Predicting genetic interactions with random walks on biological networks

Background  

Several studies have demonstrated that synthetic lethal genetic interactions between gene mutations provide an indication of functional redundancy between molecular complexes and pathways. These observations help explain the finding that organisms are able to tolerate single gene deletions for a large majority of genes. For example, system-wide gene knockout/knockdown studies in S. cerevisiae and C. elegans revealed non-viable phenotypes for a mere 18% and 10% of the genome, respectively. It has been postulated that the low percentage of essential genes reflects the extensive amount of genetic buffering that occurs within genomes. Consistent with this hypothesis, systematic double-knockout screens in S. cerevisiae and C. elegans show that, on average, 0.5% of tested gene pairs are synthetic sick or synthetic lethal. While knowledge of synthetic lethal interactions provides valuable insight into molecular functionality, testing all combinations of gene pairs represents a daunting task for molecular biologists, as the combinatorial nature of these relationships imposes a large experimental burden. Still, the task of mapping pairwise interactions between genes is essential to discovering functional relationships between molecular complexes and pathways, as they form the basis of genetic robustness. Towards the goal of alleviating the experimental workload, computational techniques that accurately predict genetic interactions can potentially aid in targeting the most likely candidate interactions. Building on previous studies that analyzed properties of network topology to predict genetic interactions, we apply random walks on biological networks to accurately predict pairwise genetic interactions. Furthermore, we incorporate all published non-interactions into our algorithm for measuring the topological relatedness between two genes. We apply our method to S. cerevisiae and C. elegans datasets and, using a decision tree classifier, integrate diverse biological networks and show that our method outperforms established methods.     

Radiotelemetry Applied to Field Studies of Shrews

Abstract: Radiotelemetry is a widely used method to study ecology, behavior, and physiology of different animals but has rarely been used on shrews. Small body size, wide neck and narrow skull, high mobility, and fragility of shrews cause problems for both transmitter attachment and the safety of the animals. We developed a method for nonpermanent attachment of transmitters, which allowed us to track such small mammals as the Eurasian water shrews (Neomys fodiens; n = 39, mean body mass 14.9 g), Mediterranean water shrews (Neomys anomalus; n = 32, 10.9 g), and common shrews (Sorex araneus; n = 51, 8.1 g). We used microtransmitters weighing 0.47 g, but those we applied to the larger Neomys species were heavier (0.67 g) because we fortified them with a layer of hard material to prevent damage from biting. We glued a transmitter directly to the skin on a shrew's back, with the anterior edges particularly well sealed. We tracked shrews in the wild and in outdoor enclosures. Transmitters usually dropped off together with peeled skin (on average, after 56.0 hr, n = 92 observations), but if not dropped and if the signal was not lost, mean duration of monitoring was 96.7 hours (n = 37) and in 2 cases exceeded 194 hours. Other advantages of our attachment method were 1) it was less invasive and easier to apply than implantation of transmitters into the body cavity, and 2) we could find dropped transmitters and reuse them. We give suggestions on how to minimize the risk of injury to animals by correct handling, manipulation, and gluing. In conclusion, we recommend radiotelemetry as a useful technique for studying shrew behavior in both free-living populations and experimental enclosures.     

Placing cryptic,recently extinct,or hypothesized taxa into an ultrametric phylogeny using continuous character data: A case study with the lizard Anolis roosevelti

