A phenomenological model for predicting melting temperatures of DNA sequences

Isotope-Edited Variable Temperature Infrared Spectroscopy for Measuring Transition Temperatures of Single A-T Watson-Crick Base Pairs in DNA Duplexes

Experimental methods to determine transition temperatures for individual base pair melting events in DNA duplexes are lacking despite intense interest in these thermodynamic parameters. Here, we determine the dimensions of the thymine (T) C2═O stretching vibration when it is within the DNA duplex via isotopic substitutions at other atomic positions in the structure. First, we determined that this stretching state was localized enough to specific atoms in the molecule to make submolecular scale measurements of local structure and stability in high molecular weight complexes. Next, we develop a new isotope-edited variable temperature infrared method to measure melting transitions at various locations in a DNA structure. As an initial test of this "sub-molecular scale thermometer", we applied our T13C2 difference infrared signal to measure location-dependent melting temperatures (TmL) in a DNA duplex via variable temperature attenuated total reflectance Fourier transform infrared (VT-ATR-FTIR) spectroscopy. We report that the TmL of a single Watson-Crick A-T base pair near the end of an A-T rich sequence (poly T) is ∼34.9 ± 0.7°C. This is slightly lower than the TmL of a single base pair near the middle position of the poly T sequence (TmL ∼35.6±0.2°C). In addition, we also report that the TmL of a single Watson-Crick A-T base pair near the end of a 50% G-C sequence (12-mer) is ∼52.5 ± 0.3°C, which is slightly lower than the global melting Tm of the 12-mer sequence (TmL ∼54.0±0.9°C). Our results provide direct physical evidence for end fraying in DNA sequences with our novel spectroscopic methods.

Towards a rational approach to promoter engineering: understanding the complexity of transcription initiation in prokaryotes

Promoter sequences are important genetic control elements. Through their interaction with RNA polymerase they determine transcription strength and specificity, thereby regulating the first step in gene expression. Consequently, they can be targeted as elements to control predictability and tuneability of a genetic circuit, which is essential in applications such as the development of robust microbial cell factories. This review considers the promoter elements implicated in the three stages of transcription initiation, detailing the complex interplay of sequence-specific interactions that are involved, and highlighting that DNA sequence features beyond the core promoter elements work in a combinatorial manner to determine transcriptional strength. In particular, we emphasize that, aside from promoter recognition, transcription initiation is also defined by the kinetics of open complex formation and promoter escape, which are also known to be highly sequence specific. Significantly, we focus on how insights into these interactions can be manipulated to lay the foundation for a more rational approach to promoter engineering.

Exploring the antioxidant, antimicrobial, cytotoxic and biothermodynamic properties of novel morpholine derivative bioactive Mn(ii), Co(ii) and Ni(ii) complexes - combined experimental and theoretical measurements towards DNA/BSA/SARS-CoV-2 3CLPro

A novel class of bioactive complexes (1-3) [MII(L)2(bpy)], where, L = 2-(4-morpholinobenzylideneamino)phenol, bpy = 2,2'-bipyridine, MII = Mn (1), Co (2) or Ni (3), were assigned to octahedral geometry based on analytical and spectral measurements. Gel electrophoresis showed that complex (2) demonstrated significant DNA cleavage activity compared to the other complexes under the action of oxidation agent (H2O2). The DNA binding constant properties measured by various techniques were in the following sequence: (2) > (3) > (1) > (HL), which suggests that the complexes might intercalate DNA, a possibility that is also supported by their biothermodynamic characteristics. The binding constant results for BSA from electronic absorption and fluorometric titrations demonstrate that complex (2) exhibits the highest binding effectiveness among them all, which means that all the compounds could interact with BSA through a static approach, additionally supported by FRET measurements. DFT and docking calculations were employed to realize the electronic structure, reactivity, and interaction capability of all substances with DNA, BSA, and the SARS-CoV-2 main protease. These binding energies fell within the ranges -7.7 to -8.5, -8.2 to -10.1 and -6.7 to -9.3 kcal mol-1, respectively. The higher reactivity of the complexes than the ligand is supported by FMO theory. The in vitro antibacterial, cytotoxicity, and radical scavenging characteristics revealed that complexes (2-3) have better biological efficacy than the others. The cytotoxicity and binding properties also show good correlation with the partition coefficient (log P), which is encouraging because all of the experimental findings are closely correlated with the theoretical measurements.

High resolution ITS amplicon melting analysis as a tool to analyse microbial communities of household biofilms in ex-situ models

Biofilms are the most common growth types of microorganisms. These complex communities usually consist of different species and are embedded in an extracellular matrix containing polymers, proteins and DNA. This matrix offers protection against different (a)biotic environmental factors and generally increases resistances. Higher resistances against antibiotics are one of the main reasons why biofilms are often associated with healthcare settings. Nevertheless, they are also found in domestic settings, mostly in humid places with abundant nutrients like dishwashers or washing machines. Biofilms in these areas show individual compositions and are influenced for example by temperature, frequency of use or the age of the device. In this study, we introduce a model for the ex-situ cultivation of domestic biofilms from household appliances. Furthermore, we tested the ability of high resolution melting analysis (HRMA) as a tool for analysing these biofilms. Our goal was to maintain a high amount of complexity in the ex-situ biofilms that is characterized by the melting behavior of the contained DNA. Dishwasher and washing machine biofilms were sampled in private households and cultivated for 10 d. After DNA extraction, 16S rDNA was sequenced and melting behavior of the bacterial Internal Transcribed Spacer (ITS) region was analysed. Additionally, testing for independence of continuous new sampling, storage of cultivated biofilms in glycerol stocks and following recultivation of them was done up to three times. Our results show that a high level of complexity could be maintained in the ex-situ biofilms after 10 d of cultivation, although in general the bacterial diversity slightly decreased compared to the original biofilm in most cases. Recultivation of a similar biofilm from glycerol stocks was possible as well with some impact by various factors. Differences in the bacterial composition of biofilms could clearly made visible by HRMA although it was not possible to match peaks to a specific phylogenetic group. Still, HRMA proved to be a less costly and time consuming alternative to sequencing for the characterization of biofilms.

