Associating somatic mutation with clinical outcomes through kernel regression and optimal transport

Somatic mutations in cancer patients are inherently sparse and potentially high dimensional. Cancer patients may share the same set of deregulated biological processes perturbed by different sets of somatically mutated genes. Therefore, when assessing the associations between somatic mutations and clinical outcomes, gene-by-gene analysis is often under-powered because it does not capture the complex disease mechanisms shared across cancer patients. Rather than testing genes one by one, an intuitive approach is to aggregate somatic mutation data of multiple genes to assess their joint association with clinical outcomes. The challenge is how to aggregate such information. Building on the optimal transport method, we propose a principled approach to estimate the similarity of somatic mutation profiles of multiple genes between tumor samples, while accounting for gene–gene similarities defined by gene annotations or empirical mutational patterns. Using such similarities, we can assess the associations between somatic mutations and clinical outcomes by kernel regression. We have applied our method to analyze somatic mutation data of 17 cancer types and identified at least five cancer types, where somatic mutations are associated with overall survival, progression-free interval, or cytolytic activity.     

Functional data analysis with covariate-dependent mean and covariance structures

Functional data analysis has emerged as a powerful tool in response to the ever-increasing resources and efforts devoted to collecting information about response curves or anything that varies over a continuum. However, limited progress has been made with regard to linking the covariance structures of response curves to external covariates, as most functional models assume a common covariance structure. We propose a new functional regression model with covariate-dependent mean and covariance structures. Particularly, by allowing variances of random scores to be covariate-dependent, we identify eigenfunctions for each individual from the set of eigenfunctions that govern the variation patterns across all individuals, resulting in high interpretability and prediction power. We further propose a new penalized quasi-likelihood procedure that combines regularization and B-spline smoothing for model selection and estimation and establish the convergence rate and asymptotic normality of the proposed estimators. The utility of the developed method is demonstrated via simulations, as well as an analysis of the Avon Longitudinal Study of Parents and Children concerning parental effects on the growth curves of their offspring, which yields biologically interesting results.     

Nucleotide Sequence Assessment of Four ORFs of Citrus Tristeza Virus: Evidence of Recombination

Citrus Tristeza Virus (CTV), usually occurs in nature as a mixture of genotypes. Six naturally infected citrus (Citrus sinensis) trees grafted on sour orange rootstock were collected from three citrus growing governorates in Egypt (Sharqia, Qalyubia and Garbia). In this study, RT-PCR, Single-Strand Conformation Polymorphism (SSCP) and nucleotide sequence analysis were used for four independent CTV genomic regions (p65, p18, p20, and p23) to detect and assess the sequence and genetic variabilities among CTV Egyptian isolates. RTPCR products (650 bp) for the CTV p23 gene obtained from the selected isolates were used for the SSCP analysis and DNA sequencing. SSCP patterns of p23 gene for individual isolates yielded different complex haplotype patterns. Nucleotide sequence analysis of p23 region amplified from six isolates under study revealed that p23 shared high nucleotide identity 98.7% with T36 isolate from USA, Florida. Phylogenetic analysis of p23 gene indicated a close evolutionary relationship between all examined isolates and Qaha isolate (T36 isolate group), suggesting that they may have originated from closely related ancestors. Nucleotide sequence analysis of the three genes located on CTV 3′-coterminal overhang, p18, p20 and p65, amplified from isolate A3, Sharqia governorate, revealed that the p18, p65, and p20 genes were related to the T3-KB isolate from South Africa with 99%–100% sequence homology. Phylogenetic relationship analysis for p65, p18 and p20 ORFs clustered the current A3 isolate with T3 genotype group. The recombination analysis identified three of six isolates from Sharqia, and Garbia as potential recombinant for p23 gene. The isolates T36 and T3 were identified as major donors for recombination events in isolate A3. Our results concluded that p23 ORF likely to be as a hotspot region for recombination and originated through recombination event. The current study indicated that recombination is an important factor for the origin of CTV strains in Egypt.     

