Density estimation for ordinal biological sequences and its applications.

Biological sequences do not come at random. Instead, they appear with particular frequencies that reflect properties of the associated system or phenomenon. Knowing how biological sequences are distributed in sequence space is thus a natural first step toward understanding the underlying mechanisms. Here we propose a new method for inferring the probability distribution from which a sample of biological sequences were drawn for the case where the sequences are composed of elements that admit a natural ordering. Our method is based on Bayesian field theory, a physics-based machine learning approach, and can be regarded as a nonparametric extension of the traditional maximum entropy estimate. As an example, we use it to analyze the aneuploidy data pertaining to gliomas from The Cancer Genome Atlas project. In addition, we demonstrate two follow-up analyses that can be performed with the resulting probability distribution. One of them is to investigate the associations among the sequence sites. This provides us a way to infer the governing biological grammar. The other is to study the global geometry of the probability landscape, which allows us to look at the problem from an evolutionary point of view. It can be seen that this methodology enables us to learn from a sample of sequences about how a biological system or phenomenon in the real world works.

Development of a Novel, Potent, and Selective Sialyltransferase Inhibitor for Suppressing Cancer Metastasis.

Sialyltransferase-catalyzed membrane protein and lipid glycosylation plays a vital role as one of the most abundant post-translational modifications and diversification reactions in eukaryotes. However, aberrant sialylation has been associated with cancer malignancy and metastasis. Sialyltransferases thus represent emerging targets for the development of small molecule cancer drugs. Herein, we report the inhibitory effects of a recently discovered lithocholic acid derivative FCW393 on sialyltransferase catalytic activity, integrin sialyation, cancer-associated signal transduction, MDA-MB-231 and B16F10 cell migration and invasion, and in in vivo studies, on tumor growth, metastasis, and angiogenesis. FCW393 showed effective and selective inhibition of the sialyltransferases ST6GAL1 (IC50 = 7.8 µM) and ST3GAL3 (IC50 = 9.45 µM) relative to ST3GAL1 (IC50 > 400 µM) and ST8SIA4 (IC50 > 100 µM). FCW393 reduced integrin sialylation in breast cancer and melanoma cells dose-dependently and downregulated proteins associated with the integrin-regulated FAK/paxillin and GEF/Rho/ROCK pathways, and with the VEGF-regulated Akt/NFκB/HIF-1α pathway. FCW393 inhibited cell migration (IC50 = 2.6 µM) and invasion in in vitro experiments, and in in vivo studies of tumor-bearing mice, FCW393 reduced tumor size, angiogenesis, and metastatic potential. Based on its demonstrated selectivity, cell permeability, relatively low cytotoxicity (IC50 = 55 µM), and high efficacy, FCW393 shows promising potential as a small molecule experimental tool compound and a lead for further development of a novel cancer therapeutic.

Neuropsychological impairment in primary malignant brain tumor patients with awake craniotomy: a hospital-based registration study.

PURPOSE: Neuroplasticity is an ability to maintain neural circuit function when facing damages. It is one of the reasons that making brain tumors notorious. Therefore, we evaluated the characteristics of patients with primary brain tumors, compared neuropsychological deficits between patients who had awake craniotomy with left- or right-sided tumors, and analyzed the association between white matter tracts and neuropsychological deficits in patients with right-sided tumors. METHODS: Using the registration dataset of Chang Gung Memory Hospital between 2014 and 2020, this study included a total of 698 adult patients who received craniotomy for primary brain tumors (538 of conventional craniotomy; 160 of awake craniotomy). Neuropsychological assessments were arranged in patients as preoperative evaluation for awake craniotomies. RESULTS: A lower proportion of right-sided tumors was noted in patient who had awake craniotomy than those who had conventional craniotomy (33.8% and 51.5%, p < 0.001). In awake craniotomy, 88.7% of patients with left-sided tumors and 77.8% of patients with right-sided tumors had neuropsychological impairment. Patients with left-sided tumors had worse preoperative performance compared to those with right-sided tumors in global function (36.2% and 8.0%, p < 0.001), language domain (57.6% and 22.2%, p < 0.001), and attention (36.0% and 18.5%, p = 0.02). Furthermore, in those with right-sided low-grade gliomas, patients involving pathway of superior longitudinal fasciculus (SLF) I had a higher risk of deficits than those without involvement in verbal memory (p = 0.001, Odd ratio = 11.2, 95% CI = 1.8 ~ 71.4) and visual memory (p = 0.048, Odd ratio = 10.5, 95% CI = 1.0 ~ 111). CONCLUSION: In awake craniotomy, patients with left-sided brain tumors had worse cognitive function than those with right-sided tumors in terms of global function, language, and attention. 77% of patients with right-sided tumors had neuropsychological impairment. Therefore, a comprehensive neuropsychological evaluation and awake craniotomy are necessary for patients with brain tumors.

Higher-order epistasis and phenotypic prediction.