Apoptosis-targeted gene therapy for non-small cell lung cancer using chitosan-poly-lactic-co-glycolic acid -based nano-delivery system and CASP8 and miRs 29A-B1 and 34A

Non-small cell lung cancer (NSCLC) is a leading cause of cancer-related deaths worldwide, with resistance to apoptosis being a major driver of therapeutic resistance and aggressive phenotype. This study aimed to develop a novel gene therapy approach for NSCLC by targeting resistance to apoptosis. Loss of function mutations of caspase 8 (CASP8) and downregulation of microRNAs (miRs) 29A-B1 and 34A were identified as key contributors to resistance to apoptosis in NSCLC. A biodegradable polymeric nano-gene delivery system composed of chitosan-poly-lactic-co-glycolic acid was formulated to deliver initiator CASP8 and miRs 29A-B1 and 34A. The nano-formulation efficiently encapsulated the therapeutic genes effectively internalized into NSCLC cells and induced significant apoptosis. Evaluation of the nano-formulation in A549 tumor spheroids showed a significant increase in apoptosis within the core of the spheroids, suggesting effective penetration into the spheroid structures. We provide a novel nano-formulation that demonstrate therapeutic potential for suicidal gene therapy in NSCLC.

Accessible light-controlled knockdown of cell-free protein synthesis using phosphorothioate-caged antisense oligonucleotides

Controlling cell-free expression of a gene to protein with non-invasive stimuli is vital to the future application of DNA nanodevices and synthetic cells. However, little emphasis has been placed on developing light-controlled 'off' switches for cell-free expression. Light-activated antisense oligonucleotides have been developed to induce gene knockdown in living cells; however, they are complicated to synthesise and have not been tested in cell-free systems. Developing simple, accessible methods to produce light-activated antisense oligonucleotides will be crucial for allowing their application in cell-free biology and biotechnology. Here, we report a mild, one-step method for selectively attaching commercially-available photoremovable protecting groups, photocages, onto phosphorothioate linkages of antisense oligonucleotides. Using this photocaging method, upon illumination, the original phosphorothioate antisense oligonucleotide is reformed. Photocaged antisense oligonucleotides, containing mixed phosphorothioate and phosphate backbones, showed a drastic reduction in duplex formation and RNase H activity, which was recovered upon illumination. We then demonstrated that these photocaged antisense oligonucleotides can be used to knock down cell-free protein synthesis using light. This simple and accessible technology will have future applications in light-controlled biological logic gates and regulating the activity of synthetic cells.

AutoPLP: A Padlock Probe Design Pipeline for Zoonotic Pathogens

Emergence of novel zoonotic infections among the human population has increased the burden on global healthcare systems to curb their spread. To meet the evolutionary agility of pathogens, it is essential to revamp the existing diagnostic methods for early detection and characterization of the pathogens at the molecular level. Padlock probes (PLPs), which can leverage the power of isothermal nucleic acid amplification techniques (NAAT) such as rolling circle amplification (RCA), are known for their high sensitivity and specificity in detecting a diverse pathogen panel of interest. However, due to the complexity involved in deciding the target regions for PLP design and the need for optimization of multiple experimental parameters, the applicability of RCA has been limited in point-of-care testing for pathogen detection. To address this gap, we have developed a novel and integrated PLP design pipeline named AutoPLP, which can automate the probe design process for a diverse pathogen panel of interest. The pipeline is composed of three modules which can perform sequence data curation, multiple sequence alignment, conservation analysis, filtration based on experimental parameters (T_m, GC content, and secondary structure formation), and in silico probe validation via potential cross-hybridization check with host genome. The modules can also take into account the backbone and restriction site information, appropriate combinations of which are incorporated along with the probe arms to design a complete probe sequence. The potential applications of AutoPLP are showcased through the design of PLPs for the detection of rabies virus and drug-resistant strains of Mycobacterium tuberculosis.

Molecular dynamics simulation-based trinucleotide and tetranucleotide level structural and energy characterization of the functional units of genomic DNA

Genomes of most organisms on earth are written in a universal language of life, made up of four units - adenine (A), thymine (T), guanine (G), and cytosine (C), and understanding the way they are put together has been a great challenge to date. Multiple efforts have been made to annotate this wonderfully engineered string of DNA using different methods but they lack a universal character. In this article, we have investigated the structural and energetic profiles of both prokaryotes and eukaryotes by considering two essential genomic sites, viz., the transcription start sites (TSS) and exon-intron boundaries. We have characterized these sites by mapping the structural and energy features of DNA obtained from molecular dynamics simulations, which considers all possible trinucleotide and tetranucleotide steps. For DNA, these physicochemical properties show distinct signatures at the TSS and intron-exon boundaries. Our results firmly convey the idea that DNA uses the same dialect for prokaryotes and eukaryotes and that it is worth going beyond sequence-level analyses to physicochemical space to determine the functional destiny of DNA sequences.

Optimization of non-equilibrium self-assembly protocols using Markov state models

The promise of self-assembly to enable the bottom-up formation of materials with prescribed architectures and functions has driven intensive efforts to uncover rational design principles for maximizing the yield of a target structure. Yet, despite many successful examples of self-assembly, ensuring kinetic accessibility of the target structure remains an unsolved problem in many systems. In particular, long-lived kinetic traps can result in assembly times that vastly exceed experimentally accessible timescales. One proposed solution is to design non-equilibrium assembly protocols in which system parameters change over time to avoid such kinetic traps. Here, we develop a framework to combine Markov state model (MSM) analysis with optimal control theory to compute a time-dependent protocol that maximizes the yield of the target structure at a finite time. We present an adjoint-based gradient descent method that, in conjunction with MSMs for a system as a function of its control parameters, enables efficiently optimizing the assembly protocol. We also describe an interpolation approach to significantly reduce the number of simulations required to construct the MSMs. We demonstrate our approach with two examples; a simple semi-analytic model for the folding of a polymer of colloidal particles, and a more complex model for capsid assembly. Our results show that optimizing time-dependent protocols can achieve significant improvements in the yields of selected structures, including equilibrium free energy minima, long-lived metastable structures, and transient states.

