Fourth Symposium on Chemical Evolution and the Origin and Evolution of Life

This symposium was held at the NASA Ames Research Center, Moffett Field, California, July 24-27, 1990. The NASA exobiology investigators reported their recent research findings. Scientific papers were presented in the following areas: cosmic evolution of biogenic compounds, prebiotic evolution (planetary and molecular), early evolution of life (biological and geochemical), evolution of advanced life, solar system exploration, and the Search for Extraterrestrial Intelligence (SETI)

Molecular simulations of conformational transitions in biomolecules using a novel computational tool

The function of biological macromolecules is inherently linked to their complex conformational behaviour. As a consequence, the corresponding potential energy landscape encompasses multiple minima. Some of the intermediate structures between the initial and final states can be characterized by experimental techniques. Computer simulations can explore the dynamics of individual states and bring these together to rationalize the overall process. A novel method based on atomistic structure-based potentials in combination with the empirical valence bond theory (EVB-SBP) has been developed and implemented in the Amber package. The method has been successfully applied to explore various biological processes. The first application of the EVB-SBP approach involves the study of base flipping in B-DNA. The use of simple structurebased potentials are shown to reproduce structural ensembles of stable states obtained by using more accurate force field simulations. Umbrella sampling in conjunction with the energy gap reaction coordinate enables the study of alternative molecular pathways efficiently. The main application of the method is the study of the switching mechanism in a short bistable RNA. Molecular pathways, which connect the two stable states, have been elucidated, with particular interest to the characterisation of the transition state ensemble. In addition, NMR experiments have been performed to support the theoretical findings. Finally, a recent study of large-scale conformational transitions in protein kinases shows the general applicability of the method to different biomolecules

Computational and experimental studies of biomolecules

Integrating experiments and computational modeling is critical for understanding the structure and dynamics of biomolecules. Beyond providing validation for experimental results, computational modeling, that incorporates accurate physical models and enhanced sampling methods, can provide insight into the mechanisms underlying experimental observations. I will present four projects where experiments and computational modeling were used together, to understand mechanisms underlying the structure and dynamics of biomolecules. The first project involves using enhanced sampling to improve the efficiency of calculating the hydration free energies of small molecules using a polarizable force field. These predictions are compared with a conventional free energy method, and excellent agreement is found between the methods. The second project involves using atomic molecular dynamics simulations to determine the molecular mechanism underlying the ability of nanosensor to detect point-mutations in a DNA sequence. By analyzing the nearest-neighbor hydrogen bonding profile, from simulations of the nanosensor, a molecular mechanism was proposed to explain the experimental data. The third project involves the incorporation of non-canonical hydrogen bonding in a RNA coarse-grained model in order to improve 3D structure prediction. This new model is applied to study the sequence-dependent stability of several RNAs including RNA G-quadruplexes. The final project involves the development of a new single-molecule assay to measure local transitions in nucleic acid structures using ultrashort DNA tethers. This project involves collaboration with an experimental biochemistry group to design the DNA tethers and to prepare single-molecule samples. All projects involve the development of new methods to understand the 3D structure and dynamics of biomoleculesPhysic

Development of a simple artificial intelligence method to accurately subtype breast cancers based on gene expression barcodes

