Identification of esophageal cancer pathway deviation and construction of a diagnosis model using three kernel genes

The purpose of this study is to better understand the role of interleukin 35 (IL35) in esophageal carcinoma by comparing the mRNA level in Barrett's esophageal mucosa and in matched normal squamous mucosa and to understand how the diagnosis model works with two other genes: hepatocyte nuclear factor 1B (HNF1B) and cAMP responsive element binding protein 3-like 1 (CREB3L1). By comparing carcinoma tissue and normal tissue samples, we extracted all the differentially expressed mRNAs. The bioinformatics analysis resulted in the discovery of three prominent genes. Eventually, the three genes were utilized to train a deep-learning model. An additional wet experiment was conducted to validate the effect of IL35. All the differentially expressed genes were enriched into nine groups, each of which has specific biological functions. Given that the three significant genes HNF1B, CREB3L1, and IL35 as diagnostic features, a deep-learning model was constructed, reaching an accuracy of 93% in the training set and 87% in the test set. Our findings suggest that IL35, along with the other two signatures, can distinguish esophageal tumor samples from normal samples precisely.     

Matching habitat choice promotes species persistence under climate change

Species may survive under contemporary climate change by either shifting their range or adapting locally to the warmer conditions. Theoretical and empirical studies recently underlined that dispersal, the central mechanism behind these responses, may depend on the match between an individuals’ phenotype and local environment. Such matching habitat choice is expected to induce an adaptive gene flow, but it now remains to be studied whether this local process could promote species’ responses to climate change. Here, we investigate this by developing an individual‐based model including either random dispersal or temperature‐dependent matching habitat choice. We monitored population composition and distribution through space and time under climate change. Relative to random dispersal, matching habitat choice induced an adaptive gene flow that lessened spatial range loss during climate warming by improving populations’ viability within the range (i.e. limiting range fragmentation) and by facilitating colonization of new habitats at the cold margin. The model even predicted range contraction under random dispersal but range expansion under optimal matching habitat choice. These benefits of matching habitat choice for population persistence mostly resulted from adaptive immigration decision and were greater for populations with larger dispersal distance and higher emigration probability. We also found that environmental stochasticity resulted in suboptimal matching habitat choice, decreasing the benefits of this dispersal mode under climate change. However population persistence was still better under suboptimal matching habitat choice than under random dispersal. Our results highlight the urgent need to implement more realistic mechanisms of dispersal such as matching habitat choice into models predicting the impacts of ongoing climate change on biodiversity.     

Living with observational data in biological anthropology

The establishment of cause and effect relationships is a fundamental objective of scientific research. Many lines of evidence can be used to make cause–effect inferences. When statistical data are involved, alternative explanations for the statistical relationship need to be ruled out. These include chance (apparent patterns due to random factors), confounding effects (a relationship between two variables because they are each associated with an unmeasured third variable), and sampling bias (effects due to preexisting properties of compared groups). The gold standard for managing these issues is a controlled randomized experiment. In disciplines such as biological anthropology, where controlled experiments are not possible for many research questions, causal inferences are made from observational data. Methods that statisticians recommend for this difficult objective have not been widely adopted in the biological anthropology literature. Issues involved in using statistics to make valid causal inferences from observational data are discussed.     

A varying‐coefficient generalized odds rate model with time‐varying exposure: An application to fitness and cardiovascular disease mortality

Varying‐coefficient models have become a common tool to determine whether and how the association between an exposure and an outcome changes over a continuous measure. These models are complicated when the exposure itself is time‐varying and subjected to measurement error. For example, it is well known that longitudinal physical fitness has an impact on cardiovascular disease (CVD) mortality. It is not known, however, how the effect of longitudinal physical fitness on CVD mortality varies with age. In this paper, we propose a varying‐coefficient generalized odds rate model that allows flexible estimation of age‐modified effects of longitudinal physical fitness on CVD mortality. In our model, the longitudinal physical fitness is measured with error and modeled using a mixed‐effects model, and its associated age‐varying coefficient function is represented by cubic B‐splines. An expectation‐maximization algorithm is developed to estimate the parameters in the joint models of longitudinal physical fitness and CVD mortality. A modified pseudoadaptive Gaussian‐Hermite quadrature method is adopted to compute the integrals with respect to random effects involved in the E‐step. The performance of the proposed method is evaluated through extensive simulation studies and is further illustrated with an application to cohort data from the Aerobic Center Longitudinal Study.     