In recent years, enormous effort and investment has been put into assembling the tree of life: a phylogenetic history for all species on Earth. Overwhelmingly, this progress toward building an ever increasingly complete phylogeny of living things has been accomplished through sophisticated analysis of molecular data. In the modern genomic age, molecular genetic data have become very easy and inexpensive to obtain for many species. However, some lineages are poorly represented in or absent from tissue collections, or are unavailable for molecular analysis for other reasons such as restrictive biological sample export laws. Other species went extinct recently and are only available in formalin museum preparations or perhaps even as subfossils. In this brief communication we present a new method for placing cryptic, recently extinct, or hypothesized taxa into an ultrametric phylogeny of extant taxa using continuous character data. This method is based on a relatively simple modification of an established maximum likelihood (ML) method for phylogeny inference from continuous traits. We show that the method works well on simulated trees and data. We then apply it to the case of placing the Culebra Island Giant Anole (Anolis roosevelti) into a phylogeny of Caribbean anoles. Anolis roosevelti is a “crown‐giant” ecomorph anole hypothesized to have once been found throughout the Spanish, United States, and British Virgin Islands, but that has not been encountered or collected since the 1930s. Although this species is widely thought to be closely related to the Puerto Rican giant anole, A. cuvieri, our ML method actually places A. roosevelti in a different part of the tree and closely related to a clade of morphologically similar species. We are unable, however, to reject a phylogenetic position for A. roosevelti that places it as sister taxon to A. cuvieri; although close relationship with the remainder of Puerto Rican anole species is strongly rejected by our method.     

A note on persistence about structured population models

In this paper, we report some results on persistence in two structured population models: a chronic- age-structured epidemic model and an age-duration-structured epidemic model. Regarding these models, we observe that the system is uniformly strongly persistent, which means, roughly speaking, that the proportion of infected subpopulation is bounded away from 0 and the bound does not depend on the initial data after a sufficient long time, if the basic reproduction ratio is larger than one. We derive this by adopting Thieme's technique, which requires some conditions about positivity and compactness. Although the compactness condition is rather difficult to show in general infinite-dimensional function spaces, we can apply Fréchet–Kolmogorov L ¹-compactness criteria to our models. The two examples that we study illuminate a useful method to show persistence in structured population models.     

Integrative factorization of bidimensionally linked matrices

Advances in molecular “omics” technologies have motivated new methodologies for the integration of multiple sources of high-content biomedical data. However, most statistical methods for integrating multiple data matrices only consider data shared vertically (one cohort on multiple platforms) or horizontally (different cohorts on a single platform). This is limiting for data that take the form of bidimensionally linked matrices (eg, multiple cohorts measured on multiple platforms), which are increasingly common in large-scale biomedical studies. In this paper, we propose bidimensional integrative factorization (BIDIFAC) for integrative dimension reduction and signal approximation of bidimensionally linked data matrices. Our method factorizes data into (a) globally shared, (b) row-shared, (c) column-shared, and (d) single-matrix structural components, facilitating the investigation of shared and unique patterns of variability. For estimation, we use a penalized objective function that extends the nuclear norm penalization for a single matrix. As an alternative to the complicated rank selection problem, we use results from the random matrix theory to choose tuning parameters. We apply our method to integrate two genomics platforms (messenger RNA and microRNA expression) across two sample cohorts (tumor samples and normal tissue samples) using the breast cancer data from the Cancer Genome Atlas. We provide R code for fitting BIDIFAC, imputing missing values, and generating simulated data.     

Correction of Systematic Bias in Single Molecule Photobleaching Measurements

Single molecule photobleaching is a powerful technique to measure the number of fluorescent units in subresolution molecular complexes, such as in toxic protein oligomers associated with amyloid diseases. However, photobleaching can occur before the sample is appropriately placed and focused. Such “prebleaching” can introduce a strong systematic bias toward smaller oligomers. Quantitative correction of prebleaching is known to be an ill-posed problem, limiting the utility of the technique. Here, we provide an experimental solution to improve its reliability. We chemically construct multimeric standards to estimate the prebleaching probability, B. We show that B can be used as a constraint to reliably correct the statistics obtained from a known distribution of standard oligomers. Finally, we apply this method to the data obtained from a heterogeneous oligomeric solution of human islet amyloid polypeptide. Our results show that photobleaching can critically skew the estimation of oligomeric distributions, so that low abundance monomers display a much higher apparent abundance. In summary, any inference from photobleaching experiments with B > 0.1 is likely to be unreliable, but our method can be used to quantitatively correct possible errors.     

