Causal inference in medical records and complementary systems pharmacology for metformin drug repurposing towards dementia

Metformin, a diabetes drug with anti-aging cellular responses, has complex actions that may alter dementia onset. Mixed results are emerging from prior observational studies. To address this complexity, we deploy a causal inference approach accounting for the competing risk of death in emulated clinical trials using two distinct electronic health record systems. In intention-to-treat analyses, metformin use associates with lower hazard of all-cause mortality and lower cause-specific hazard of dementia onset, after accounting for prolonged survival, relative to sulfonylureas. In parallel systems pharmacology studies, the expression of two AD-related proteins, APOE and SPP1, was suppressed by pharmacologic concentrations of metformin in differentiated human neural cells, relative to a sulfonylurea. Together, our findings suggest that metformin might reduce the risk of dementia in diabetes patients through mechanisms beyond glycemic control, and that SPP1 is a candidate biomarker for metformin's action in the brain.

The Transmission, Infection Prevention, and Control during the COVID-19 Pandemic in China: A Retrospective Study

The first wave of COVID-19 in China began in December 2019. The outbreak was quickly and effectively controlled through strict infection prevention and control with multipronged measures. By the end of March 2020, the outbreak had basically ended. Therefore, there are relatively complete and effective infection prevention and control (IPC) processes in China to curb virus transmission. Furthermore, there were two large-scale updates for the daily reports by the National Health Commission of the People’s Republic of China in the early stage of the pandemic. We retrospectively studied the transmission characteristics and IPC of COVID-19 in China. Additionally, we analyzed and modeled the data in the two revisions. We found that most cases were limited to Hubei Province, especially in Wuhan, and the mortality rate was lower in non-Wuhan areas. We studied the two revisions and utilized the proposed transmission model to revise the daily confirmed cases at the beginning of the pandemic in Wuhan. Moreover, we estimated the cases and deaths for the same stage and analyzed the effect of IPC in China. The results show that strong and effective IPC with strict implementation was able to effectively and quickly control the pandemic.

An Adaptive Routing Algorithm Based on Relation Tree in DTN

It is found that nodes in Delay Tolerant Networks (DTN) exhibit stable social attributes similar to those of people. In this paper, an adaptive routing algorithm based on Relation Tree (AR-RT) for DTN is proposed. Each node constructs its own Relation Tree based on the historical encounter frequency, and will adopt different forwarding strategies based on the Relation Tree in the forwarding phase, so as to achieve more targeted forwarding. To further improve the scalability of the algorithm, the source node dynamically controls the initial maximum number of message copies according to its own cache occupancy, which enables the node to make negative feedback to network environment changes. Simulation results show that the AR-RT algorithm proposed in this paper has significant advantages over existing routing algorithms in terms of average delay, average hop count, and message delivery rate.

Bottom-Up Synthesized All-Thermal-Catalyst Aerogels for Heat-Regenerative Air Filtration

Airborne particular matter (PM) pollution is an increasing global issue and alternative sources of filter fibers are now an area of significant focus. Compared with relatively mature hazardous gas treatments, state of the art high-efficiency PM filters still lack thermal decomposition ability for organic PM pollutants, such as soot from coal-fired power plants and waste-combustion incinerators, resulting in frequent replacement, high cost, and second-hand pollution. In this manuscript, we propose a bottom-up synthesis method to make the first all-thermal-catalyst air filter (ATCAF). Self-assembled from ∼50 nm diameter TiO₂ fibers, ATCAF could not only capture the combustion-generated PM pollutants with >99.999% efficiency but also catalyze the complete decomposition of the as-captured hydrocarbon pollutants at high temperature. It has the potential of in situ eliminating the PM pollutants from burning of hydrocarbon materials leveraging the burning heat.