>Magister Scientiae - MScINTRODUCTION: Breast cancer is a highly heterogeneous disease. The complexity of achieving an accurate diagnosis and an effective treatment regimen lies within this heterogeneity. Subtypes of the disease are not simply molecular, i.e. hormone receptor over-expression or absence, but the tumour itself is heterogeneous in terms of tissue of origin, metastases, and histopathological variability. Accurate tumour classification vastly improves treatment decisions, patient outcomes and 5-year survival rates. Gene expression studies aided by transcriptomic technologies such as microarrays and next-generation sequencing (e.g. RNA-Sequencing) have aided oncology researcher and clinician understanding of the complex molecular portraits of malignant breast tumours. Mechanisms governing cancers, which include tumorigenesis, gene fusions, gene over-expression and suppression, cellular process and pathway involvementinvolvement, have been elucidated through comprehensive analyses of the cancer transcriptome. Over the past 20 years, gene expression signatures, discovered with both microarray and RNA-Seq have reached clinical and commercial application through the development of tests such as Mammaprint®, OncotypeDX®, and FoundationOne® CDx, all which focus on chemotherapy sensitivity, prediction of cancer recurrence, and tumour mutational level. The Gene Expression Barcode (GExB) algorithm was developed to allow for easy interpretation and integration of microarray data through data normalization with frozen RMA (fRMA) preprocessing and conversion of relative gene expression to a sequence of 1's and 0's. Unfortunately, the algorithm has not yet been developed for RNA-Seq data. However, implementation of the GExB with feature-selection would contribute to a machine-learning based robust breast cancer and subtype classifier. METHODOLOGY: For microarray data, we applied the GExB algorithm to generate barcodes for normal breast and breast tumour samples. A two-class classifier for malignancy was developed through feature-selection on barcoded samples by selecting for genes with 85% stable absence or presence within a tissue type, and differentially stable between tissues. A multi-class feature-selection method was employed to identify genes with variable expression in one subtype, but 80% stable absence or presence in all other subtypes, i.e. 80% in n-1 subtypes. For RNA-Seq data, a barcoding method needed to be developed which could mimic the GExB algorithm for microarray data. A z-score-to-barcode method was implemented and differential gene expression analysis with selection of the top 100 genes as informative features for classification purposes. The accuracy and discriminatory capability of both microarray-based gene signatures and the RNA-Seq-based gene signatures was assessed through unsupervised and supervised machine-learning algorithms, i.e., K-means and Hierarchical clustering, as well as binary and multi-class Support Vector Machine (SVM) implementations. RESULTS: The GExB-FS method for microarray data yielded an 85-probe and 346-probe informative set for two-class and multi-class classifiers, respectively. The two-class classifier predicted samples as either normal or malignant with 100% accuracy and the multi-class classifier predicted molecular subtype with 96.5% accuracy with SVM. Combining RNA-Seq DE analysis for feature-selection with the z-score-to-barcode method, resulted in a two-class classifier for malignancy, and a multi-class classifier for normal-from-healthy, normal-adjacent-tumour (from cancer patients), and breast tumour samples with 100% accuracy. Most notably, a normal-adjacent-tumour gene expression signature emerged, which differentiated it from normal breast tissues in healthy individuals. CONCLUSION: A potentially novel method for microarray and RNA-Seq data transformation, feature selection and classifier development was established. The universal application of the microarray signatures and validity of the z-score-to-barcode method was proven with 95% accurate classification of RNA-Seq barcoded samples with a microarray discovered gene expression signature. The results from this comprehensive study into the discovery of robust gene expression signatures holds immense potential for further R&F towards implementation at the clinical endpoint, and translation to simpler and cost-effective laboratory methods such as qtPCR-based tests

The Effects of Carboxyl-group Specific Modification and Triiodo-L-thyronine on Cardiac Sodium Channels

The patch clamp method was used to evaluate the effects of 3,5,3\u27-triiodo-L-thyronine (T3) and carboxyl modification on adult rabbit ventricular Na+ channels. In contrast to TTX-sensitive Na+ channels, Ca2+ block of cardiac Na+ channels was not prevented by selective carboxyl modification by trimethyloxonium (TMO) or water soluble carbodiimide (WSC). In 2 mM Ca2+, TMO-treated patches exhibited 3 discrete conductance (γNa) levels. An abbreviation of mean open time (MOT) accompanied each decrease in γNa. The effects on channel gating of elevating external Ca2+ differed from those of TMO pretreatment. Ensemble averages after TMO showed a shortening of the time to peak current and an acceleration of the rate of current decay. WSC caused a decrease in γNa and an abbreviation of MOT at all potentials tested. We conclude that alteration of the surface potential by a single carboxyl modification is inadequate to explain the effects of TMO and WSC. Physiological concentrations of T3 increased bursting as measured by the ratio of long events (LE) to the total number of events. In the cell-attached patch configuration, addition of 5 nM T3 to the pipette increased the %LE at all potentials examined. The increase had a biphasic voltage-dependence and peaked at -50 mV. A similar increase in the %LE occurred with 50 nM T3 suggesting saturation at ≤5 nM. LEs sometimes were grouped into runs, but the more usual pattern suggested that modal shifts occurred in ~1 s. Addition of T3 to the bath but not the pipette in cell-attached patches failed to alter the MOT, unitary current, or %LE. Na+ channel gating also was unaffected by patch excision or by addition of T3 to the cytoplasmic face of inside-out patches. Nevertheless, with T3 in the pipette, patch excision to the inside-out configuration caused a dramatic increase in the %LE, especially near the threshold potential, and an increase in the MOT. These results suggested that T3 was not membrane permeable during the time scale of the experiments and that T3\u27s action required close proximity to the extracellular face of the Na+ channel

Dynamics of Macrosystems; Proceedings of a Workshop, September 3-7, 1984

There is an increasing awareness of the important and persuasive role that instability and random, chaotic motion play in the dynamics of macrosystems. Further research in the field should aim at providing useful tools, and therefore the motivation should come from important questions arising in specific macrosystems. Such systems include biochemical networks, genetic mechanisms, biological communities, neutral networks, cognitive processes and economic structures. This list may seem heterogeneous, but there are similarities between evolution in the different fields. It is not surprising that mathematical methods devised in one field can also be used to describe the dynamics of another. IIASA is attempting to make progress in this direction. With this aim in view this workshop was held at Laxenburg over the period 3-7 September 1984. These Proceedings cover a broad canvas, ranging from specific biological and economic problems to general aspects of dynamical systems and evolutionary theory