Evaluating Plant Gene Models Using Machine Learning

Gene models are regions of the genome that can be transcribed into RNA and translated to proteins, or belong to a class of non-coding RNA genes. The prediction of gene models is a complex process that can be unreliable, leading to false positive annotations. To help support the calling of confident conserved gene models and minimize false positives arising during gene model prediction we have developed Truegene, a machine learning approach to classify potential low confidence gene models using 14 gene and 41 protein-based characteristics. Amino acid and nucleotide sequence-based features were calculated for conserved (high confidence) and non-conserved (low confidence) annotated genes from the published Pisum sativum Cameor genome. These features were used to train eXtreme Gradient Boost (XGBoost) classifier models to predict whether a gene model is likely to be real. The optimized models demonstrated a prediction accuracy ranging from 87% to 90% and an F-1 score of 0.91–0.94. We used SHapley Additive exPlanations (SHAP) and feature importance plots to identify the features that contribute to the model predictions, and we show that protein and gene-based features can be used to build accurate models for gene prediction that have applications in supporting future gene annotation processes.

Terahertz thermal curve analysis for label-free identification of pathogens

In this study, we perform a thermal curve analysis with terahertz (THz) metamaterials to develop a label-free identification tool for pathogens such as bacteria and yeasts. The resonant frequency of the metasensor coated with a bacterial layer changes as a function of temperature; this provides a unique fingerprint specific to the individual microbial species without the use of fluorescent dyes and antibodies. Differential thermal curves obtained from the temperature-dependent resonance exhibit the peaks consistent with bacterial phases, such as growth, thermal inactivation, DNA denaturation, and cell wall destruction. In addition, we can distinguish gram-negative bacteria from gram-positive bacteria which show strong peaks in the temperature range of cell wall destruction. Finally, we perform THz melting curve analysis on the mixture of bacterial species in which the pathogenic bacteria are successfully distinguished from each other, which is essential for practical clinical and environmental applications such as in blood culture.

A label-free sensing method has been developed for identifying hazardous pathogens based on their intrinsic properties. This was possible by interrogating the temperature-dependent dielectric constant of the microbes in the far-infrared range.

Multiplex real-time PCR using SYBR Green: Unspecific intercalating dye to detect antimicrobial resistance genes of Streptococcus pneumoniae in cerebrospinal fluid

Meningitis caused by Streptococcus pneumoniae is still a disease of great impact on Public health, which requires immediate diagnosis and treatment. However, the culture of clinical specimens is often negative and antibiotic susceptibility testing (AST) must be performed with isolated strains. Multiplex real-time polymerase chain reaction (qPCR) has high sensitivity and specificity, produces faster results to identify the pathogen, and it can also be an important tool to identify resistance antibiotic genes earlier than AST, especially in the absence of an isolated strain. This study developed a multiplex qPCR assay, using SYBR Green as a nonspecific dye, to detect antibiotic resistance genes to predict pneumococcal susceptibility/resistance in cerebrospinal fluid (CSF) samples from meningitis patients. From 2017 to 2020, CSF samples were cultured and analyzed by qPCR to detect the main three bacteria causing meningitis. Isolated and reference strains were applied in SYBR Green qPCR multiplex to detect pbp2b, ermB, and mef genes, and the results were compared with the AST. Pneumococcal-positive CSF samples (lytA-positive gene) without isolated strains were also tested to evaluate the antimicrobial susceptibility profile in the region from 2014 to 2020. From the received 873 CSF samples; 263 were cultivated, 149 were lytA-positive in the qPCR, and 25 produced viable isolated pneumococci strains, which were evaluated by AST. Melting temperature for each gene and the acceptance criteria were determined (pbp2b: 78.24–79.86; ermB: 80.88–82.56; mef: 74.85–76.34 ºC). A total of 48/51 strains presented a genetic profile in agreement with the AST results. Resistant strains to erythromycin and clindamycin were ermB-positive, and two were also mef-positive, indicating both resistance mechanisms were present. In the retrospective study of the genetic profile of resistance, 82 lytA-positive CSF samples plus 4 strains were applied in the SYBR Green qPCR multiplex: 51% of samples presented the wild genotype (pbp2b positive and ermB/mef negative); 15% were negative for all the three evaluated, indicating pneumococci resistant to penicillin; and 17% represented the multidrug-resistant pneumococci (pbp2b negative and ermB positive or pbp2b negative and ermB and mef positive). Therefore, SYBR Green qPCR multiplex proved to be a reliable tool to identify resistance genes in S. pneumoniae and would be less expensive than multiplex qPCR using specific probes. This could be easily introduced into the routine of diagnostic laboratories and provide a strong presumption of pneumococcal resistance, especially in the absence of isolated strains.

A positive correlation between GC content and growth temperature in prokaryotes

BACKGROUND

GC pairs are generally more stable than AT pairs; GC-rich genomes were proposed to be more adapted to high temperatures than AT-rich genomes. Previous studies consistently showed positive correlations between growth temperature and the GC contents of structural RNA genes. However, for the whole genome sequences and the silent sites of the codons in protein-coding genes, the relationship between GC content and growth temperature is in a long-lasting debate.

RESULTS

With a dataset much larger than previous studies (681 bacteria and 155 archaea with completely assembled genomes), our phylogenetic comparative analyses showed positive correlations between optimal growth temperature (Topt) and GC content both in bacterial and archaeal structural RNA genes and in bacterial whole genome sequences, chromosomal sequences, plasmid sequences, core genes, and accessory genes. However, in the 155 archaea, we did not observe a significant positive correlation of Topt with whole-genome GC content (GCw) or GC content at four-fold degenerate sites. We randomly drew 155 samples from the 681 bacteria for 1000 rounds. In most cases (> 95%), the positive correlations between Topt and genomic GC contents became statistically nonsignificant (P > 0.05). This result suggested that the small sample sizes might account for the lack of positive correlations between growth temperature and genomic GC content in the 155 archaea and the bacterial samples of previous studies. Comparing the GC content among four categories (psychrophiles/psychrotrophiles, mesophiles, thermophiles, and hyperthermophiles) also revealed a positive correlation between GCw and growth temperature in bacteria. By including the GCw of incompletely assembled genomes, we expanded the sample size of archaea to 303. Positive correlations between GCw and Topt appear especially after excluding the halophilic archaea whose GC contents might be strongly shaped by intense UV radiation.