Transcompp: understanding phenotypic plasticity by estimating Markov transition rates for cell state transitions

MOTIVATION

Gradual population-level changes in tissues can be driven by stochastic plasticity, meaning rare stochastic transitions of single-cell phenotype. Quantifying the rates of these stochastic transitions requires time-intensive experiments, and analysis is generally confounded by simultaneous bidirectional transitions and asymmetric proliferation kinetics. To quantify cellular plasticity, we developed Transcompp (Transition Rate ANalysis of Single Cells to Observe and Measure Phenotypic Plasticity), a Markov modeling algorithm that uses optimization and resampling to compute best-fit rates and statistical intervals for stochastic cell-state transitions.

RESULTS

We applied Transcompp to time-series datasets in which purified subpopulations of stem-like or non-stem cancer cells were exposed to various cell culture environments, and allowed to re-equilibrate spontaneously over time. Results revealed that commonly used cell culture reagents hydrocortisone and cholera toxin shifted the cell population equilibrium toward stem-like or non-stem states, respectively, in the basal-like breast cancer cell line MCF10CA1a. In addition, applying Transcompp to patient-derived cells showed that transition rates computed from short-term experiments could predict long-term trajectories and equilibrium convergence of the cultured cell population.

AVAILABILITY AND IMPLEMENTATION

Freely available for download at http://github.com/nsuhasj/Transcompp.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Ambivert degree identifies crucial brain functional hubs and improves detection of Alzheimer's Disease and Autism Spectrum Disorder

Functional modules in the human brain support its drive for specialization whereas brain hubs act as focal points for information integration. Brain hubs are brain regions that have a large number of both within and between module connections. We argue that weak connections in brain functional networks lead to misclassification of brain regions as hubs. In order to resolve this, we propose a new measure called ambivert degree that considers the node's degree as well as connection weights in order to identify nodes with both high degree and high connection weights as hubs. Using resting-state functional MRI scans from the Human Connectome Project, we show that ambivert degree identifies brain hubs that are not only crucial but also invariable across subjects. We hypothesize that nodal measures based on ambivert degree can be effectively used to classify patients from healthy controls for diseases that are known to have widespread hub disruption. Using patient data for Alzheimer's Disease and Autism Spectrum Disorder, we show that the hubs in the patient and healthy groups are very different for both the diseases and deep feedforward neural networks trained on nodal hub features lead to a significantly higher classification accuracy with significantly fewer trainable weights compared to using functional connectivity features. Thus, the ambivert degree improves identification of crucial brain hubs in healthy subjects and can be used as a diagnostic feature to detect neurological diseases characterized by hub disruption.

Deep learning enables automated scoring of liver fibrosis stages

Current liver fibrosis scoring by computer-assisted image analytics is not fully automated as it requires manual preprocessing (segmentation and feature extraction) typically based on domain knowledge in liver pathology. Deep learning-based algorithms can potentially classify these images without the need for preprocessing through learning from a large dataset of images. We investigated the performance of classification models built using a deep learning-based algorithm pre-trained using multiple sources of images to score liver fibrosis and compared them against conventional non-deep learning-based algorithms - artificial neural networks (ANN), multinomial logistic regression (MLR), support vector machines (SVM) and random forests (RF). Automated feature classification and fibrosis scoring were achieved by using a transfer learning-based deep learning network, AlexNet-Convolutional Neural Networks (CNN), with balanced area under receiver operating characteristic (AUROC) values of up to 0.85–0.95 versus ANN (AUROC of up to 0.87–1.00), MLR (AUROC of up to 0.73–1.00), SVM (AUROC of up to 0.69–0.99) and RF (AUROC of up to 0.94–0.99). Results indicate that a deep learning-based algorithm with transfer learning enables the construction of a fully automated and accurate prediction model for scoring liver fibrosis stages that is comparable to other conventional non-deep learning-based algorithms that are not fully automated.