Bias-reduced estimators of conditional odds ratios in matched case-control studies with unmatched confounding

We study bias-reduced estimators of exponentially transformed parameters in general linear models (GLMs) and show how they can be used to obtain bias-reduced conditional (or unconditional) odds ratios in matched case-control studies. Two options are considered and compared: the explicit approach and the implicit approach. The implicit approach is based on the modified score function where bias-reduced estimates are obtained by using iterative procedures to solve the modified score equations. The explicit approach is shown to be a one-step approximation of this iterative procedure. To apply these approaches for the conditional analysis of matched case-control studies, with potentially unmatched confounding and with several exposures, we utilize the relation between the conditional likelihood and the likelihood of the unconditional logit binomial GLM for matched pairs and Cox partial likelihood for matched sets with appropriately setup data. The properties of the estimators are evaluated by using a large Monte Carlo simulation study and an illustration of a real dataset is shown. Researchers reporting the results on the exponentiated scale should use bias-reduced estimators since otherwise the effects can be under or overestimated, where the magnitude of the bias is especially large in studies with smaller sample sizes.     

Using real-world data to predict health outcomes—The prediction design: Application and sample size planning

The gold standard for investigating the efficacy of a new therapy is a (pragmatic) randomized controlled trial (RCT). This approach is costly, time-consuming, and not always practicable. At the same time, huge quantities of available patient-level control condition data in analyzable format of (former) RCTs or real-world data (RWD) are neglected. Therefore, alternative study designs are desirable. The design presented here consists of setting up a prediction model for determining treatment effects under the control condition for future patients. When a new treatment is intended to be tested against a control treatment, a single-arm trial for the new therapy is conducted. The treatment effect is then evaluated by comparing the outcomes of the single-arm trial against the predicted outcomes under the control condition. While there are obvious advantages of this design compared to classical RCTs (increased efficiency, lower cost, alleviating participants’ fear of being on control treatment), there are several sources of bias. Our aim is to investigate whether and how such a design—the prediction design—may be used to provide information on treatment effects by leveraging external data sources. For this purpose, we investigated under what assumptions linear prediction models could be used to predict the counterfactual of patients precisely enough to construct a test and an appropriate sample size formula for evaluating the average treatment effect in the population of a new study. A user-friendly R Shiny application (available at: https://web.imbi.uni-heidelberg.de/PredictionDesignR/ ) facilitates the application of the proposed methods, while a real-world application example illustrates them.     

Genome-wide identification,classification, and expression profiling of serine esterases and other esterase-related proteins in the tobacco hornworm,Manduca sexta

Serine esterases (SEs) are hydrolases that catalyze the conversion of carboxylic esters into acids and alcohols. Lipases and carboxylesterases constitute two major groups of SEs. Although over a hundred of insect genomes are known, systematic identification and classification of SEs are rarely performed, likely due to large size and complex composition of the gene family in each species. Considering their key roles in lipid metabolism and other physiological processes, we have categorized 144 M. sexta SEs and SE homologs (SEHs), 114 of which contain a motif of GXSXG. Multiple sequence alignment and phylogenetic tree analysis have revealed 39 neutral lipases (NLs), 3 neutral lipase homologs (NLHs), 11 acidic lipases (ALs), 3 acidic lipase homologs (ALHs), a lipase-3, a triglyceride lipase, a monoglyceride lipase, a hormone-sensitive lipase, and a GDSL lipase. Eighty-three carboxylesterase genes encode 29 α-esterases (AEs), 12 AEHs (e.g., SEH4-1–3), 20 feruloyl esterases (FEs), 2 FEHs, 2 β-esterases (BEs), 2 integument esterases (IEs), 1 IEH, 4 juvenile hormone esterases, 2 acetylcholinesterases, gliotactin, 6 neuroligins, neurotactin, and an uncharacteristic esterase homolog. In addition to these GXSXG proteins, we have identified 26 phospholipases and 13 thioesterases. Expression profiling of these genes in specific tissues and stages has provided insights into their functions including digestion, detoxification, hormone processing, neurotransmission, reproduction, and developmental regulation. In summary, we have established a framework of information on SEs and related proteins in M. sexta to stimulate their research in the model species and comparative investigations in agricultural pests or disease vectors.     