Contemporary high-throughput mutagenesis experiments are providing an increasingly detailed view of the complex patterns of genetic interaction that occur between multiple mutations within a single protein or regulatory element. By simultaneously measuring the effects of thousands of combinations of mutations, these experiments have revealed that the genotype-phenotype relationship typically reflects not only genetic interactions between pairs of sites but also higher-order interactions among larger numbers of sites. However, modeling and understanding these higher-order interactions remains challenging. Here we present a method for reconstructing sequence-to-function mappings from partially observed data that can accommodate all orders of genetic interaction. The main idea is to make predictions for unobserved genotypes that match the type and extent of epistasis found in the observed data. This information on the type and extent of epistasis can be extracted by considering how phenotypic correlations change as a function of mutational distance, which is equivalent to estimating the fraction of phenotypic variance due to each order of genetic interaction (additive, pairwise, three-way, etc.). Using these estimated variance components, we then define an empirical Bayes prior that in expectation matches the observed pattern of epistasis and reconstruct the genotype-phenotype mapping by conducting Gaussian process regression under this prior. To demonstrate the power of this approach, we present an application to the antibody-binding domain GB1 and also provide a detailed exploration of a dataset consisting of high-throughput measurements for the splicing efficiency of human pre-mRNA [Formula: see text] splice sites, for which we also validate our model predictions via additional low-throughput experiments.

Field-theoretic density estimation for biological sequence space with applications to 5' splice site diversity and aneuploidy in cancer.

Density estimation in sequence space is a fundamental problem in machine learning that is also of great importance in computational biology. Due to the discrete nature and large dimensionality of sequence space, how best to estimate such probability distributions from a sample of observed sequences remains unclear. One common strategy for addressing this problem is to estimate the probability distribution using maximum entropy (i.e., calculating point estimates for some set of correlations based on the observed sequences and predicting the probability distribution that is as uniform as possible while still matching these point estimates). Building on recent advances in Bayesian field-theoretic density estimation, we present a generalization of this maximum entropy approach that provides greater expressivity in regions of sequence space where data are plentiful while still maintaining a conservative maximum entropy character in regions of sequence space where data are sparse or absent. In particular, we define a family of priors for probability distributions over sequence space with a single hyperparameter that controls the expected magnitude of higher-order correlations. This family of priors then results in a corresponding one-dimensional family of maximum a posteriori estimates that interpolate smoothly between the maximum entropy estimate and the observed sample frequencies. To demonstrate the power of this method, we use it to explore the high-dimensional geometry of the distribution of 5' splice sites found in the human genome and to understand patterns of chromosomal abnormalities across human cancers.

Benzenesulfonamide Derivatives as Calcium/Calmodulin-Dependent Protein Kinase Inhibitors and Antiviral Agents against Dengue and Zika Virus Infections.

Emerging and resurging mosquito-borne flaviviruses are an important public health challenge. The increased prevalence of dengue virus (DENV) infection has had a significant socioeconomic impact on epidemic countries. The recent outbreak of Zika virus (ZIKV) has created an international public health emergency because ZIKV infection has been linked to congenital defects and Guillain-Barré syndrome. To develop potentially prophylactic antiviral drugs for combating these acute infectious diseases, we have targeted the host calcium/calmodulin-dependent kinase II (CaMKII) for inhibition. By using CaMKII structure-guided inhibitor design, we generated four families of benzenesulfonamide (BSA) derivatives for SAR analysis. Among these substances, N-(4-cycloheptyl-4-oxobutyl)-4-methoxy-N-phenylbenzenesulfonamide (9) showed superior properties as a lead CaMKII inhibitor and antiviral agent. BSA 9 inhibited CaMKII activity with an IC50 value of 0.79 µM and displayed EC50 values of 1.52 µM and 1.91 µM against DENV and ZIKV infections of human neuronal BE(2)C cells, respectively. Notably, 9 significantly reduced the viremia level and increased animal survival time in mouse-challenge models.

Density Estimation on Small Data Sets.

How might a smooth probability distribution be estimated with accurately quantified uncertainty from a limited amount of sampled data? Here we describe a field-theoretic approach that addresses this problem remarkably well in one dimension, providing an exact nonparametric Bayesian posterior without relying on tunable parameters or large-data approximations. Strong non-Gaussian constraints, which require a nonperturbative treatment, are found to play a major role in reducing distribution uncertainty. A software implementation of this method is provided.

Compactness of Neutron Stars.

Recent progress in the determination of both masses and radii of neutron stars is starting to place stringent constraints on the dense matter equation of state. In particular, new theoretical developments together with improved statistical tools seem to favor stellar radii that are significantly smaller than those predicted by models using purely nucleonic equations of state. Given that the underlying equation of state must also account for the observation of 2M⊙ neutron stars, theoretical approaches to the study of the dense matter equation of state are facing serious challenges. In response to this challenge, we compute the underlying equation of state associated with an assumed mass-radius template similar to the "common radius" assumption used in recent studies. Once such a mass-radius template is adopted, the equation of state follows directly from the implementation of Lindblom's algorithm; assumptions on the nature or composition of the dense stellar core are not required. By analyzing mass-radius profiles with a maximum mass consistent with observation and common radii in the 8-11 km range, a lower limit on the stellar radius of a 1.4M⊙ neutron star of RNS≳10.7 km is required to prevent the equation of state from violating causality.