Heterogeneous characters modeling of instant message services users' online behavior

Research on temporal characteristics of human dynamics has attracted much attentions for its contribution to various areas such as communication, medical treatment, finance, etc. Existing studies show that the time intervals between two consecutive events present different non-Poisson characteristics, such as power-law, Pareto, bimodal distribution of power-law, exponential distribution, piecewise power-law, et al. With the occurrences of new services, new types of distributions may arise. In this paper, we study the distributions of the time intervals between two consecutive visits to QQ and WeChat service, the top two popular instant messaging services in China, and present a new finding that when the value of statistical unit T is set to 0.001s, the inter-event time distribution follows a piecewise distribution of exponential and power-law, indicating the heterogeneous character of IM services users' online behavior in different time scales. We infer that the heterogeneous character is related to the communication mechanism of IM and the habits of users. Then we develop a combination model of exponential model and interest model to characterize the heterogeneity. Furthermore, we find that the exponent of the inter-event time distribution of the same service is different in two cities, which is correlated with the popularity of the services. Our research is useful for the application of information diffusion, prediction of economic development of cities, and so on.

A method to quantify co-localization in biological images

Quantitative co-localization analysis with fluorescent microscopy is a common approach to assess the spatial co-ordination of molecules and thus to understand their functions in biological processes. However, the co-localization analysis results might not be consistent due to various imaging conditions and different quantification methods used. We propose a novel method to separate a co-localization event into two aspects: co-occurrence and intensity correlation, which are usually combined as one parameter in other quantitative co-localization analyses. By examining co-localization through both co-occurrence and intensity correlation, the co-localization analysis provides accurate and interpretable results. Furthermore, the co-occurrence pixels can be visualized in an additional image channel to provide an intuitive impression of the quantity and locations of the co-localization events occurring.

Differential remodeling of extracellular matrices by breast cancer initiating cells

Cancer initiating cells (CICs) have been the focus of recent anti-cancer therapies, exhibiting strong invasion capability via potentially enhanced ability to remodel extracellular matrices (ECM). We have identified CICs in a human breast cancer cell line, MX-1, and developed a xenograft model in SCID mice. We investigated the CICs' matrix-remodeling effects using Second Harmonic Generation (SHG) microscopy to identify potential phenotypic signatures of the CIC-rich tumors. The isolated CICs exhibit higher proliferation, drug efflux and drug resistant properties in vitro; were more tumorigenic than non-CICs, resulting in more and larger tumors in the xenograft model. The CIC-rich tumors have less collagen in the tumor interior than in the CIC-poor tumors supporting the idea that the CICs can remodel the collagen more effectively. The collagen fibers were preferentially aligned perpendicular to the CIC-rich tumor boundary while parallel to the CIC-poor tumor boundary suggesting more invasive behavior of the CIC-rich tumors. These findings would provide potential translational values in quantifying and monitoring CIC-rich tumors in future anti-cancer therapies. CIC-rich tumors remodel the collagen matrix more than CIC-poor tumors.

Statistical Evaluation of Causal Factors Associated with Astronaut Shoulder Injury in Space Suits

INTRODUCTION

Shoulder injuries due to working inside the space suit are some of the most serious and debilitating injuries astronauts encounter. Space suit injuries occur primarily in the Neutral Buoyancy Laboratory (NBL) underwater training facility due to accumulated musculoskeletal stress. We quantitatively explored the underlying causal mechanisms of injury.

METHODS

Logistic regression was used to identify relevant space suit components, training environment variables, and anthropometric dimensions related to an increased propensity for space-suited injury. Two groups of subjects were analyzed: those whose reported shoulder incident is attributable to the NBL or working in the space suit, and those whose shoulder incidence began in active duty, meaning working in the suit could be a contributing factor.

RESULTS

For both groups, percent of training performed in the space suit planar hard upper torso (HUT) was the most important predictor variable for injury. Frequency of training and recovery between training were also significant metrics. The most relevant anthropometric dimensions were bideltoid breadth, expanded chest depth, and shoulder circumference. Finally, record of previous injury was found to be a relevant predictor for subsequent injury. The first statistical model correctly identifies 39% of injured subjects, while the second model correctly identifies 68% of injured subjects.

DISCUSSION

A review of the literature suggests this is the first work to quantitatively evaluate the hypothesized causal mechanisms of all space-suited shoulder injuries. Although limited in predictive capability, each of the identified variables can be monitored and modified operationally to reduce future impacts on an astronaut's health.