CONCLUSIONS

This study explains the previous contradictory observations and ends a long debate. Prokaryotes growing in high temperatures have higher GC contents. Thermal adaptation is one possible explanation for the positive association. Meanwhile, we propose that the elevated efficiency of DNA repair in response to heat mutagenesis might have the by-product of increasing GC content like that happens in intracellular symbionts and marine bacterioplankton.

Intron exon boundary junctions in human genome have in-built unique structural and energetic signals

Precise identification of correct exon–intron boundaries is a prerequisite to analyze the location and structure of genes. The existing framework for genomic signals, delineating exon and introns in a genomic segment, seems insufficient, predominantly due to poor sequence consensus as well as limitations of training on available experimental data sets. We present here a novel concept for characterizing exon–intron boundaries in genomic segments on the basis of structural and energetic properties. We analyzed boundary junctions on both sides of all the exons (3 28 368) of protein coding genes from human genome (GENCODE database) using 28 structural and three energy parameters. Study of sequence conservation at these sites shows very poor consensus. It is observed that DNA adopts a unique structural and energy state at the boundary junctions. Also, signals are somewhat different for housekeeping and tissue specific genes. Clustering of 31 parameters into four derived vectors gives some additional insights into the physical mechanisms involved in this biological process. Sites of structural and energy signals correlate well to the positions playing important roles in pre-mRNA splicing.

Plasmonic Layer as a Localized Temperature Control Element for Surface Plasmonic Resonance-Based Sensors

Surface plasmon resonance (SPR) sensing is a well-established high-sensitivity, label-free and real-time detection technique for biomolecular interaction study. Its primary working principle consists of the measurement of the optical refractive index of the medium that is in close vicinity of the sensor surface. Bio-functionalization techniques allow biomolecular events to be located in such a way. Since optical refractive indices of any medium varies with the temperature, the place where the measurement takes place shall be within a temperature-controlled environment in order to ensure any temperature fluctuation is interpreted as a biomolecular event. Since the SPR measurement probes the sensed medium within the penetration depth of the plasmonic wave, which is less or in the order of 1 µm, we propose to use the metallic film constituting the detection surface as a localized heater aiming at controlling finely and quickly the temperature of the sensed medium. The Joule heating principle is then used and the modeling of the heater is reported as well as its validation by thermal IR imaging. Using water as a demonstration medium, SPR measurement results at different temperatures are successfully compared to the theoretical optical refractive index of water versus temperature.

A novel method SEProm for prokaryotic promoter prediction based on DNA structure and energetics

MOTIVATION

Despite conservation in general architecture of promoters and protein-DNA interaction interface of RNA polymerases among various prokaryotes, identification of promoter regions in the whole genome sequences remains a daunting challenge. The available tools for promoter prediction do not seem to address the problem satisfactorily, apparently because the biochemical nature of promoter signals is yet to be understood fully. Using 28 structural and 3 energetic parameters, we found that prokaryotic promoter regions have a unique structural and energy state, quite distinct from that of coding regions and the information for this signature state is in-built in their sequences. We developed a novel promoter prediction tool from these 31 parameters using various statistical techniques.

RESULTS

Here, we introduce SEProm, a novel tool that is developed by studying and utilizing the in-built structural and energy information of DNA sequences, which is applicable to all prokaryotes including archaea. Compared to five most recent, diverged and current best available tools, SEProm performs much better, predicting promoters with an 'F-value' of 82.04 and 'Precision' of 81.08. The next best 'F-value' was obtained with PromPredict (72.14) followed by BProm (68.37). On the basis of 'Precision' value, the next best 'Precision' was observed for Pepper (75.39) followed by PromPredict (72.01). SEProm maintained the lead even when comparison was done on two test organisms (not involved in training for SEProm).

AVAILABILITY AND IMPLEMENTATION

The software is freely available with easy to follow instructions (www.scfbio-iitd.res.in/software/TSS_Predict.jsp).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

RNAIndel: discovering somatic coding indels from tumor RNA-Seq data

MOTIVATION

Reliable identification of expressed somatic insertions/deletions (indels) is an unmet need due to artifacts generated in PCR-based RNA-Seq library preparation and the lack of normal RNA-Seq data, presenting analytical challenges for discovery of somatic indels in tumor transcriptome.

RESULTS

We present RNAIndel, a tool for predicting somatic, germline and artifact indels from tumor RNA-Seq data. RNAIndel leverages features derived from indel sequence context and biological effect in a machine-learning framework. Except for tumor samples with microsatellite instability, RNAIndel robustly predicts 88-100% of somatic indels in five diverse test datasets of pediatric and adult cancers, even recovering subclonal (VAF range 0.01-0.15) driver indels missed by targeted deep-sequencing, outperforming the current best-practice for RNA-Seq variant calling which had 57% sensitivity but with 14 times more false positives.

AVAILABILITY AND IMPLEMENTATION

RNAIndel is freely available at https://github.com/stjude/RNAIndel.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Elucidation of leak-resistance DNA hybridization chain reaction with universality and extensibility

Hybridization chain reaction (HCR) was a significant discovery for the development of nanoscale materials and devices. One key challenge for HCR is the vulnerability to background leakage in the absence of the initiator. Here, we systematically analyze the sources of leakage and refine leak-resistant rule by using molecular thermodynamics and dynamics, biochemical and biophysical methods. Transient melting of DNA hairpin is revealed to be the underlying cause of leakage and that this can be mitigated through careful consideration of the sequence thermodynamics. The transition threshold of the energy barrier is proposed as a testing benchmark of leak-resistance DNA hairpins. The universal design of DNA hairpins is illustrated by the analysis of hsa-miR-21-5p as biomarker when used in conjunction with surface-enhanced Raman spectroscopy. We further extend the strategy for specific signal amplification of miRNA homologs. Significantly, it possibly provides a practical route to improve the accuracy of DNA self-assembly for signal amplification, and that could facilitate the development of sensors for the sensitive detection of interest molecules in biotechnology and clinical medicine.