Prediction analysis for microbiome sequencing data

One goal of human microbiome studies is to relate host traits with human microbiome compositions. The analysis of microbial community sequencing data presents great statistical challenges, especially when the samples have different library sizes and the data are overdispersed with many zeros. To address these challenges, we introduce a new statistical framework, called predictive analysis in metagenomics via inverse regression (PAMIR), to analyze microbiome sequencing data. Within this framework, an inverse regression model is developed for overdispersed microbiota counts given the trait, and then a prediction rule is constructed by taking advantage of the dimension‐reduction structure in the model. An efficient Monte Carlo expectation‐maximization algorithm is proposed for maximum likelihood estimation. The method is further generalized to accommodate other types of covariates. We demonstrate the advantages of PAMIR through simulations and two real data examples.     

The pearl necklace model in protein A chromatography: Molecular mechanisms at the resin interface

Staphylococcal protein A chromatography is an established core technology for monoclonal antibody purification and capture in the downstream processing. MabSelect SuRe involves a tetrameric chain of a recombinant form of the B domain of staphylococcal protein A, called the Z-domain. Little is known about the stoichiometry, binding orientation, or preferred binding. We analyzed small-angle X-ray scattering data of the antibody–protein A complex immobilized in an industrial highly relevant chromatographic resin at different antibody concentrations. From scattering data, we computed the normalized radial density distributions. We designed three-dimensional (3D) models with protein data bank crystallographic structures of an IgG1 (the isoform of trastuzumab, used here; Protein Data Bank: 1HZH) and the staphylococcal protein A B domain (the native form of the recombinant structure contained in MabSelect SuRe resin; Protein Data Bank: 1BDD). We computed different binding conformations for different antibody to protein A stoichiometries (1:1, 2:1, and 3:1) and compared the normalized radial density distributions computed from 3D models with those obtained from the experimental data. In the linear range of the isotherm we favor a 1:1 ratio, with the antibody binding to the outer domains in the protein A chain at very low and high concentrations. In the saturation region, a 2:1 ratio is more likely to occur. A 3:1 stoichiometry is excluded because of steric effects.     

Local and long range potentials for heparin-protein systems for coarse-grained simulations

Heparin belongs to glycosaminoglycans (GAGs), a class of periodic linear anionic polysaccharides, which are functionally important components of the extracellular matrix owing to their interactions with various protein targets. Heparin is known to be involved in many cell signaling processes, while the experimental data available for heparin are significantly more abundant than for other GAGs. At the same time, the length and conformational flexibility of the heparin represent major challenges for its theoretical analysis. Coarse-grained (CG) approaches, which enable us to extend the size- and time-scale by orders of magnitude owing to reduction of system representation, appear, therefore, to be useful in simulating these systems. In this work, by using umbrella-sampling molecular dynamics simulations, we derived and parameterized the CG backbone-local potentials of heparin chains and the orientational potentials for the interactions of heparin with amino acid side chains to be further included in the physics-based Unified Coarse-Grained Model of biological macromolecules. With these potentials, simulations of extracellular matrix processes where both heparin and multiple proteins participate will be possible.     

Dynamic investigation and modeling of the nitrogen cometabolism in Methylococcus capsulatus ( Bath)

The methanotrophic bacterium Methylococcus capsulatus is capable of assimilating methane and oxygen into protein-rich biomass, however, the diverse metabolism of the microorganism also allows for several undesired cometabolic side-reactions to occur. In this study, the ammonia cometabolism in Methylococcus capsulatus is investigated using pulse experiments. Surprisingly Methylococcus capsulatus oxidizes ammonia to nitrate through a yet unknown mechanism and fixes molecular nitrogen even at a high dissolved oxygen tension. The observed phenomena can be modeled using 14 ordinary differential equations and 18 kinetic parameters, of which 6 were revealed by Morris screening to be identifiable from the experimental data. Monte Carlo simulations showed that the model was robust and accurate even with uncertainty in the parameter values as confirmed by statistical error analysis.     

Construction of a sensitive pyrogen-testing cell model by site-specific knock-in of multiple genes

A pyrogen test is crucial for evaluating the safety of drugs and medical equipment, especially those involved in injections. As existing pyrogen tests, including the rabbit pyrogen test, the limulus amoebocyte lysate (LAL) test and the monocyte activation test have limitations, development of new models for pyrogen testing is necessary. Here we develop a sensitive cell model for pyrogen test based on the lipopolysaccharides (LPS) signal pathway. TLR4, MD2, and CD14 play key roles in the LPS-mediated pyrogen reaction. We established a new TLR4/MD2/CD14-specific overexpressing knock-in cell model using the CRISPR/CAS9 technology and homologous recombination to detect LPS. Stimulation of our TLR4/CD14/MD2 knock-in cell line model with LPS leads to the release of the cytokines IL-6 and TNF-alpha, with a detection limit of 0.005 EU/ml, which is greatly lower than the lower limit of 0.015 EU/ml detected by the Tachypleus amebocyte lysate (TAL) assay.