the observation that young genes with more disordered structures are better tolerated in genomes. Our data indicate that random sequences can be a source of evolutionary innovation, since a large fraction of them are well tolerated by the cells or can provide a growth advantage.

Improving high-resolution copy number variation analysis from next generation sequencing using unique molecular identifiers

BackgroundRecently, copy number variations (CNV) impacting genes involved in oncogenic pathways have attracted an increasing attention to manage disease susceptibility. CNV is one of the most important somatic aberrations in the genome of tumor cells. Oncogene activation and tumor suppressor gene inactivation are often attributed to copy number gain/amplification or deletion, respectively, in many cancer types and stages. Recent advances in next generation sequencing protocols allow for the addition of unique molecular identifiers (UMI) to each read. Each targeted DNA fragment is labeled with a unique random nucleotide sequence added to sequencing primers. UMI are especially useful for CNV detection by making each DNA molecule in a population of reads distinct.ResultsHere, we present molecular Copy Number Alteration (mCNA), a new methodology allowing the detection of copy number changes using UMI. The algorithm is composed of four main steps: the construction of UMI count matrices, the use of control samples to construct a pseudo-reference, the computation of log-ratios, the segmentation and finally the statistical inference of abnormal segmented breaks. We demonstrate the success of mCNA on a dataset of patients suffering from Diffuse Large B-cell Lymphoma and we highlight that mCNA results have a strong correlation with comparative genomic hybridization.ConclusionWe provide mCNA, a new approach for CNV detection, freely available at https://gitlab.com/pierrejulien.viailly/mcna/ under MIT license. mCNA can significantly improve detection accuracy of CNV changes by using UMI.

Predicting Antimicrobial and Other Cysteine-Rich Peptides in 1267 Plant Transcriptomes.

Antimicrobial peptides (AMPs) are a key component of innate immunity in various organisms including bacteria, insects, mammals, and plants. Their mode of action decreases the probability of developing resistance in pathogenic organisms, which makes them a promising object of study. However, molecular biology methods for searching for AMPs are laborious and expensive, especially for plants. Earlier, we developed a computational pipeline for identifying potential AMPs based on the cysteine motifs they usually possess. Since most motifs are too species-specific, a wide-scale screening of novel data is required to maintain the accuracy of searching algorithms. We have performed a search for potential AMPs in 1267 plant transcriptomes using our pipeline. On average, 50–150 peptides were revealed in each transcriptome. The data was verified by a BLASTp search in nr database to confirm peptide functions and by using random nucleotide sequences to estimate the fraction of erroneous predictions. The datasets obtained will be useful both for molecular biologists investigating AMPs in various organisms and for bioinformaticians developing novel algorithms of motif searching in transcriptomic and genomic sequences. The results obtained will represent a good reference point for future investigations in the fields mentioned above.

Enzymatic Preparation of Modified DNA: Study of the Kinetics by Real-Time PCR

The effects of modified deoxyuridine triphosphates (mod-dUTPs) with different substituents at the C5 position of the pyrimidine cycle on the kinetics of PCR with Taq and Vent (exo-) DNA polymerases are studied. Substituents in mod-dUTP include carboxamide group and groups that are part of the side chains of alanine, valine, leucine, phenylalanine, tryptophan, or tyrosine. For each mod-dUTP, the yields of the target product are measured with the full substitution of dTTP. A fragment of bacterial DNA with a certain nucleotide sequence and a synthetic combinatorial DNA library of random nucleotide sequences are used as templates for amplification. For each mod-dUTP-template-polymerase combination, the correlation between the amplification efficiencies and yields of the target product are investigated. PCR product accumulation curves are influenced by both the template used and the presence of a modified substrate. The catalytic activity of Taq polymerase is higher when mod-dUTPs with short aliphatic substituents are used and decreases when the derivatives with long aliphatic, phenyl, and indole substituents are utilized. Vent (exo-) polymerase is less sensitive to the chemical structure of mod-dUTP. The dynamic measuring of DNA accumulation may be useful for optimizing the temperature-time PCR profiles individually for each of the mod-dUTP. The derivatives may be used in combination with Vent (exo-) polymerase to obtain modified DNA sequences for the method of selection of modified aptamers (mod-SELEX).

