A computational and in silico system level framework was developed to identify and prioritize the antibacterial drug targets in Clostridium botulinum (Clb), the causative agent of flaccid paralysis in humans that can be fatal in 5 to 10% of cases. This disease is difficult to control due to the emergence of drug-resistant pathogenic strains and the only available treatment antitoxin which can target the neurotoxin at the extracellular level and cannot reverse the paralysis. This study framework is based on comprehensive systems-scale analysis of genomic sequence homology and phylogenetic relationships among Clostridium, other infectious bacteria, host and human gut flora. First, the entire 2628-annotated genes of this bacterial genome were categorized into essential, non-essential and virulence genes. The results obtained showed that 39% of essential proteins that functionally interact with virulence proteins were identified, which could be a key to new interventions that may kill the bacteria and minimize the host damage caused by the virulence factors. Second, a comprehensive comparative COGs and blast sequence analysis of these proteins and host proteins to minimize the risks of side effects was carried out. This revealed that 47% of a set of C. botulinum proteins were evolutionary related with Homo sapiens proteins to sort out the non-human homologs. Third, orthology analysis with other infectious bacteria to assess broad-spectrum effects was executed and COGs were mostly found in Clostridia, Bacilli (Firmicutes), and in alpha and beta Proteobacteria. Fourth, a comparative phylogenetic analysis was performed with human microbiota to filter out drug targets that may also affect human gut flora. This reduced the list of candidate proteins down to 131. Finally, the role of these putative drug targets in clostridial biological pathways was studied while subcellular localization of these candidate proteins in bacterial cellular system exhibited that 68% of the proteins were located in the cytoplasm, out of which 6% was virulent. Finally, this framework may serve as a general computational strategy for future drug target identification in infectious diseases.
Studies of toxicity and unintended side effects can lead to improved drug safety and efficacy. One promising form of study comes from molecular systems biology in the form of "systems pharmacology". Systems pharmacology combines data from clinical observation and molecular biology. This approach is new, however, and there are few examples of how it can practically predict adverse reactions (ADRs) from an experimental drug with acceptable accuracy.We have developed a new and practical computational framework to accurately predict ADRs of trial drugs. We combine clinical observation data with drug target data, protein-protein interaction (PPI) networks, and gene ontology (GO) annotations. We use cardiotoxicity, one of the major causes for drug withdrawals, as a case study to demonstrate the power of the framework. Our results show that an in silico model built on this framework can achieve a satisfactory cardiotoxicity ADR prediction performance (median AUC = 0.771, Accuracy = 0.675, Sensitivity = 0.632, and Specificity = 0.789). Our results also demonstrate the significance of incorporating prior knowledge, including gene networks and gene annotations, to improve future ADR assessments.Biomolecular network and gene annotation information can significantly improve the predictive accuracy of ADR of drugs under development. The use of PPI networks can increase prediction specificity and the use of GO annotations can increase prediction sensitivity. Using cardiotoxicity as an example, we are able to further identify cardiotoxicity-related proteins among drug target expanding PPI networks. The systems pharmacology approach that we developed in this study can be generally applicable to all future developmental drug ADR assessments and predictions.
Regression trackers have been shown to perform superiorly in visual tracking. However, existing researches in regression trackers mainly explore deep models for feature extraction, and then use sophisticated architectures for online detection. Such systems should optimize a massive number of trainable parameters. In this paper, we present a simple yet effective visual tracking system, called LiteCNT. Our algorithm only consists of three convolutional layers for the whole tracking process. In addition, a multi-region convolutional operator is introduced for regression output. This idea is simple but powerful as it enables our tracker to capture more details of target object. We further derive an efficient and effective operator to approximate multi-region aggregation.
Diversity of the gut microbiome is associated with higher response rates for cancer patients receiving immunotherapy but has not been investigated in patients receiving radiation therapy. Additionally, current studies investigating the gut microbiome and outcomes in cancer patients may not adjusted for established risk factors. Here, we sought to determine if diversity and composition of the gut microbiome was independently associated with survival in cervical cancer patients receiving chemoradiation. Our study demonstrates that the diversity of gut microbiota is associated with a favorable response to chemoradiation. Additionally, compositional variation among patients correlated with short term and long-term survival. Short term survivor fecal samples were significantly enriched in Porphyromonas, Porphyromonadaceae, and Dialister, whereas long term survivor samples were significantly enriched in Escherichia Shigella, Enterobacteriaceae, and Enterobacteriales. Moreover, analysis of immune cells from cervical tumor brush samples by flow cytometry revealed that patients with a high microbiome diversity had increased tumor infiltration of CD4+ lymphocytes as well as activated subsets of CD4 cells expressing ki67+ and CD69+ over the course of radiation therapy. The modulation of gut microbiota before chemoradiation might provide an alternative way to enhance treatment efficacy and improve treatment outcomes in cervical cancer patients.