A new approach to HLA-DPB1 typing combining DNA heteroduplex analysis with allele-specific amplification and enzyme restriction

Allelic polymorphism of HLA-class II antigens plays a key role in the regulation of the immune response and in transplantation immunity. The allelic diversity of these antigens can now be analyzed at the DNA level after amplification by polymerase chain reaction. In this study we apply a simple technique based on the electrophoretic analysis of DNA heteroduplexes to the typing of HLA-DPB1 alleles. In order to increase its resolution, a group-specific amplification was used which subdivides the 19 HLA-DPB1 alleles in two non-overlapping families. A separate analysis was then performed within each group of alleles. This approach allowed an unequivocal one-step typing of the alleles belonging to group 1 which comprises few alleles of high frequency. Some group 2 alleles require, as a further step, the test with a restriction enzyme. The combination of more than one technique represents, in our opinion, the easiest way to solve the micropolymorphism of class II alleles. We conclude that this method, which is very simple, quick, and accurate and does not require probes, may become the method of choice for HLA-DPB1 typing.     

Gene conversion and the evolution of euryarchaeal chaperonins: a maximum likelihood-based method for detecting conflicting phylogenetic signals

Recombination is well known as a complicating factor in the interpretation of molecular phylogenies. Here we describe a maximum likelihood sliding window method based on a likelihood ratio test for scanning DNA sequence alignments for regions of incongruent phylogenetic signals, such as those influenced by recombination. Using this method, we identify several instances of gene conversion between paralogous chaperonin genes in euryarchaeote Archaea, many of which are not detected by two other widely used methods. In the Thermococcus/Pyrococcus lineage, where a gene duplication producing a and b paralogues predates the divergence of Thermococcus strains KS-1 and KS-8, gene conversion has homogenized portions of the a and b genes in KS-8 since the divergence of these two strains. A region near the 3′ end of the a and b paralogues in the methanogen Methanobacterium thermoautotrophicum also appears to have undergone gene conversion. We apply the method to two additional test data sets, the argF gene of Neisseria and a set of actin paralogues in maize, and show that it successfully identifies all the recombinant regions that were previously detected with other methods. Our approach is relatively insensitive to the presence of divergent sequences in the alignment, making it ideal for detecting recombination between both closely and distantly related genes.     

Curing of four different plasmids in Yersinia pestis using plasmid incompatibility

Aims: Plasmids are critical for the pathogenicity of Yersinia pestis. In order to carry out a systematic investigation of their role in pathogenesis, we cured plasmids from Y. pestis. Methods and Results: Each plasmid’s replicon of Y. pestis was cloned into plasmid pEX18Gm containing a counter‐selectable sacB gene, and was then introduced into Y. pestis strain 201 by electroporation. Strains containing recombinant plasmids were cultivated under antibiotic selection. The resultant plasmid‐curing colonies, identified by specific polymerase chain reactions, were then cured off pEX18Gm under sucrose pressure. This method was used to successfully cure all four plasmids of Y. pestis, singly or in different combinations. Conclusions: Naturally evolving plasmids in Y. pestis are difficult to remove by conventional curing methods. We employed a method based on plasmid incompatibility to cure the plasmids from Y. pestis, which confirmed the efficacy of this method for curing plasmids with different types of replicons from one bacterium. Significance and Impact of the Study: There have been no reports on the curing of multiple plasmids by using replication mechanisms from one bacterium with this technique. In the present study, we were able to successfully apply this methodology to cure four plasmids from Y. pestis, confirming its feasibility.     

Incongruence in molecular species delimitation schemes: What to do when adding more data is difficult