Abstract 5168: Gastric adenocarcinomas show pervasive loss of heterozygosity and enrichment for “STOP” genes in regions of hemizygous deletion

Purpose: Gastric cancer, the second leading cause of cancer death worldwide, has been little studied compared with other cancers that impose similar burdens on public health. Our goal is to assess loss of heterozygosity (LOH) and hemizygous deletion across a large series of gastric adenocarcinomas using the most sensitive and comprehensive method currently available.

Experimental design: We used high-density single-nucleotide-polymorphism microarrays that assay genotype and copy number at 906,600 sites and analyzed the results to determine patterns of LOH and hemizygous deletion in 77 gastric adenocarcinomas. We investigated whether “STOP” genes - genes that tend to impede proliferation - are associated with hemizygous deletion. We also analyzed the extent to which hemizygous deletion affected CYCLOPS (Copy number alterations Yielding Cancer Liabilities Owing to Partial losS) genes - genes that may be attractive targets for therapeutic inhibition when hemizygously deleted.

Results: We found that LOH is pervasive: on average 27% of each tumor genome is subject to LOH, and > 98% of single nucleotide polymorphisms assayed were subject to LOH in at least 10% of tumors. Furthermore, 26% of LOH was due to hemizygous deletion, and STOP genes were significantly associated with regions of frequent hemizygous deletion; on average, 61 STOP genes were hemizygously deleted per tumor. Furthermore, on average, 4.65 CYCLOPS genes were hemizygously deleted per tumor, and 54.5% of the tumors had at least one CYCLOPS gene deleted.

Conclusions: These findings suggest that hemizygous deletion of anti-proliferative STOP genes contributes substantially to gastric carcinogenesis. Furthermore, the presence of several hemizygously deleted CYLOPS genes in some tumors suggests potential therapeutic targets in these tumors.

Citation Format: Ioana Cutcutache, Alice Yingting Wu, John R. McPherson, Zhengdeng Lei, Niantao Deng, Wai Keong Wong, Khee Chee Soo, Weng Hoong Chan, London Lucien Ooi, Roy E. Welsch, Patrick Tan, Steven G. Rozen. Gastric adenocarcinomas show pervasive loss of heterozygosity and enrichment for “STOP” genes in regions of hemizygous deletion. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 5168. doi:10.1158/1538-7445.AM2014-5168

Neutrophils infected with highly virulent influenza H3N2 virus exhibit augmented early cell death and rapid induction of type I interferon signaling pathways

We developed a model of influenza virus infection of neutrophils by inducing differentiation of the MPRO promyelocytic cell line. After 5 days of differentiation, about 20-30% of mature neutrophils could be detected. Only a fraction of neutrophils were infected by highly virulent influenza (HVI) virus, but were unable to support active viral replication compared with MDCK cells. HVI infection of neutrophils augmented early and late apoptosis as indicated by annexin V and TUNEL assays. Comparison between the global transcriptomic responses of neutrophils to HVI and low virulent influenza (LVI) revealed that the IFN regulatory factor and IFN signaling pathways were the most significantly overrepresented pathways, with activation of related genes in HVI as early as 3 h. Relatively consistent results were obtained by real-time RT-PCR of selected genes associated with the type I IFN pathway. Early after HVI infection, comparatively enhanced expression of apoptosis-related genes was also elicited.

Estimation of the population of neutrophils induced to differentiate from the MPRO mouse promyelocytic cell line

Neutrophils derived from induced-differentiated mouse promyleocyte (MPRO) cell lines provide an alternative source of mouse neutrophils for in vitro experiments, substituting for primary mouse neutrophils that are normally obtained by sacrificing mice. One issue with using induced-differentiated MPRO cells (or NEUTs) is that they are usually composed of not only mature neutrophils, but also neutrophil precursors. Here, we report on an assessment of an automated image analysis system to estimate mature neutrophil proportion in giemsa-stained NEUTs, and compare the accuracy with manual cell counting and flow cytometry results.