ChemGenome2.1: An Ab Initio Gene Prediction Software

Gene prediction, also known as gene identification, gene finding, gene recognition, or gene discovery, is among one of the important problems of molecular biology and is receiving increasing attention due to the advent of large-scale genome sequencing projects. We designed an ab initio model (called ChemGenome) for gene prediction in prokaryotic genomes based on physicochemical characteristics of codons. In this chapter, we present the methodology of the latest version of this model ChemGenome2.1 (CG2.1). The first module of the protocol builds a three-dimensional vector from three calculated quantities for each codon-the double-helical trinucleotide base pairing energy, the base pair stacking energy, and an index of the propensity of a codon for protein-nucleic acid interactions. As this three-dimensional vector moves along any genome, the net orientation of the resultant vector should differ significantly for gene and non-genic regions to make a distinction feasible. The predicted putative protein-coding genes from above parameters are passed through a second module of the protocol which reduces the number of false positives by utilizing a filter based on stereochemical properties of protein sequences. The chemical properties of amino acid side chains taken into consideration are the presence of sp3 hybridized γ carbon atom, hydrogen bond donor ability, short/absence of δ carbon and linearity of the side chains/non-occurrence of bi-dentate forks with terminal hydrogen atoms in the side chain. The final prediction of the potential protein-coding genes is based on the frequency of occurrence of amino acids in the predicted protein sequences and their deviation from the frequency values of Swissprot protein sequences, both at monomer and tripeptide levels. The final screening is based on Z-score. Though CG2.1 is a gene finding tool for prokaryotes, considering the underlying similarity in the chemical and physical properties of DNA among prokaryotes and eukaryotes, we attempted to evaluate its applicability for gene finding in the lower eukaryotes. The results give a hope that the concept of gene finding based on physicochemical model of codons is a viable idea for eukaryotes as well, though, undoubtedly, improvements are needed.

Systematic Dissection of Sequence Elements Controlling σ70 Promoters Using a Genomically Encoded Multiplexed Reporter Assay in Escherichia coli

Promoters are the key drivers of gene expression and are largely responsible for the regulation of cellular responses to time and environment. In Escherichia coli, decades of studies have revealed most, if not all, of the sequence elements necessary to encode promoter function. Despite our knowledge of these motifs, it is still not possible to predict the strength and regulation of a promoter from primary sequence alone. Here we develop a novel multiplexed assay to study promoter function in E. coli by building a site-specific genomic recombination-mediated cassette exchange system that allows for the facile construction and testing of large libraries of genetic designs integrated into precise genomic locations. We build and test a library of 10898 σ70 promoter variants consisting of all combinations of a set of eight -35 elements, eight -10 elements, three UP elements, eight spacers, and eight backgrounds. We find that the -35 and -10 sequence elements can explain approximately 74% of the variance in promoter strength within our data set using a simple log-linear statistical model. Simple neural network models explain >95% of the variance in our data set by capturing nonlinear interactions with the spacer, background, and UP elements.

Toward a Universal Structural and Energetic Model for Prokaryotic Promoters

With almost no consensus promoter sequence in prokaryotes, recruitment of RNA polymerase (RNAP) to precise transcriptional start sites (TSSs) has remained an unsolved puzzle. Uncovering the underlying mechanism is critical for understanding the principle of gene regulation. We attempted to search the hidden code in ∼16,500 promoters of 12 prokaryotes representing two kingdoms in their structure and energetics. Twenty-eight fundamental parameters of DNA structure including backbone angles, basepair axis, and interbasepair and intrabasepair parameters were used, and information was extracted from x-ray crystallography data. Three parameters (solvation energy, hydrogen-bond energy, and stacking energy) were selected for creating energetics profiles using in-house programs. DNA of promoter regions was found to be inherently designed to undergo a change in every parameter undertaken for the study, in all prokaryotes. The change starts from some distance upstream of TSSs and continues past some distance from TSS, hence giving a signature state to promoter regions. These signature states might be the universal hidden codes recognized by RNAP. This observation was reiterated when randomly selected promoter sequences (with little sequence conservation) were subjected to structure generation; all developed into very similar three-dimensional structures quite distinct from those of conventional B-DNA and coding sequences. Fine structural details at important motifs (viz. -11, -35, and -75 positions relative to TSS) of promoters reveal novel to our knowledge and pointed insights for RNAP interaction at these locations; it could be correlated with how some particular structural changes at the -11 region may allow insertion of RNAP amino acids in interbasepair space as well as facilitate the flipping out of bases from the DNA duplex.

The Development of an Effective Bacterial Single-Cell Lysis Method Suitable for Whole Genome Amplification in Microfluidic Platforms

Single-cell sequencing is a powerful technology that provides the capability of analyzing a single cell within a population. This technology is mostly coupled with microfluidic systems for controlled cell manipulation and precise fluid handling to shed light on the genomes of a wide range of cells. So far, single-cell sequencing has been focused mostly on human cells due to the ease of lysing the cells for genome amplification. The major challenges that bacterial species pose to genome amplification from single cells include the rigid bacterial cell walls and the need for an effective lysis protocol compatible with microfluidic platforms. In this work, we present a lysis protocol that can be used to extract genomic DNA from both gram-positive and gram-negative species without interfering with the amplification chemistry. Corynebacterium glutamicum was chosen as a typical gram-positive model and Nostoc sp. as a gram-negative model due to major challenges reported in previous studies. Our protocol is based on thermal and chemical lysis. We consider 80% of single-cell replicates that lead to >5 ng DNA after amplification as successful attempts. The protocol was directly applied to Gloeocapsa sp. and the single cells of the eukaryotic Sphaerocystis sp. and achieved a 100% success rate.

Aqueous ionic liquids in comparison with standard co-solutes : Differences and common principles in their interaction with protein and DNA structures

Ionic liquids (ILs) are versatile solvents for a broad range of biotechnological applications. Recent experimental and simulation results highlight the potential benefits of dilute ILs in aqueous solution (aqueous ILs) in order to modify protein and DNA structures systematically. In contrast to a limited number of standard co-solutes like urea, ectoine, trimethylamine-N-oxide (TMAO), or guanidinium chloride, the large amount of possible cation and anion combinations in aqueous ILs can be used to develop tailor-made stabilizers or destabilizers for specific purposes. In this review article, we highlight common principles and differences between aqueous ILs and standard co-solutes with a specific focus on their underlying macromolecular stabilization or destabilization behavior. In combination with statistical thermodynamics theories, we present an efficient framework, which is used to classify structure modification effects consistently. The crucial importance of enthalpic and entropic contributions to the free energy change upon IL-assisted macromolecular unfolding in combination with a complex destabilization mechanism is described in detail. A special focus is also set on aqueous IL-DNA interactions, for which experimental and simulation outcomes are summarized and discussed in the context of previous findings.