DEVELOPMENTAL MUTANTS OF VOLVOX: DOES MUTATION RECREATE THE PATTERNS OF PHYLOGENETIC DIVERSITY?

The nature of the variation which is created by mutation can show how the direction of evolution is constrained by internal biases arising from development and pre-existing design. We have attempted to quantify these biases by measuring eight life history characters in developmental mutants of Volvox carteri. Most of the mutants in our sample were inferior to the wild type, but deviated by less than tenfold from the wild-type mean. Characters differed in mutability, suggesting different levels of canalisation. Most correlations between life history characters among strains were positive, but there was a significant negative correlation between the size and the number of reproductive cells, suggesting an upper limit to the total quantity of germ produced by individuals. The most extreme phenotypes in our sample were very vigorous, showing that not all mutations of large effect are unconditionally deleterious. We investigated the effect of developmental constraints on the course of evolution by comparing the variance and covariance patterns among mutant strains with those among species in the family Volvocaceae. A close correspondence between patterns at these two levels would suggest that pre-existing design has a strong influence on evolution, while little or no correspondence shows the action of selection. The variance generated by mutation was equal to that generated by speciation in the family Volvocaceae, the genus Volvox, or the section Merillosphaera, depending on the character considered. We found that mutation changes the volume of somatic tissue independently of the quantity of germ tissue, so that the interspecific correlation between soma and germ can be attributed to selection. The negative correlation between size and number of germ cells among mutants of V. carteri is also seen among the larger members of the family (Volvox spp.), but not among the smaller members, suggesting a powerful design constraint that may be responsible for the absence of larger forms in the entire group.     

Area, shape and isolation of tropical forest fragments: effects on tree species diversity and implications for conservation

Aim To quantify the influences of forest area, shape and isolation on tree species diversity in Ghana and to compare their significance with the influences of climate (average annual rainfall) and disturbance (fire burn, logging, agriculture). Location The forest zone of southern Ghana, West Africa (between 5 and 8° N). Methods For twenty‐two forest fragments (1) bivariate regression analyses of tree species diversity (number and composition) were employed with forest spatial geometry, climate and disturbance variables. (2) Multivariate regression analyses of tree species number and all seven environmental variables were used to determine the variability in tree species number that could be accounted for by these environmental variables. Results Forest area, shape and isolation accounted for sharply decreasing proportions of variability in tree species diversity. Large forest fragments contained the greatest numbers of tree species and the highest proportions of rare tree species; irregular fragments had high proportions of regenerating, light‐demanding pioneers and mature, animal‐dispersed species and isolated fragments were floristically similar to less isolated fragments. Fire burn and average annual rainfall accounted for small, but nevertheless significant, proportions of variability in tree species diversity. Logging and agriculture were non‐significant variables. Main conclusions (1) Forest area is the most important consideration when planning tropical forest reserves. (2) Management of disturbance should take priority over management of forest shape if higher levels of tree diversity and species quality are to be maintained. (3) If new reserves are to be designated, they should be located within different climatic zones in order to capture a large fraction of the regional biota. (4) Biogeographers have an important role to play in formulating and testing hypotheses at a broad spatial scale and ultimately, informing conservation management within the tropical biome.     

The catchment and climate regulation of pCO2 in boreal lakes

The regulation of surface water pCO₂ was studied in a set of 33 unproductive boreal lakes of different humic content, situated along a latitudinal gradient (57°N to 64°N) in Sweden. The lakes were sampled four times during one year, and analyzed on a wide variety of water chemistry parameters. With only one exception, all lakes were supersaturated with CO₂ with respect to the atmosphere at all sampling occasions. pCO₂ was closely related to the DOC concentration in lakes, which in turn was mainly regulated by catchment characteristics. This pattern was similar along the latitudinal gradient and at different seasons of the year, indicating that it is valid for a variety of climatic conditions within the boreal forest zone. We suggest that landscape characteristics determine the accumulation and subsequent supply of allochthonous organic matter from boreal catchments to lakes, which in turn results in boreal lakes becoming net sources of atmospheric CO₂.