Predicting in vivo anti-hepatofibrotic drug efficacy based on in vitro high-content analysis

Background/Aims

Many anti-fibrotic drugs with high in vitro efficacies fail to produce significant effects in vivo. The aim of this work is to use a statistical approach to design a numerical predictor that correlates better with in vivo outcomes.

Methods

High-content analysis (HCA) was performed with 49 drugs on hepatic stellate cells (HSCs) LX-2 stained with 10 fibrotic markers. ∼0.3 billion feature values from all cells in >150,000 images were quantified to reflect the drug effects. A systematic literature search on the in vivo effects of all 49 drugs on hepatofibrotic rats yields 28 papers with histological scores. The in vivo and in vitro datasets were used to compute a single efficacy predictor (E_predict).

Results

We used in vivo data from one context (CCl₄ rats with drug treatments) to optimize the computation of E_predict. This optimized relationship was independently validated using in vivo data from two different contexts (treatment of DMN rats and prevention of CCl₄ induction). A linear in vitro-in vivo correlation was consistently observed in all the three contexts. We used E_predict values to cluster drugs according to efficacy; and found that high-efficacy drugs tended to target proliferation, apoptosis and contractility of HSCs.

Conclusions

The E_predict statistic, based on a prioritized combination of in vitro features, provides a better correlation between in vitro and in vivo drug response than any of the traditional in vitro markers considered.

Differential pulmonary transcriptomic profiles in murine lungs infected with low and highly virulent influenza H3N2 viruses reveal dysregulation of TREM1 signaling, cytokines, and chemokines

Investigating the relationships between critical influenza viral mutations contributing to increased virulence and host expression factors will shed light on the process of severe pathogenesis from the systems biology perspective. We previously generated a mouse-adapted, highly virulent influenza (HVI) virus through serial lung-to-lung passaging of a human influenza H3N2 virus strain that causes low virulent influenza (LVI) in murine lungs. This HVI virus is characterized by enhanced replication kinetics, severe lung injury, and systemic spread to major organs. Our gene microarray investigations compared the host transcriptomic responses of murine lungs to LVI virus and its HVI descendant at 12, 48, and 96 h following infection. More intense expression of genes associated with cytokine activity, type 1 interferon response, and apoptosis was evident in HVI at all time-points. We highlighted dysregulation of the TREM1 signaling pathway (an amplifier of cytokine production) that is likely to be upregulated in infiltrating neutrophils in HVI-infected lungs. The cytokine gene expression changes were corroborated by elevated levels of multiple cytokine and chemokine proteins in the bronchoalveolar lavage fluid of infected mice, especially at 12 h post-infection. Concomitantly, the downregulation of genes that mediate proliferative, developmental, and metabolic processes likely contributed to the lethality of HVI as well as lack of lung repair. Overall, our comparative transcriptomic study provided insights into key host factors that influence the dynamics, pathogenesis, and outcome of severe influenza.

MCAM: multiple clustering analysis methodology for deriving hypotheses and insights from high-throughput proteomic datasets

Advances in proteomic technologies continue to substantially accelerate capability for generating experimental data on protein levels, states, and activities in biological samples. For example, studies on receptor tyrosine kinase signaling networks can now capture the phosphorylation state of hundreds to thousands of proteins across multiple conditions. However, little is known about the function of many of these protein modifications, or the enzymes responsible for modifying them. To address this challenge, we have developed an approach that enhances the power of clustering techniques to infer functional and regulatory meaning of protein states in cell signaling networks. We have created a new computational framework for applying clustering to biological data in order to overcome the typical dependence on specific a priori assumptions and expert knowledge concerning the technical aspects of clustering. Multiple clustering analysis methodology (‘MCAM’) employs an array of diverse data transformations, distance metrics, set sizes, and clustering algorithms, in a combinatorial fashion, to create a suite of clustering sets. These sets are then evaluated based on their ability to produce biological insights through statistical enrichment of metadata relating to knowledge concerning protein functions, kinase substrates, and sequence motifs. We applied MCAM to a set of dynamic phosphorylation measurements of the ERRB network to explore the relationships between algorithmic parameters and the biological meaning that could be inferred and report on interesting biological predictions. Further, we applied MCAM to multiple phosphoproteomic datasets for the ERBB network, which allowed us to compare independent and incomplete overlapping measurements of phosphorylation sites in the network. We report specific and global differences of the ERBB network stimulated with different ligands and with changes in HER2 expression. Overall, we offer MCAM as a broadly-applicable approach for analysis of proteomic data which may help increase the current understanding of molecular networks in a variety of biological problems.