Physico-chemical fingerprinting of RNA genes

We advance here a novel concept for characterizing different classes of RNA genes on the basis of physico-chemical properties of DNA sequences. As knowledge-based approaches could yield unsatisfactory outcomes due to limitations of training on available experimental data sets, alternative approaches that utilize properties intrinsic to DNA are needed to supplement training based methods and to eventually provide molecular insights into genome organization. Based on a comprehensive series of molecular dynamics simulations of Ascona B-DNA consortium, we extracted hydrogen bonding, stacking and solvation energies of all combinations of DNA sequences at the dinucleotide level and calculated these properties for different types of RNA genes. Considering ∼7.3 million mRNA, 255 524 tRNA, 40 649 rRNA (different subunits) and 5250 miRNA, 3747 snRNA, gene sequences from 9282 complete genome chromosomes of all prokaryotes and eukaryotes available at NCBI, we observed that physico-chemical properties of different functional units on genomic DNA differ in their signatures.

Direct Nanofabrication Using DNA Nanostructure

Recent advances in DNA nanotechnology make it possible to fabricate arbitrarily shaped 1D, 2D, and 3D DNA nanostructures through controlled folding and/or hierarchical assembly of up to several thousands of unique sequenced DNA strands. Both individual DNA nanostructures and their assembly can be made with almost arbitrarily shaped patterns at a theoretical resolution down to 2 nm. Furthermore, the deposition of DNA nanostructures on a substrate can be made with precise control of their location and orientation, making them ideal templates for bottom-up nanofabrication. However, many fabrication processes require harsh conditions, such as corrosive chemicals and high temperatures. It still remains a challenge to overcome the limited stability of DNA nanostructures during the fabrication process.This chapter focuses on the proof-of-principle study to directly convert the structural information of DNA nanostructure to various kinds of materials by nanofabrication.

Onco-Regulon: an integrated database and software suite for site specific targeting of transcription factors of cancer genes

Transcription factors (TFs) bind at multiple sites in the genome and regulate expression of many genes. Regulating TF binding in a gene specific manner remains a formidable challenge in drug discovery because the same binding motif may be present at multiple locations in the genome. Here, we present Onco-Regulon (http://www.scfbio-iitd.res.in/software/onco/NavSite/index.htm), an integrated database of regulatory motifs of cancer genes clubbed with Unique Sequence-Predictor (USP) a software suite that identifies unique sequences for each of these regulatory DNA motifs at the specified position in the genome. USP works by extending a given DNA motif, in 5′→3′, 3′ →5′ or both directions by adding one nucleotide at each step, and calculates the frequency of each extended motif in the genome by Frequency Counter programme. This step is iterated till the frequency of the extended motif becomes unity in the genome. Thus, for each given motif, we get three possible unique sequences. Closest Sequence Finder program predicts off-target drug binding in the genome. Inclusion of DNA-Protein structural information further makes Onco-Regulon a highly informative repository for gene specific drug development. We believe that Onco-Regulon will help researchers to design drugs which will bind to an exclusive site in the genome with no off-target effects, theoretically.

Database URL:http://www.scfbio-iitd.res.in/software/onco/NavSite/index.htm

Relationship between digital information and thermodynamic stability in bacterial genomes

Ever since the introduction of the Watson-Crick model, numerous efforts have been made to fully characterize the digital information content of the DNA. However, it became increasingly evident that variations of DNA configuration also provide an “analog” type of information related to the physicochemical properties of the DNA, such as thermodynamic stability and supercoiling. Hence, the parallel investigation of the digital information contained in the base sequence with associated analog parameters is very important for understanding the coding capacity of the DNA. In this paper, we represented analog information by its thermodynamic stability and compare it with digital information using Shannon and Gibbs entropy measures on the complete genome sequences of several bacteria, including Escherichia coli (E. coli), Bacillus subtilis (B. subtilis), Streptomyces coelicolor (S. coelicolor), and Salmonella typhimurium (S. typhimurium). Furthermore, the link to the broader classes of functional gene groups (anabolic and catabolic) is examined. Obtained results demonstrate the couplings between thermodynamic stability and digital sequence organization in the bacterial genomes. In addition, our data suggest a determinative role of the genome-wide distribution of DNA thermodynamic stability in the spatial organization of functional gene groups.

A statistical thermodynamic model for investigating the stability of DNA sequences from oligonucleotides to genomes

We describe the development and testing of a simple statistical mechanics methodology for duplex DNA applicable to sequences of any composition and extensible to genomes. The microstates of a DNA sequence are modeled in terms of blocks of basepairs that are assumed to be fully closed (paired) or open. This approach generates an ensemble of bubblelike microstates that are used to calculate the corresponding partition function. The energies of the microstates are calculated as additive contributions from hydrogen bonding, basepair stacking, and solvation terms parameterized from a comprehensive series of molecular dynamics simulations including solvent and ions. Thermodynamic properties and nucleotide stability constants for DNA sequences follow directly from the partition function. The methodology was tested by comparing computed free energies per basepair with the experimental melting temperatures of 60 oligonucleotides, yielding a correlation coefficient of -0.96. The thermodynamic stability of genic/nongenic regions was tested in terms of nucleotide stability constants versus sequence for the Escherichia coli K-12 genome. It showed clear differentiation of the genes from promoters and captures genic regions with a sensitivity of 0.94. The statistical thermodynamic model presented here provides a seemingly new handle on the challenging problem of interpreting genomic sequences.

