DNA pooling in mutation detection with reference to sequence analysis

We discuss pooling methods of mutation detection for identifying rare mutations. We provide mathematical formulae for obtaining the optimal pool size as a function of the mutation frequency in the study population and the specificity of the test. The optimal pool size depends strongly on the specificity of the test. With a test that has 99% specificity, pooling can reduce the number of tests that need to be performed by 80%, whereas, with a test with 95% specificity, pooling reduces the number of samples that must be tested by only 50%. We used the software PHRED to call mutations after sequencing of pooled samples with known STK11 mutations. We found that, when the area under the curve for the less prominent peak was used to call mutations, we were able to pool pairs of samples and correctly identify mutations. Pooling of three samples did not lead to an adequately specific test for the basic automated allele-calling procedures that we used. We discuss methods by which the specificity may be improved to permit pooling of three or more samples when testing for mutations by sequencing.     

噪声和高温对机体健康影响的复合效应

当今社会随着经济和科技的发展,多种有害职业因素往往共同存在于同一岗位。复合因素对机体健康影响的相互作用包括四种情况:协同作用、相加作用、拮抗作用和无关作用。噪声和高温作为有害的职业因素常存在于同一岗位,那么噪声复合高温对机体健康会产生怎样的影响,这两种环境因素是否存在复合效应?文献报道不一。噪声和高温联合对听觉系统、心血管系统、神经系统的影响可能表现为协同、相加、拮抗和无关作用,对呼吸系统的影响表现为拮抗作用,目前,研究结果不一致主要原因是实验条件和暴露方法的不一致。我们认为将来的研究热点集中在噪声和高温联合产生复合效应的条件及剂量反应关系研究、机制及防治措施研究,噪声和高温联合对其他系统如消化、免疫系统是否存在复合效应也值得深入研究。     

Group screening for rare events based on incomplete block designs

Fields such as, diagnostic testing, biotherapeutics, drug development, and toxicology among others, center on the premise of searching through many specimens for a rare event. Scientists in the business of “searching for a needle in a haystack” may greatly benefit from the use of group screening design strategies. Group screening, where specimens are composited into pools with each pool being tested for the presence of the event, can be much more cost-efficient than testing each individual specimen. A number of group screening designs have been proposed in the literature. Incomplete block screening designs are described here and compared with other group screening designs. It is shown under certain conditions, that incomplete block screening designs can provide nearly a 90% cost saving compared to other group screening designs such as when prevalence is 0.001 and screening 3876 specimens with an ICB-sequential design vs. a Dorfman design. In other cases, previous group screening designs are shown to be most efficient. Overall, when prevalence is small (≤0.05) group screening designs are shown to be quite cost effective at screening a large number of specimens and in general there is no one design that is best in all situations. © 2018 American Institute of Chemical Engineers Biotechnol Progress, 35: e2770, 2019.     

Estimation of gene frequency and heterozygosity from pooled samples

Pooling of DNA samples can significantly reduce the effort of population studies with DNA markers. I present a statistical model and numerical method for estimating gene frequency when pooled DNA is assayed for the presence/absence of alleles. Analytical and Monte‐Carlo methods examined estimation variance and bias, and hence optimal pool size, under a triangular allele frequency distribution. For gene frequency of rarer alleles, the optimal number of pooled individuals is approximately the inverse of the gene frequency. For heterozygosity, the optimal pool is approximately half the allele number; this results in pools containing, on average, 60% of possible alleles.     

Statistical decoding of potent pools based on chemical structure

Pooling experiments are used as a cost-effective approach for screening chemical compounds as part of the drug discovery process in pharmaceutical companies. When a biologically potent pool is found, the goal is to decode the pool, i.e., to determine which of the individual compounds are potent. We propose augmenting the data on pooled testing with information on the chemical structure of compounds in order to complete the decoding process. This proposal is based on the well-known relationship between biological potency of a compound and its chemical structure. Application to real data from a drug discovery process at GlaxoSmithKline reveals a 100% increase in hit rate, namely, the number of potent compounds identified divided by the number of tests required.     

A Positive Detecting Code and Its Decoding Algorithm for DNA Library Screening

The study of gene functions requires high-quality DNA libraries. However, a large number of tests and screenings are necessary for compiling such libraries. We describe an algorithm for extracting as much information as possible from pooling experiments for library screening. Collections of clones are called pools, and a pooling experiment is a group test for detecting all positive clones. The probability of positiveness for each clone is estimated according to the outcomes of the pooling experiments. Clones with high chance of positiveness are subjected to confirmatory testing. In this paper, we introduce a new positive clone detecting algorithm, called the Bayesian network pool result decoder (BNPD). The performance of BNPD is compared, by simulation, with that of the Markov chain pool result decoder (MCPD) proposed by Knill et al. in 1996. Moreover, the combinatorial properties of pooling designs suitable for the proposed algorithm are discussed in conjunction with combinatorial designs and dhbox{-}{rm disjunct} matrices. We also show the advantage of utilizing packing designs or BIB designs for the BNPD algorithm.     