Emerging Topics in Genome Sequencing and Analysis

This dissertation studies the emerging topics in genome sequencing and analysis with DNA and RNA. The optimal hybrid sequencing and assembly for accurate genome reconstruction and efficient detection approaches for novel ncRNAs in genomes are discussed. The next-generation sequencing is a significant topic that provides whole genetic information for the further biological research. Recent advances in high-throughput genome sequencing technologies have enabled the systematic study of various genomes by making whole genome sequencing affordable. To date, many hybrid genome assembly algorithms have been developed that can take reads from multiple read sources to reconstruct the original genome. An important aspect of hybrid sequencing and assembly is that the feasibility conditions for genome reconstruction can be satisfied by different combinations of the available read sources, opening up the possibility of optimally combining the sources to minimize the sequencing cost while ensuring accurate genome reconstruction. In this study, we derive the conditions for whole genome reconstruction from multiple read sources at a given confidence level and also introduce the optimal strategy for combining reads from different sources to minimize the overall sequencing cost. We show that the optimal read set, which simultaneously satisfies the feasibility conditions for genome reconstruction and minimizes the sequencing cost, can be effectively predicted through constrained discrete optimization. The availability of genome-wide sequences for a variety of species provides a large database for the further RNA analysis with computational methods. Recent studies have shown that noncoding RNAs (ncRNAs) are known to play crucial roles in various biological processes, and some ncRNAs are related to the genome stability and a variety of inherited diseases. The discovery of novel ncRNAs is hence an important topic, and there is a pressing need for accurate computational detection approaches that can be used to efficiently detect novel ncRNAs in genomes. One important issue is RNA structure alignment for comparative genome analysis, as RNA secondary structures are better conserved than the RNA sequences. Simultaneous RNA alignment and folding algorithms aim to accurately align RNAs by predicting the consensus structure and alignment at the same time, but the computational complexity of the optimal dynamic programming algorithm for simultaneous alignment and folding is extremely high. In this work, we proposed an innovative method, TOPAS, for RNA structural alignment that can efficiently align RNAs through topological networks. Although many ncRNAs are known to have a well conserved secondary structure, which provides useful clues for computational prediction, the prediction of ncRNAs is still challenging, since it has been shown that a structure-based approach alone may not be sufficient for detecting ncRNAs in a single sequence. In this study, we first develop a new approach by utilizing the n-gram model to classify the sequences and extract effective features to capture sequence homology. Based on this approach, we propose an advanced method, piRNAdetect, for reliable computational prediction of piRNAs in genome sequences. Utilizing the n-gram model can enhance the detection of ncRNAs that have sparse folding structures with many unpaired bases. By incorporating the n-gram model with the generalized ensemble defect, which assesses structure conservation and conformation to the consensus structure, we further propose RNAdetect, a novel computational method for accurate detection of ncRNAs through comparative genome analysis. Extensive performance evaluation based on the Rfam database and bacterial genomes demonstrates that our approaches can accurately and reliably detect novel ncRNAs, outperforming the current advanced methods

De novo development of novel DM1 toxic ncRNA targeting small molecules and its biological evaluation