Local Symmetry of Non-Coding Genetic Sequences

The simplest hypothesis of DNA strand symmetry states that proportions of nucleotides of the same base pair are approximately equal within single DNA strands. Results of extensive empirical studies using asymmetry measures and various visualization tools show that for long DNA sequences (approximate) strand symmetry generally holds with rather rare exceptions. In the paper, a formal definition of DNA strand local symmetry is presented, characterized in terms of generalized logits and tested for the longest non-coding sequences of bacterial genomes. Validity of a special regression-type probabilistic structure of the data is supposed. This structure is compatible with probability distribution of random nucleotide sequences at a steady state of a context-dependent reversible Markov evolutionary process. The null hypothesis of strand local symmetry is rejected in majority of bacterial genomes suggesting that even neutral mutations are skewed with respect to leading and lagging strands.

CG dinucleotide suppression enables antiviral defence targeting non-self RNA.

Vertebrate genomes exhibit marked CG-suppression, that is lower than expected numbers of 5′-CG-3′ dinucleotides1. This feature is likely due to C-to-T mutations that have accumulated over hundreds of millions of years, driven by CG-specific DNA methyl transferases and spontaneous methyl-cytosine deamination. Remarkably, many RNA viruses of vertebrates that are not substrates for DNA methyl transferases mimic the CG-suppression of their hosts2–4. This striking property of viral genomes is unexplained4–6. In a synonymous mutagenesis experiment, we found that CG-suppression is essential for HIV-1 replication. The deleterious effect of CG dinucleotides on HIV-1 replication was cumulative, evident as cytoplasmic RNA depletion, and exerted by CG dinucleotides in both translated and non-translated exonic RNA sequences. A focused siRNA screen revealed that zinc finger antiviral protein (ZAP)7 inhibited virion production by cells infected with CG-enriched HIV-1. Crucially, HIV-1 mutants containing segments whose CG-content mimicked random sequence were defective in unmanipulated cells, but replicated normally in ZAP-deficient cells. Crosslinking-immunoprecipitation-sequencing assays demonstrated that ZAP binds directly and selectively to RNA sequences containing CG dinucleotides. These findings suggest that ZAP exploits host CG-suppression to discriminate non-self RNA. The dinucleotide composition of HIV-1, and perhaps other RNA viruses, appears to have adapted to evade this host defense.

NullSeq: A Tool for Generating Random Coding Sequences with Desired Amino Acid and GC Contents.

The existence of over- and under-represented sequence motifs in genomes provides evidence of selective evolutionary pressures on biological mechanisms such as transcription, translation, ligand-substrate binding, and host immunity. In order to accurately identify motifs and other genome-scale patterns of interest, it is essential to be able to generate accurate null models that are appropriate for the sequences under study. While many tools have been developed to create random nucleotide sequences, protein coding sequences are subject to a unique set of constraints that complicates the process of generating appropriate null models. There are currently no tools available that allow users to create random coding sequences with specified amino acid composition and GC content for the purpose of hypothesis testing. Using the principle of maximum entropy, we developed a method that generates unbiased random sequences with pre-specified amino acid and GC content, which we have developed into a python package. Our method is the simplest way to obtain maximally unbiased random sequences that are subject to GC usage and primary amino acid sequence constraints. Furthermore, this approach can easily be expanded to create unbiased random sequences that incorporate more complicated constraints such as individual nucleotide usage or even di-nucleotide frequencies. The ability to generate correctly specified null models will allow researchers to accurately identify sequence motifs which will lead to a better understanding of biological processes as well as more effective engineering of biological systems.

Synthetic approach to the generation of antibody diversity

The in vitro antibody discovery technologies revolutionized the generation of target-specific antibodies that traditionally relied on the humoral response of immunized animals. An antibody library, a large collection of diverse, pre-constructed antibodies, can be rapidly screened using in vitro display technologies such as phage display. One of the keys to successful in vitro antibody discovery is the quality of the library diversity. Antibody diversity can be obtained either from natural B-cell sources or by the synthetic methods that combinatorially generate random nucleotide sequences. While the functionality of a natural antibody library depends largely upon the library size, various other factors can affect the quality of a synthetic antibody library, making the design and construction of synthetic antibody libraries complicated and challenging. In this review, we present various library designs and diversification methods for synthetic antibody library. From simple degenerate oligonucleotide synthesis to trinucleotide synthesis to physicochemically optimized library design, the synthetic approach is evolving beyond the simple emulation of natural antibodies, into a highly sophisticated method that is capable of producing high quality antibodies suitable for therapeutic, diagnostic, and other demanding applications. [BMB Reports 2015; 48(9): 489-494]

Nucleic acid-based aptamers: applications, development and clinical trials.