Using multiple, independent approaches to molecular species delimitation is advocated to accommodate limitations and assumptions of a single approach. Incongruence in delimitation schemes is a potential by‐product of employing multiple methods on the same data, and little attention has been paid to its reconciliation. Instead, a particular scheme is prioritized, and/or molecular delimitations are coupled with additional, independent lines of evidence that mitigate incongruence. We advocate that incongruence within a line of evidence should be accounted for before comparing across lines of evidence that can themselves be incongruent. Additionally, it is not uncommon for empiricists working in nonmodel systems to be data‐limited, generating some concern for the adequacy of available data to address the question of interest. With conservation and management decisions often hinging on the status of species, it seems prudent to understand the capabilities of approaches we use given the data we have. Here, we apply two molecular species delimitation approaches, spedeSTEM and BPP, to the Castilleja ambigua (Orobanchaceae) species complex, a relatively young plant lineage in western North America. Upon finding incongruence in our delimitation, we employed a post hoc simulation study to examine the power of these approaches to delimit species. Given the data we collected, we find that spedeSTEM lacks the power to delimit while BPP is capable, thus allowing us to address incongruence before proceeding in delimitation. We suggest post hoc simulation studies like this compliment empirical delimitation and serve as a means of exploring conflict within a line of evidence and dealing with it appropriately.     

A Booststrap Adaptive Test for Two-way Analysis of Variance

We propose a method to construct adaptive tests based on a bootstrap technique. The procedure leads to a nearly exact adaptive test depending on the size of the sample. With the use of the estimated Pitman's relative efficacy as selector statistic, we show that the adaptive test has a power that is asymptotically equal to the power of it's better component. We apply the idea to construct an adaptive test for two-way analysis of variance model. Finally, we use simulations to observe the behaviour of the method for small sample sizes.     

In situ activity of a dominant Prochlorococcus ecotype (eHL‐II) from rRNA content and cell size

In the open ocean genetically diverse clades of the unicellular cyanobacteria Prochlorococcus are biogeographically structured along environmental gradients, yet little is known about their in situ activity. To address this gap, here we use the numerically dominant Prochlorococcus clade eHL‐II (eMIT9312) as a model organism to develop and apply a method to examine their in situ activity using rRNA content and cell size as metrics of cellular physiology. For two representative isolates (MIT9312 and MIT9215) rRNA cell^?1 increases linearly with specific growth rate but is anticorrelated with cell size indicated by flow cytometrically measured (SSC). Although each strain has a unique relationship between cellular rRNA (or cell size) and growth rate, both strains have the same strong positive correlation between rRNA cell^?1 SSC^?1 and growth rate. We field test this approach and observe distinct patterns of eHL‐II clade specific activity (rRNA cell^?1 SSC^?1) with depth that are consistent with patterns of photosynthetic rates. This molecular technique provides unique insight into the ecology of Prochlorococcus and could potentially be expanded to include other microbes to unravel the ecological and biogeochemical contributions of genetically distinct marine side scatter microbes.     

Cryptic diversity in disjunct populations of Middle American Montane Pitvipers: a systematic reassessment of Cerrophidion godmani

Jadin, R.C., Townsend, J.H., Castoe, T.A. & Campbell, J.A. (2012). Cryptic diversity in disjunct populations of Middle American Montane Pitvipers: a systematic reassessment of Cerrophidion godmani. —Zoologica Scripta, 41, 455–470. The discovery and taxonomic recognition of cryptic species has become increasingly frequent with the application of molecular phylogenetic analyses, particularly for species with broad geographic distributions. In this study we focus on the venomous pitviper species Cerrophidion godmani that is widely distributed throughout the highlands of Central America. We provide evidence based on both molecular phylogenetic analyses and morphological data that C. godmani represents three deeply divergent lineages and is possibly non‐monophyletic. These three lineages are relatively conserved in their morphology and tend to be highly variable among individuals, but we do find sufficient morphological characters to diagnose them as evolutionarily distinct. We apply these data, together with known geographic distributions of populations, to infer boundaries of these three divergent evolutionary lineages. Based on the body of evidence, we formally name and describe two new species of Cerrophidion and redescribe C. godmani sensu stricto.     