Proteomic measurements, especially modification measurements, are greatly expanding the current knowledge of the state of proteins under various conditions. Harnessing these measurements to understand how these modifications are enzymatically regulated and their subsequent function in cellular signaling and physiology is a challenging new problem. Clustering has been very useful in reducing the dimensionality of many types of high-throughput biological data, as well inferring function of poorly understood molecular species. However, its implementation requires a great deal of technical expertise since there are a large number of parameters one must decide on in clustering, including data transforms, distance metrics, and algorithms. Previous knowledge of useful parameters does not exist for measurements of a new type. In this work we address two issues. First, we develop a framework that incorporates any number of possible parameters of clustering to produce a suite of clustering solutions. These solutions are then judged on their ability to infer biological information through statistical enrichment of existing biological annotations. Second, we apply this framework to dynamic phosphorylation measurements of the ERBB network, constructing the first extensive analysis of clustering of phosphoproteomic data and generating insight into novel components and novel functions of known components of the ERBB network.

PTMScout, a Web resource for analysis of high throughput post-translational proteomics studies

The rate of discovery of post-translational modification (PTM) sites is increasing rapidly and is significantly outpacing our biological understanding of the function and regulation of those modifications. To help meet this challenge, we have created PTMScout, a web-based interface for viewing, manipulating, and analyzing high throughput experimental measurements of PTMs in an effort to facilitate biological understanding of protein modifications in signaling networks. PTMScout is constructed around a custom database of PTM experiments and contains information from external protein and post-translational resources, including gene ontology annotations, Pfam domains, and Scansite predictions of kinase and phosphopeptide binding domain interactions. PTMScout functionality comprises data set comparison tools, data set summary views, and tools for protein assignments of peptides identified by mass spectrometry. Analysis tools in PTMScout focus on informed subset selection via common criteria and on automated hypothesis generation through subset labeling derived from identification of statistically significant enrichment of other annotations in the experiment. Subset selection can be applied through the PTMScout flexible query interface available for quantitative data measurements and data annotations as well as an interface for importing data set groupings by external means, such as unsupervised learning. We exemplify the various functions of PTMScout in application to data sets that contain relative quantitative measurements as well as data sets lacking quantitative measurements, producing a set of interesting biological hypotheses. PTMScout is designed to be a widely accessible tool, enabling generation of multiple types of biological hypotheses from high throughput PTM experiments and advancing functional assignment of novel PTM sites. PTMScout is available at http://ptmscout.mit.edu.

A Cell Profiling Framework for Modeling Drug Responses from HCS Imaging

The authors present an unsupervised, scalable, and interpretable cell profiling framework that is compatible with data gathered from high-content screening. They demonstrate the effectiveness of their framework by modeling drug differential effects of IC-21 macrophages treated with microtubule and actin disrupting drugs. They identify significant features of cell phenotypes for unsupervised learning based on maximum relevancy and minimum redundancy criteria. A 2-stage clustering approach annotates, clusters cells, and then merges them together to form super-clusters. An interpretable cell profile consisting of super-cluster proportions profiled at each drug treatment, concentration, or duration is obtained. Differential changes in super-cluster profiles are the basis for understanding the drug’s differential effect and biology. The authors’ method is validated by significant chi-squared statistics obtained from similar drug-treated super-cluster profiles from a 5-fold cross-validation. In addition, drug profiles of 2 microtubule drugs with equivalent mechanisms of action are statistically similar. Several distinct trends are identified for the 5 cytoskeletal drugs profiled under different conditions.