Fractional simplex designs for interaction screening in complex mixtures

In mixture experiments, one may be interested in estimating not only main effects but also some interactions. Main effects and significant interactions in a mixture may be estimated through appropriate mixture experiments, such as simplex-centroid designs. However, for mixtures with a large number of factors, the run size for these designs becomes impractically large. A subset of a full simplex-centroid design may be used, but the problem remains regarding which factor-level settings should be selected. In this paper, we propose a solution that considers design points with either one or p individual nonzero factor-level settings. These fractional simplex designs provide a means of screening for interactions and of investigating the behavior of many-component mixtures as a whole while greatly reducing the run size compared with full simplex-centroid designs. The means of construction of the design arrays is described, and designs for < or = 31 factors are presented. Some of the proposed methodology is illustrated using generated data.     

Common pitfalls when testing additivity of treatment mixtures with chi‐square analyses

Studying interactions of multiple pesticides applied simultaneously in a mixture is a common task in phytopathology. Statistical methods are employed to test whether the treatment components influence each other's efficacy in a promotive or inhibitory way (synergistic or antagonistic interaction) or rather act independent of one another (additivity). The trouble is that widely used procedures based on chi‐square tests are often seriously flawed, either because people apply them in a preposterous way or because the method simply does not fit the problem at hand. Browsing recent volumes of entomological journals, we found that numerous researchers have (in all likelihood unwittingly) analysed their data as if they had had a sample size of 100 or, equally bad, a sample size of one! We show how to avoid such poor practices and further argue that chi‐square testing is, even if applied correctly (meaning that no technical errors are made), a limited purpose tool for assessing treatment interactions.     

Interactive and cumulative effects of multiple human stressors in marine systems

Humans impact natural systems in a multitude of ways, yet the cumulative effect of multiple stressors on ecological communities remains largely unknown. Here we synthesized 171 studies that manipulated two or more stressors in marine and coastal systems and found that cumulative effects in individual studies were additive (26%), synergistic (36%), and antagonistic (38%). The overall interaction effect across all studies was synergistic, but interaction type varied by response level (community: antagonistic, population: synergistic), trophic level (autotrophs: antagonistic, heterotrophs: synergistic), and specific stressor pair (seven pairs additive, three pairs each synergistic and antagonistic). Addition of a third stressor changed interaction effects significantly in two‐thirds of all cases and doubled the number of synergistic interactions. Given that most studies were performed in laboratories where stressor effects can be carefully isolated, these three‐stressor results suggest that synergies may be quite common in nature where more than two stressors almost always coexist. While significant gaps exist in multiple stressor research, our results suggest an immediate need to account for stressor interactions in ecological studies and conservation planning.     

Evolution can favor antagonistic epistasis

The accumulation of deleterious mutations plays a major role in evolution, and key to this are the interactions between their fitness effects, known as epistasis. Whether mutations tend to interact synergistically (with multiple mutations being more deleterious than would be expected from their individual fitness effects) or antagonistically is important for a variety of evolutionary questions, particularly the evolution of sex. Unfortunately, the experimental evidence on the prevalence and strength of epistasis is mixed and inconclusive. Here we study theoretically whether synergistic or antagonistic epistasis is likely to be favored by evolution and by how much. We find that in the presence of recombination, evolution favors less synergistic or more antagonistic epistasis whenever mutations that change the epistasis in this direction are possible. This is because evolution favors increased buffering against the effects of deleterious mutations. This suggests that we should not expect synergistic epistasis to be widespread in nature and hence that the mutational deterministic hypothesis for the advantage of sex may not apply widely.     

Do Deleterious Mutations Act Synergistically? Metabolic Control Theory Provides a Partial Answer

Metabolic control theory is used to derive conditions under which two deleterious mutations affecting the dynamics of a metabolic pathway act synergistically. It is found that two mutations tend to act mostly synergistically when they reduce the activity of the same enzyme. If the two mutations affect different enzymes, the conclusion depends on the way that fitness is determined by aspects of the pathway. The cases analyzed are: selection for (1) maximal flux, (2) maximal equilibrium concentration (pool size) of an intermediate, (3) optimal flux, (4) optimal pool size. The respective types of epistasis found are: (1) antagonistic, (2) partly synergistic, (3-4) synergism is likely to predominate over antagonism. This results in somewhat different predictions concerning the effect of metabolic mutations on fitness in prokaryotes and eukaryotes. The fact that bacteria are largely clonal but have often a mosaic gene structure is consistent with expectations from the model.     