An unexpected switch in peptide binding mode: from simulation to substrate specificity

A ten microsecond molecular dynamics simulation of a kallikrein-related peptidase 7 peptide complex revealed an unexpected change in binding mode. After more than two microseconds unrestrained sampling we observe a spontaneous transition of the binding pose including a 180° rotation around the P1 residue. Subsequently, the substrate peptide occupies the prime side region rather than the cognate non-prime side in a stable conformation. We characterize the unexpected binding mode in terms of contacts, solvent-accessible surface area, molecular interactions and energetic properties. We compare the new pose to inhibitor-bound structures of kallikreins with occupied prime side and find that a similar orientation is adopted. Finally, we apply in silico mutagenesis based on the alternative peptide binding position to explore the prime side specificity of kallikrein-related peptidase 7 and compare it to available experimental data. Our study provides the first microsecond time scale simulation data on a kallikrein protease and shows previously unexplored prime side interactions. Therefore, we expect our study to advance the rational design of inhibitors targeting kallikrein-related peptidase 7, an emerging drug target involved in several skin diseases as well as cancer.     

Computational Identification of Conjugate Paths for Estimation of Properties of Organic Compounds

Abstract

We have developed and implemented a computer-based method to generate and enumerate all conjugate forms of acyclic organic compounds. These algorithms are used in a new approach for the estimation of thermodynamic and physical properties of acyclic organic compounds from their molecular structure. The approach is based on the contributions of Atoms and Bonds in the properties of Conjugate forms (ABC) of a compound and has produced more accurate results than group-contribution methods. Generating all conjugate forms of the molecule whose properties we wish to estimate is necessary in the application of this technique. The use of symbolic computing environments allows the flexible representation and manipulation of molecular structures. Atoms, bonds, molecules, and other entities are represented as interconnected objects. The generation, comparison, and analysis of conjugates are carried out through computer-based manipulation of the objects and their interconnections.     

Optical‐resolution photoacoustic microscopy with ultrafast dual‐wavelength excitation

Fast functional and molecular photoacoustic microscopy requires pulsed laser excitations at multiple wavelengths with enough pulse energy and short wavelength‐switching time. Recent development of stimulated Raman scattering in optical fiber offers a low‐cost laser source for multiwavelength photoacoustic imaging. In this approach, long fibers temporally separate different wavelengths via optical delay. The time delay between adjacent wavelengths may eventually limits the highest A‐line rate. In addition, a long‐time delay in fiber may limit the highest pulse energy, leading to poor image quality. In order to achieve high pulse energy and ultrafast dual‐wavelength excitation, we present optical‐resolution photoacoustic microscopy with ultrafast dual‐wavelength excitation and a signal separation method. The signal separation method is validated in numerical simulation and phantom experiments. We show that when two photoacoustic signals are partially overlapped with a 50‐ns delay, they can be recovered with 98% accuracy. We apply this ultrafast dual‐wavelength excitation technique to in vivo OR‐PAM. Results demonstrate that A‐lines at two wavelengths can be successfully separated, and sO₂ values can be reliably computed from the separated data. The ultrafast dual‐wavelength excitation enables fast functional photoacoustic microscopy with negligible misalignment among different wavelengths and high pulse energy, which is important for in vivo imaging of microvascular dynamics.     

Dynamic linear model for the identification of miRNAs in next-generation sequencing data

Summary Next‐generation sequencing technologies are poised to revolutionize the field of biomedical research. The increased resolution of these data promise to provide a greater understanding of the molecular processes that control the morphology and behavior of a cell. However, the increased amounts of data require innovative statistical procedures that are powerful while still being computationally feasible. In this article, we present a method for identifying small RNA molecules, called miRNAs, which regulate genes by targeting their mRNAs for degradation or translational repression. In the first step of our modeling procedure, we apply an innovative dynamic linear model that identifies candidate miRNA genes in high‐throughput sequencing data. The model is flexible and can accurately identify interesting biological features while accounting for both the read count, read spacing, and sequencing depth. Additionally, miRNA candidates are also processed using a modified Smith–Waterman sequence alignment that scores the regions for potential RNA hairpins, one of the defining features of miRNAs. We illustrate our method on simulated datasets as well as on a small RNA Caenorhabditis elegans dataset from the Illumina sequencing platform. These examples show that our method is highly sensitive for identifying known and novel miRNA genes.