Temperature and magnetic field driven modifications in the I-V features of gold-DNA-gold structure I-V

The fabrication of Metal-DNA-Metal (MDM) structure-based high sensitivity sensors from DNA micro-and nanoarray strands is a key issue in their development. The tunable semiconducting response of DNA in the presence of external electromagnetic and thermal fields is a gift for molecular electronics. The impact of temperatures (25-55 °C) and magnetic fields (0-1200 mT) on the current-voltage (I-V) features of Au-DNA-Au (GDG) structures with an optimum gap of 10 μm is reported. The I-V characteristics acquired in the presence and absence of magnetic fields demonstrated the semiconducting diode nature of DNA in GDG structures with high temperature sensitivity. The saturation current in the absence of magnetic field was found to increase sharply with the increase of temperature up to 45 °C and decrease rapidly thereafter. This increase was attributed to the temperature-assisted conversion of double bonds into single bond in DNA structures. Furthermore, the potential barrier height and Richardson constant for all the structures increased steadily with the increase of external magnetic field irrespective of temperature variations. Our observation on magnetic field and temperature sensitivity of I-V response in GDG sandwiches may contribute towards the development of DNA-based magnetic sensors.

Temperature effect on poly(dA).poly(dT): molecular dynamics simulation studies of polymeric and oligomeric constructs

Understanding unwinding and melting of double helical DNA is very important to characterize role of DNA in replication, transcription, translation etc. Sequence dependent melting thermodynamics is used extensively for detecting promoter regions but melting studies are generally done for short oligonucleotides. This study reports several molecular dynamics (MD) simulations of homopolymeric poly(dA).poly(dT) as regular oligonucleotide fragments as well as its corresponding polymeric constructs with water and charge-neutralizing counterions at different temperatures ranging from 300 to 400 K. We have eliminated the end-effect or terminal peeling propensity by employing MD simulation of DNA oligonucleotides in such a manner that gives rise to properties of polymeric DNA of infinite length. The dynamic properties such as basepairing and stacking geometry, groove width, backbone conformational parameters, bending, distribution of counter ions and number of hydrogen bonds of oligomeric and polymeric constructs of poly(dA).poly(dT) have been analyzed. The oligomer shows terminal fraying or peeling effect at temperatures above 340 K. The polymer shows partial melting at elevated temperatures although complete denaturations of basepairs do not take place. The analysis of cross strand hydrogen bonds shows that the number of N-H···O hydrogen bonds increases with increase in temperature while C-H···O hydrogen bond frequencies decrease with temperature. Restructuring of counterions in the minor groove with temperature appear as initiation of melting in duplex structures.

β sheets not required: combined experimental and computational studies of self-assembly and gelation of the ester-containing analogue of an Fmoc-dipeptide hydrogelator

In our work toward developing ester-containing self-assembling peptides as soft biomaterials, we have found that a fluorenylmethoxycarbonyl (Fmoc)-conjugated alanine-lactic acid (Ala-Lac) sequence self-assembles into nanostructures that gel in water. This process occurs despite Fmoc-Ala-Lac's inability to interact with other Fmoc-Ala-Lac molecules via β-sheet-like amide-amide hydrogen bonding, a condition previously thought to be crucial to the self-assembly of Fmoc-conjugated peptides. Experimental comparisons of Fmoc-Ala-Lac to its self-assembling peptide sequence analogue Fmoc-Ala-Ala using a variety of microscopic, spectroscopic, and bulk characterization techniques demonstrate distinct features of the two systems and show that while angstrom-scale self-assembled structures are similar, their nanometer-scale size and morphological properties diverge and give rise to different bulk mechanical properties. Molecular dynamics simulations were performed to gain more insight into the differences between the two systems. An analysis of the hydrogen-bonding and solvent-surface interface properties of the simulated fibrils revealed that Fmoc-Ala-Lac fibrils are stronger and less hydrophilic than Fmoc-Ala-Ala fibrils. We propose that this difference in fibril amphiphilicity gives rise to differences in the higher-order assembly of fibrils into nanostructures seen in TEM. Importantly, we confirm experimentally that β-sheet-type hydrogen bonding is not crucial to the self-assembly of short, conjugated peptides, and we demonstrate computationally that the amide bond in such systems may act mainly to mediate the solvation of the self-assembled single fibrils and therefore regulate a more extensive higher-order aggregation of fibrils. This work provides a basic understanding for future research in designing highly degradable self-assembling materials with peptide-like bioactivity for biomedical applications.

A new framework to accurately quantify soil bacterial community diversity from DGGE

Denaturing gradient gel electrophoresis (DGGE) has been and remains extensively used to assess and monitor the effects of various treatments on soil bacterial communities. Considering only abundant phylotypes, the diversity estimates produced by this technique have been proven to be uncorrelated to true community diversity. The aim of this paper was to develop a framework to estimate a community's true diversity from DGGE. Developed using in silico DGGE profiles generated from published pyrosequencing datasets, this framework elongates the rank-abundance distributions (RADs) drawn by band quantification using the peak-to-signal ratio (PSR) parameter, which was proven to be related to bacterial richness. The ability to compare DGGE-based diversity estimates to the true diversity of communities led to a unique opportunity to identify potential pitfalls when analyzing DGGE gels with commercial analysis software programs and gain insight into the process of DNA band clustering in the profiles. Bacterial diversity was compared through richness, Shannon, and Simpson's 1/D indices. Intermediate results demonstrated that, even though commercial gel analysis software programs were unable to produce consistent results throughout all samples, a newly developed Matlab-based framework unraveled the dominance profiles of communities from band quantification. Elongating these partial RADs using the PSRs extracted from the DGGE profiles chiefly made it possible to accurately estimate the true diversity of communities. For all the samples analyzed, the estimated Shannon and Simpson's 1/D were accurate at ±10 %. Richness estimations were less accurate, ranging from -11 to 31 % of the expected values. The framework showed great potential to study the structure and diversity of soil bacterial communities.

TfReg: calculating DNA and RNA melting temperatures and opening profiles with mesoscopic models

SUMMARY

The mesoscopic statistical physics models, known generically as Peyrard-Bishop (PB) models, have found many applications for the study of oligonucleotide properties. Unfortunately, PB models have not reached a wider non-specialized audience for the lack of freely available software implementations. Here we present an extensible C++ implementation of four variants of the PB model, which allows the user to calculate melting temperatures from tested model parameters. Even for a non-specialist, it should be straightforward to change these parameters to reflect different experimental environments or different types of oligonucleotides. For users with some proficiency in C++ programming, it should be feasible to extend the code to other PB models owing to the generic programming implementation adopted for TfReg. Pre-calculated parameters are included that allow the immediate calculation of melting temperatures and thermal equivalence indexes for DNA and RNA.