Human population structure and its effects on sampling Y chromosome sequence variation

The excess of rare variants in global sequencing studies of the nonrecombining portion of the Y chromosome (NRY) has been interpreted as evidence for the effects of human demographic expansion. However, many NRY polymorphisms are geographically localized and the effect of different geographical sampling on patterns of NRY variation is unknown. We use two sampling designs to detect population structure and its effects on patterns of human NRY polymorphism. First, we sequence 26.5 kb of noncoding Y chromosome DNA from 92 globally distributed males representing 35 populations. We find that the number of polymorphisms with singleton variants is positively correlated with the number of populations sampled and that there is a significant negative correlation of Tajima's D (TD) and Fu and Li's D (FD) statistics with the number of pooled populations. We then sequence the same region in a total of 73 males sampled from 3 distinct populations and find that TD and FD values for the 3 pooled and individual population samples were much less negative than those in the aforementioned global sample. Coalescent simulations show that a simple splitting model of population structure, with no changes in population size, is sufficient to produce the negative values of TD seen in our pooled samples. These empirical and simulation results suggest that observed levels of NRY population structure may lead to an upward bias in the number of singleton variants in global surveys and call into question inferences of population expansion based on global sampling strategies.     

The antimicrobial activity of four commercial essential oils in combination with conventional antimicrobials

Aims:  Due to the emergence of multi-drug resistance, alternatives to conventional antimicrobial therapy are needed. This study aims to investigate the in vitro pharmacological interactions between essential oils (considered valuable as natural therapeutic treatments) and conventional antimicrobials (ciprofloxacin/amphotericin B) when used in combination.
Methods and Results:  Interactions of the essential oils ( Melaleuca alternifolia , Thymus vulgaris , Mentha piperita and Rosmarinus officinalis ) when combined with ciprofloxacin against Staphylococcus aureus indicate mainly antagonistic profiles. When tested against Klebsiella pneumoniae the isobolograms show antagonistic, synergistic and additive interactions depending on the combined ratio. The R. officinalis/ ciprofloxacin combination against K. pneumoniae displayed the most favourable synergistic pattern. The interactions of M. alternifolia (tea tree), T.   vulgaris (thyme), M. piperita (peppermint) and R. officinalis (rosemary) essential oils with amphotericin B indicate mainly antagonistic profiles when tested against Candida albicans.
Conclusion:  While a number of interactions show complete antagonism, others show varied (synergistic, additive and/or antagonistic) interactions, thus the efficacy is dependent on the ratio in which the two components co-exist.
Significance and Impact of the Study:  The predominant antagonistic interactions noted here, suggests that some natural therapies containing essential oils should be used with caution when combined with antibiotics.     

Informative Dorfman screening

Since the early 1940s, group testing (pooled testing) has been used to reduce costs in a variety of applications, including infectious disease screening, drug discovery, and genetics. In such applications, the goal is often to classify individuals as positive or negative using initial group testing results and the subsequent process of decoding of positive pools. Many decoding algorithms have been proposed, but most fail to acknowledge, and to further exploit, the heterogeneous nature of the individuals being screened. In this article, we use individuals' risk probabilities to formulate new informative decoding algorithms that implement Dorfman retesting in a heterogeneous population. We introduce the concept of "thresholding" to classify individuals as "high" or "low risk," so that separate, risk-specific algorithms may be used, while simultaneously identifying pool sizes that minimize the expected number of tests. When compared to competing algorithms which treat the population as homogeneous, we show that significant gains in testing efficiency can be realized with virtually no loss in screening accuracy. An important additional benefit is that our new procedures are easy to implement. We apply our methods to chlamydia and gonorrhea data collected recently in Nebraska as part of the Infertility Prevention Project.     

Facilitative and antagonistic interactions between plant viruses in mixed infections

Mixed infections of plant viruses are common in nature, and a number of important virus diseases of plants are the outcomes of interactions between causative agents. Multiple infections lead to a variety of intrahost virus-virus interactions, many of which may result in the generation of variants showing novel genetic features, and thus change the genetic structure of the viral population. Hence, virus-virus interactions in plants may be of crucial significance for the understanding of viral pathogenesis and evolution, and consequently for the development of efficient and stable control strategies. The interactions between plant viruses in mixed infections are generally categorized as synergistic or antagonistic. Moreover, mixtures of synergistic and antagonistic interactions, creating usually unpredictable biological and epidemiological consequences, are likely to occur in plants. The mechanisms of some of these are still unknown. This review aims to bring together the current knowledge on the most commonly occurring facilitative and antagonistic interactions between related or unrelated viruses infecting the same host plant. The best characterized implications of these interactions for virus-vector-host relationships are included. The terms 'synergism' and 'helper dependence' for facilitative virus-virus interactions, and 'cross-protection' and 'mutual exclusion' for antagonistic interactions, are applied in this article.     