Short single-stranded oligonucleotides called aptamers, often termed as chemical antibodies, have been developed as powerful alternatives to traditional antibodies with respect to their obvious advantages like high specificity and affinity, longer shelf-life, easier manufacturing protocol, freedom to introduce chemical modifications for further improvement, etc. Reiterative selection process of aptamers over 10-15 cycles starting from a large initial pool of random nucleotide sequences renders them with high binding affinity, thereby making them extremely specific for their targets. Aptamer-based detection systems are well investigated and likely to displace primitive detection systems. Aptamer chimeras (combination of aptamers with another aptamer or biomacromolecule or chemical moiety) have the potential activity of both the parent molecules, and thus hold the capability to perform diverse functions at the same time. Owing to their extremely high specificity and lack of immunogenicity or pathogenicity, a number of other aptamers have recently entered clinical trials and have garnered favorable attention from pharmaceutical companies. Promising results from the clinical trials provide new hope to change the conventional style of therapy. Aptamers have attained high therapeutic relevance in a short time as compared to synthetic drugs and/or other modes of therapy. This review follows the various trends in aptamer technology including production, selection, modifications and success in clinical fields. It focusses largely on the various applications of aptamers which mainly depend upon their selection procedures. The review also sheds light on various modifications and chimerizations that have been implemented in order to improve the stability and functioning of the aptamers, including introduction of locked nucleic acids (LNAs). The application of various aptamers in detection systems has been discussed elaborately in order to stress on their role as efficient diagnostic agents. The key aspect of this review is focused on success of aptamers on the basis of their performance in clinical trials for various diseases.

Computational intelligence-based polymerase chain reaction primer selection based on a novel teaching-learning-based optimisation.

Specific primers play an important role in polymerase chain reaction (PCR) experiments, and therefore it is essential to find specific primers of outstanding quality. Unfortunately, many PCR constraints must be simultaneously inspected which makes specific primer selection difficult and time-consuming. This paper introduces a novel computational intelligence-based method, Teaching-Learning-Based Optimisation, to select the specific and feasible primers. The specified PCR product lengths of 150-300 bp and 500-800 bp with three melting temperature formulae of Wallace's formula, Bolton and McCarthy's formula and SantaLucia's formula were performed. The authors calculate optimal frequency to estimate the quality of primer selection based on a total of 500 runs for 50 random nucleotide sequences of 'Homo species' retrieved from the National Center for Biotechnology Information. The method was then fairly compared with the genetic algorithm (GA) and memetic algorithm (MA) for primer selection in the literature. The results show that the method easily found suitable primers corresponding with the setting primer constraints and had preferable performance than the GA and the MA. Furthermore, the method was also compared with the common method Primer3 according to their method type, primers presentation, parameters setting, speed and memory usage. In conclusion, it is an interesting primer selection method and a valuable tool for automatic high-throughput analysis. In the future, the usage of the primers in the wet lab needs to be validated carefully to increase the reliability of the method.

Estimation of teaching-learning-based optimization primer design using regression analysis for different melting temperature calculations.

Primers plays important role in polymerase chain reaction (PCR) experiments, thus it is necessary to select characteristic primers. Unfortunately, manual primer design manners are time-consuming and easy to get human negligence because many PCR constraints must be considered simultaneously. Automatic programs for primer design were developed urgently. In this study, the teaching-learning-based optimization (TLBO), a robust and free of algorithm-specific parameters method, is applied to screen primers conformed primer constraints. The optimal primer frequency (OPF) based on three known melting temperature formulas is estimated by 500 runs for primer design in each different number of generations. We selected optimal primers from fifty random nucleotide sequences of Homo sapiens at NCBI. The results indicate that the SantaLucia's formula is better coupled with the method to get higher optimal primer frequency and shorter CPU-time than the Wallace's formula and the Bolton and McCarthy's formula. Through the regression analysis, we also find the generations are significantly associated with the optimal primer frequency. The results are helpful for developing the novel TLBO-based computational method to design feasible primers.

SOME FEATURES OF HYDROLYSIS OF THE HYBRID B-Z-FORM DNA BY &lt;i&gt;SERRATIA MARCESCENS&lt;/i&gt; NUCLEASE

Highly polymerized herring testis DNA of the random nucleotide sequence was used as a model of natural substrate to study some features of hydroly sis of the hybrid B-Z form with Serratia marcescens nuclease. The hybrid B-Z form was formed upon addition of 1.15 M MgSO 4 and 0.421 mM Co(NH 3)6Cl 3. The DNA transition from the right handed B-form t o the hybrid B-Z-form caused a decrease in Vmax of DNA cleavage with the nuclease. The diminishing Vmax was consistent with diminishing values of Km and Kcat. The binding of Mg 2+ or Co(NH 3)6 3+ to highly polymerized DNA caused correspondingly about 80-or 7-fold decrease in Km and more than 1600 or 600 decrease in Kcat compared with that of Mg-DNA complex of B-form.