AVAILABILITY

C++ source code and compiled binaries for several Linux distributions are available from https://sites.google.com/site/geraldweberufmg/tfreg and from OpenSuse build service at http://build.opensuse.org.

CONTACT

gweberbh@gmail.com

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Genomes to hits in silico - a country path today, a highway tomorrow: a case study of chikungunya

These are exciting times for bioinformaticians, computational biologists and drug designers with the genome and proteome sequences and related structural databases growing at an accelerated pace. The post-genomic era has triggered high expectations for a rapid and successful treatment of diseases. However, in this biological information rich and functional knowledge poor scenario, the challenges are indeed grand, no less than the assembly of the genome of the whole organism. These include functional annotation of genes, identification of druggable targets, prediction of three-dimensional structures of protein targets from their amino acid sequences, arriving at lead compounds for these targets followed by a transition from bench to bedside. We propose here a "Genome to Hits In Silico" strategy (called Dhanvantari) and illustrate it on Chikungunya virus (CHIKV). "Genome to hits" is a novel pathway incorporating a series of steps such as gene prediction, protein tertiary structure determination, active site identification, hit molecule generation, docking and scoring of hits to arrive at lead compounds. The current state of the art for each of the steps in the pathway is high-lighted and the feasibility of creating an automated genome to hits assembly line is discussed.

Spectral analysis of naturally occurring methylxanthines (theophylline, theobromine and caffeine) binding with DNA

Nucleic acids exist in a dynamic equilibrium with a number of molecules that constantly interact with them and regulate the cellular activities. The inherent nature of the structure and conformational integrity of these macromolecules can lead to altered biological activity through proper targeting of nucleic acids binding ligands or drug molecules. We studied the interaction of naturally occurring methylxanthines such as theophylline, theobromine and caffeine with DNA, using UV absorption and Fourier transform infrared (FTIR) spectroscopic methods, and especially monitored their binding affinity in the presence of Mg(2+) and during helix-coil transitions of DNA by temperature (T(m)) or pH melting profiles. The study indicates that all these molecules effectively bind to DNA in a dose dependent manner. The overall binding constants of DNA-theophylline = 3.5×10(3) M(-1), DNA-theobromine = 1.1×10(3) M(-1), and DNA-Caffeine = 3.8×10(3) M(-1). On the other hand T(m)/pH melting profiles showed 24-35% of enhanced binding activity of methylxanthines during helix-coil transitions of DNA rather than to its native double helical structure. The FTIR analysis divulged that theophylline, theobromine and caffeine interact with all the base pairs of DNA (A-T; G-C) and phosphate group through hydrogen bond (H-bond) interaction. In the presence of Mg(2+), methylxanthines altered the structure of DNA from B to A-family. However, the B-family structure of DNA remained unaltered in DNA-methylxanthines complexes or in the absence of Mg(2+). The spectral analyses indicated the order of binding affinity as "caffeine≥theophylline>theobromine" to the native double helical DNA, and "theophylline≥theobromine>caffeine to the denatured form of DNA and in the presence of divalent metal ions.

DNA-energetics-based analyses suggest additional genes in prokaryotes

We present here a novel methodology for predicting new genes in prokaryotic genomes on the basis of inherent energetics of DNA. Regions of higher thermodynamic stability were identified, which were filtered based on already known annotations to yield a set of potentially new genes. These were then processed for their compatibility with the stereo-chemical properties of proteins and tripeptide frequencies of proteins in Swissprot data, which results in a reliable set of new genes in a genome. Quite surprisingly, the methodology identifies new genes even in well-annotated genomes. Also, the methodology can handle genomes of any GC-content, size and number of annotated genes.

DNA-water interactions distinguish messenger RNA genes from transfer RNA genes

Physicochemical properties of DNA sequences as a guide to developing insights into genome organization has received little attention. Here, we utilize the energetics of DNA to further advance the knowledge on its language at a molecular level. Specifically, we ask the question whether physicochemical properties of different functional units on genomes differ. We extract intramolecular and solvation energies of different DNA base pair steps from a comprehensive set of molecular dynamics simulations. We then investigate the solvation behavior of DNA sequences coding for mRNAs and tRNAs. Distinguishing mRNA genes from tRNA genes is a tricky problem in genome annotation without assumptions on length of DNA and secondary structure of the product of transcription. We find that solvation energetics of DNA behaves as an extremely efficient property in discriminating 2,063,537 genes coding for mRNAs from 56,251 genes coding for tRNAs in all (~1500) completely sequenced prokaryotic genomes.

Role of sequence encoded κB DNA geometry in gene regulation by Dorsal

Many proteins of the Rel family can act as both transcriptional activators and repressors. However, mechanism that discerns the ‘activator/repressor’ functions of Rel-proteins such as Dorsal (Drosophila homologue of mammalian NFκB) is not understood. Using genomic, biophysical and biochemical approaches, we demonstrate that the underlying principle of this functional specificity lies in the ‘sequence-encoded structure’ of the κB-DNA. We show that Dorsal-binding motifs exist in distinct activator and repressor conformations. Molecular dynamics of DNA-Dorsal complexes revealed that repressor κB-motifs typically have A-tract and flexible conformation that facilitates interaction with co-repressors. Deformable structure of repressor motifs, is due to changes in the hydrogen bonding in A:T pair in the ‘A-tract’ core. The sixth nucleotide in the nonameric κB-motif, ‘A’ (A₆) in the repressor motifs and ‘T’ (T₆) in the activator motifs, is critical to confer this functional specificity as A₆ → T₆ mutation transformed flexible repressor conformation into a rigid activator conformation. These results highlight that ‘sequence encoded κB DNA-geometry’ regulates gene expression by exerting allosteric effect on binding of Rel proteins which in turn regulates interaction with co-regulators. Further, we identified and characterized putative repressor motifs in Dl-target genes, which can potentially aid in functional annotation of Dorsal gene regulatory network.