La distròfia miotònica de tipus 1 (DM1) és un trastorn neuromuscular incurable causat per les transcripcions tòxiques del gen DMPK. Aquests transcrits porten expansions de repeticions CUG a les regions no traduïdes 3′ (3′UTR). La complexitat intrínseca i la falta de dades cristal·logràfiques fan que les regions d'ARN no codificant siguin objectius difícils d'estudiar en el camp del desenvolupament de nous fàrmacs. En el cas de la DM1, els transcrits tòxics tendeixen a estancar-se a l'interior dels nuclis formant complexos cossos d'inclusió anomenats foci i segrestant molts factors de splicing alternatiu essencials com el Muscleblind-like 1 (MBNL1). La majoria de les característiques fenotípiques de la DM1 es deriven de la reduïda disponibilitat de MBNL1 lliure, per la qual cosa molts esforços terapèutics es centren en recuperar la seva activitat regular. Per a això, en la present tesi, decidim utilitzar com a diana terapèutica l'ARN CUG, amb la finalitat d'alliberar MBNL1. Pel que respecta al disseny de noves estructures, es descriu el cribratge in-silico mitjançant tècniques de disseny de fàrmacs basades en estructura usant dues premisses diferents d'abordar CUG. A més, es desenvolupen vies sintètiques per als candidats seleccionats basades en química clic. Finalment, per a avaluar la seva activitat biològica, es posa a punt un assaig bioquímic ja descrit, i s'utilitzen models cel·lulars i cèl·lules musculars derivades de pacients per a avaluar els candidats més prometedors. Els resultats obtinguts poden conduir a posteriors generacions de lligands, posant de manifest un nou tractament assequible contra la DM1.La distrofia miotónica de tipo 1 (DM1) es un trastorno neuromuscular incurable causado por las transcripciones tóxicas del gen DMPK. Estos transcritos llevan expansiones de repeticiones CUG en las regiones no traducidas 3′ (3′UTR). La complejidad intrínseca y la falta de datos cristalográficos hacen que las regiones de ARN no codificante sean objetivos difíciles de estudiar en el campo del desarrollo de nuevos fármacos. En la DM1, los transcritos tóxicos tienden a estancarse en el interior de los núcleos formando complejos cuerpos de inclusión llamados foci y secuestrando muchos factores de splicing alternativo esenciales como el Muscleblind-like 1 (MBNL1). La mayoría de las características fenotípicas de la DM1 se derivan de la reducida disponibilidad de MBNL1 libre, por lo que muchos esfuerzos terapéuticos se centran en recuperar su actividad regular. Para ello, en la presente tesis, decidimos utilizar como diana terapéutica el ARN CUG, con el fin de liberar MBNL1. Por lo que respecta al diseño de nuevas estructuras, se describe el cribado in-silico mediante técnicas de diseño de fármacos basadas en estructura usando dos premisas diferentes de abordar CUG. Además, se desarrollan vías sintéticas para los candidatos seleccionados basadas en química click. Por último, para evaluar su actividad biológica, se pone a punto un ensayo bioquímico ya descrito, y se utilizan modelos celulares y células musculares derivadas de pacientes para evaluar los candidatos más prometedores. Los resultados obtenidos pueden conducir a posteriores generaciones de ligandos, poniendo de manifiesto un nuevo tratamiento asequible contra la DM1.Myotonic Dystrophy type 1 (DM1) is an incurable neuromuscular disorder caused by toxic DMPK transcripts that carry CUG repeat expansions in the 3′ untranslated regions (3′UTR). The intrinsic complexity and lack of crystallographic data make noncoding RNA regions challenging targets to study in the field of drug discovery. In DM1, toxic transcripts tend to stall in the nuclei forming complex inclusion bodies called foci and sequestering many essential alternative splicing factors such as Muscleblind-like 1 (MBNL1). Most DM1 phenotypic features stem from the reduced availability of free MBNL1, and therefore many therapeutic efforts are focused on recovering its regular activity. For that purpose, in the present thesis, we decide to target CUG RNA to free MBNL1. The in-silico screening using structure-based drug design techniques of novel candidates based on two different approaches is described. Furthermore, synthetic pathways are developed for the selected candidates based on the click chemistry approach. Finally, to assess their biological activity, an already described biochemical test is tuned, and cellular models and patient-derived muscular cells are used to evaluate the most promising candidates. The obtained results may lead to subsequent generations of ligands, highlighting a new affordable treatment against DM1

Fluorescence and NMR Studies of the Role of Metal Ions in HIV-1 Genomic RNA Dimerization and Maturation

The dimerization initiation site (DIS) is an essential RNA element responsible for dimerization of HIV-1 genomic RNA through a kissing loop interaction. The DIS loop contains six auto-complementary nucleotides stabilized by 5'- and 3'-flanking purines. NCp7 chaperone protein catalyzes conversion of an intermediate DIS kissing dimer to a more thermodynamically stable extended duplex dimer in the presence of Mg2+. Sequence constructs intended to model the extended duplex, (DIS 21), and the kissing dimer, DIS23(GA)*DIS23(HxUC), were designed to examine the structural information and biochemical behaviors during maturation. We introduced the fluorescent labeling, 2-aminopurine (2-AP) into these RNA constructs, to finely probe structural transition and local dynamics accompanied by the formation of the DIS dimer. The 2-AP nucleotides were inserted either in the DIS loop or junction to study loop-loop interaction or purine base stacking conformation at the junction responding to the metal ion effect. High resolution NMR methods were then used to probe structural changes associated with mono versus divalent cation binding to the DIS dimers and also determine the Mg2+ binding sites. Significant chemical shift perturbations (CSP) were found upon Mg2+ binding and used to map structural changes. Further Mn2+ paramagnetic relaxation enhancement (PRE) experiments provided evidence for specific Mg2+ ion binding are localized around the 5' purine bases in both the extended duplex and kissing dimers with profound line broadening effects. Mapping the CSP and PRE data onto the available X-ray crystal and NMR solution structures allowed localization of specific Mg2+ ions at binding sites on the DIS dimers created by the unpaired flanking DIS loop purine nucleotides. Our data indicates that the conformations that are metal cation dependent. These findings are consistent with previous results that suggested a role for divalent metal cations in stabilizing the DIS kissing dimer structure and influencing its maturation to an extended duplex form through interactions with the DIS loop