Antagonistic and Synergistic Interactions Among Predators

The structure and dynamics of food webs are largely dependent upon interactions among consumers and their resources. However, interspecific interactions such as intraguild predation and interference competition can also play a significant role in the stability of communities. The role of antagonistic/synergistic interactions among predators has been largely ignored in food web theory. These mechanisms influence predation rates, which is one of the key factors regulating food web structure and dynamics, thus ignoring them can potentially limit understanding of food webs. Using nonlinear models, it is shown that critical aspects of multiple predator food web dynamics are antagonistic/synergistic interactions among predators. The influence of antagonistic/synergistic interactions on coexistence of predators depended largely upon the parameter set used and the degree of feeding niche differentiation. In all cases when there was no effect of antagonism or synergism (a _ij =1.00), the predators coexisted. Using the stable parameter set, coexistence occurred across the range of antagonism/synergism used. However, using the chaotic parameter strong antagonism resulted in the extinction of one or both species, while strong synergism tended to coexistence. Whereas using the limit cycle parameter set, coexistence was strongly dependent on the degree of feeding niche overlap. Additionally increasing the degree of feeding specialization of the predators on the two prey species increased the amount of parameter space in which coexistence of the two predators occurred. Bifurcation analyses supported the general pattern of increased stability when the predator interaction was synergistic and decreased stability when it was antagonistic. Thus, synergistic interactions should be more common than antagonistic interactions in ecological systems.     

In silico predicted robustness of viroid RNA secondary structures. II. Interaction between mutation pairs

Viroids are plant subviral pathogens whose genomes are constituted by a single-stranded and covalently closed small RNA molecule that does not encode for any protein. Most of the 29 described viroid species fold into a rodlike or quasi-rodlike structure, whereas a few of them fold as highly branched structures. In a previous study, we used RNA thermodynamic secondary structure prediction algorithms to compare the mutational robustness of all viroid species. Here we used the same approach to explore the sign and strength of epistasis among pairs of random mutations. We found that antagonistic interactions were more abundant than synergistic ones. However, despite their lower frequency, synergistic interactions tended to be more intense. Mutational robustness and the intensity of epistasis were correlated such that viroid species with large average mutational effects showed stronger antagonistic epistasis, whereas viroids with mild average mutational effects showed weaker antagonistic interactions. The strength of antagonistic epistasis decreased with genome complexity as a consequence of the gained robustness of duplicated genomes. In good agreement with our previous finding of an evolutionary trend toward increased robustness, we now found a trend toward reduced antagonistic epistasis.     

A simulation study comparing the impact of experimental error on the performance of experimental designs and artificial neural networks used for process screening

Many variables and their interactions can affect a biotechnological process. Testing a large number of variables and all their possible interactions is a cumbersome task and its cost can be prohibitive. Several screening strategies, with a relatively low number of experiments, can be used to find which variables have the largest impact on the process and estimate the magnitude of their effect. One approach for process screening is the use of experimental designs, among which fractional factorial and Plackett–Burman designs are frequent choices. Other screening strategies involve the use of artificial neural networks (ANNs). The advantage of ANNs is that they have fewer assumptions than experimental designs, but they render black-box models (i.e., little information can be extracted about the process mechanics). In this paper, we simulate a biotechnological process (fed-batch growth of bakers yeast) to analyze and compare the effect of random experimental errors of different magnitudes and statistical distributions on experimental designs and ANNs. Except for the situation in which the error has a normal distribution and the standard deviation is constant, it was not possible to determine a clear-cut rule for favoring one screening strategy over the other. Instead, we found that the data can be better analyzed using both strategies simultaneously.     

Overview of QTL detection in plants and tests for synergistic epistatic interactions

Improvements in the usefulness of QTL analysis arise from better statistical methods applied to the problem, ability to analyze more complex mating designs, and the fitting of less simplified genetic models. Here we review the advantages of different plant mating designs in QTL analysis and conclude that diallel designs have several favorable properties. We then turn to the detection of systematic genome-wide synergistic epistasis. This form of epistasis has important implications from evolutionary (maintenance of sexual reproduction and concealment of cryptic genetic variation) and practical perspectives (response to pyramided favorable alleles). We develop two methods for detecting systematic synergistic epistasis, one based on analyzing interactions between locus effects and predicted individual genotypic values and one based on analyzing pairwise locus interactions. Using the first method we detect synergistic epistasis in a barley and a wheat dataset but not in a maize dataset. We fail to detect synergistic epistasis with the second method. We discuss our results in the light of theoretical questions concerning the mechanisms of synergistic epistasis.