An Exploration of the Triplet Periodicity in Nucleotide Sequences with a Mature Self-Adaptive Spectral Rotation Approach

Previously, for predicting coding regions in nucleotide sequences, a self-adaptive spectral rotation (SASR) method has been developed, based on a universal statistical feature of the coding regions, named triplet periodicity (TP). It outputs a random walk, that is, TP walk, in the complex plane for the query sequence. Each step in the walk is corresponding to a position in the sequence and generated from a long-term statistic of the TP in the sequence. The coding regions (TP intensive) are then visually discriminated from the noncoding ones (without TP), in the TP walk. In this paper, the behaviors of the walks for random nucleotide sequences are further investigated qualitatively. A slightly leftward trend (a negative noise) in such walks is observed, which is not reported in the previous SASR literatures. An improved SASR, named the mature SASR, is proposed, in order to eliminate the noise and correct the TP walks. Furthermore, a potential sequence pattern opposite to the TP persistent pattern, that is, the TP antipersistent pattern, is explored. The applications of the algorithms on simulated datasets show their capabilities in detecting such a potential sequence pattern.

Synthetic Promoters Functional in Francisella novicida and Escherichia coli

In this work, we describe the identification of synthetic, controllable promoters that function in the bacterial pathogen Francisella novicida, a model facultative intracellular pathogen. Synthetic DNA fragments consisting of the tetracycline operator (tetO) flanked by a random nucleotide sequence were inserted into a Francisella novicida shuttle plasmid upstream of a promoterless artificial operon containing the reporter genes cat and lacZ. Fragments able to promote transcription were selected for based on their ability to drive expression of the cat gene, conferring chloramphenicol resistance. Promoters of various strengths were found, many of which were repressed in the presence of the tetracycline repressor (TetR) and promoted transcription only in the presence of the TetR inducer anhydrotetracycline. A subset of both constitutive and inducible synthetic promoters were characterized to find their induction ratios and to identify their transcription start sites. In cases where tetO was located between or downstream of the -10 and -35 regions of the promoter, control by TetR was observed. If the tetO region was upstream of the -35 region by more than 9 bp, it did not confer TetR control. We found that three of three promoters isolated in F. novicida functioned at a comparable level in E. coli; however, none of the 10 promoters isolated in E. coli functioned at a significant level in F. novicida. Our results allowed us to isolate minimal F. novicida promoters of 47 and 48 bp in length.

An Experimental and Computational Evolution-Based Method to Study a Mode of Co-evolution of Overlapping Open Reading Frames in the AAV2 Viral Genome

Overlapping open reading frames (ORFs) in viral genomes undergo co-evolution; however, how individual amino acids coded by overlapping ORFs are structurally, functionally, and co-evolutionarily constrained remains difficult to address by conventional homologous sequence alignment approaches. We report here a new experimental and computational evolution-based methodology to address this question and report its preliminary application to elucidating a mode of co-evolution of the frame-shifted overlapping ORFs in the adeno-associated virus (AAV) serotype 2 viral genome. These ORFs encode both capsid VP protein and non-structural assembly-activating protein (AAP). To show proof of principle of the new method, we focused on the evolutionarily conserved QVKEVTQ and KSKRSRR motifs, a pair of overlapping heptapeptides in VP and AAP, respectively. In the new method, we first identified a large number of capsid-forming VP3 mutants and functionally competent AAP mutants of these motifs from mutant libraries by experimental directed evolution under no co-evolutionary constraints. We used Illumina sequencing to obtain a large dataset and then statistically assessed the viability of VP and AAP heptapeptide mutants. The obtained heptapeptide information was then integrated into an evolutionary algorithm, with which VP and AAP were co-evolved from random or native nucleotide sequences in silico. As a result, we demonstrate that these two heptapeptide motifs could exhibit high degeneracy if coded by separate nucleotide sequences, and elucidate how overlap-evoked co-evolutionary constraints play a role in making the VP and AAP heptapeptide sequences into the present shape. Specifically, we demonstrate that two valine (V) residues and β-strand propensity in QVKEVTQ are structurally important, the strongly negative and hydrophilic nature of KSKRSRR is functionally important, and overlap-evoked co-evolution imposes strong constraints on serine (S) residues in KSKRSRR, despite high degeneracy of the motifs in the absence of co-evolutionary constraints.