Femoral neck fractures are a common traumatic injury. The removal of the internal fixation remains controversial, especially in terms of mechanical stability. Moreover, collapsed necrosis of the femoral head continues to occur after fracture healing. We believe that sclerotic cancellous bone (SCB) formation around the screw is associated with femoral head necrosis. We aimed to compare mechanical features before and after implant removal and determine the effect of SCB formation on stress distribution.Cylindrical cancellous bone sections were collected from a relatively normal region and an SCB region of a necrotic femoral head, and their elastic moduli were measured. Four femoral finite element models were developed: a) femoral neck fracture healing with implants, b) fracture healing without implants, c) sclerosis around the screw with implants, and d) sclerosis around the screw without implants.The maximum von Mises peak stresses of models a and b were 66.643 MPa and 63.76 MPa, respectively, and were concentrated in the upper lateral femur. The main stress was scattered at the lowest screw tail, femoral calcar region, and lateral femur shaft. Moreover, coronal plane strain throughout the screw paths near the femoral head in models a and b was mostly in the range of 1000-3000 με. The maximum stress concentrations in models c and d were located at the lower femoral head and reached 91.199 MPa and 78.019 MPa, respectively.The stresses in the sclerotic model around the cannulated screws are more concentrated on the femoral head than in the healing model without sclerotic bone. The overall stresses in the healing femoral neck fracture model were essentially unchanged before and after removal of the internal fixation.
Blind modulation classification is an important step in implementing cognitive radio networks. The multiple-input multiple-output (MIMO) technique is widely used in military and civil communication systems. Due to the lack of prior information about channel parameters and the overlapping of signals in MIMO systems, the traditional likelihood-based and feature-based approaches cannot be applied in these scenarios directly. Hence, in this paper, to resolve the problem of blind modulation classification in MIMO systems, the time–frequency analysis method based on the windowed short-time Fourier transform was used to analyze the time–frequency characteristics of time-domain modulated signals. Then, the extracted time–frequency characteristics are converted into red–green–blue (RGB) spectrogram images, and the convolutional neural network based on transfer learning was applied to classify the modulation types according to the RGB spectrogram images. Finally, a decision fusion module was used to fuse the classification results of all the receiving antennas. Through simulations, we analyzed the classification performance at different signal-to-noise ratios (SNRs); the results indicate that, for the single-input single-output (SISO) network, our proposed scheme can achieve 92.37% and 99.12% average classification accuracy at SNRs of −4 and 10 dB, respectively. For the MIMO network, our scheme achieves 80.42% and 87.92% average classification accuracy at −4 and 10 dB, respectively. The proposed method greatly improves the accuracy of modulation classification in MIMO networks.
Military and industrial activities have lead to reported release of 2,4-dinitrotoluene (2,4DNT) into soil, groundwater or surface water. It has been reported that 2,4DNT can induce toxic effects on humans and other organisms. However the mechanism of 2,4DNT induced toxicity is still unclear. Although a series of methods for gene network construction have been developed, few instances of applying such technology to generate pathway connected networks have been reported. Microarray analyses were conducted using liver tissue of rats collected 24h after exposure to a single oral gavage with one of five concentrations of 2,4DNT. We observed a strong dose response of differentially expressed genes after 2,4DNT treatment. The most affected pathways included: long term depression, breast cancer regulation by stathmin1, WNT Signaling; and PI3K signaling pathways. In addition, we propose a new approach to construct pathway connected networks regulated by 2,4DNT. We also observed clear dose response pathway networks regulated by 2,4DNT. We developed a new method for constructing pathway connected networks. This new method was successfully applied to microarray data from liver tissue of 2,4DNT exposed animals and resulted in the identification of unique dose responsive biomarkers in regards to affected pathways.
A central focus of clinical proteomics for cancer is to identify protein biomarkers with diagnostic and therapeutic application potential. Network-based analyses have been used in computational disease-related gene prioritisation for several years. The Random Walk Ranking (RWR) algorithm has been successfully applied to prioritising disease-related gene candidates by exploiting global network topology in a Protein–Protein Interaction (PPI) network. Increasing the specificity and sensitivity of biomarkers may require consideration of similar or closely-related disease phenotypes and molecular pathological mechanisms shared across different disease phenotypes. In this paper, we propose a method called Seed-Weighted Random Walk Ranking (SW-RWR) for prioritizing cancer biomarker candidates. This method uses the information of cancer phenotype association to assign to each gene a disease-specific, weighted value to guide the RWR algorithm in a global human PPI network. In a case study of prioritizing leukaemia biomarkers, SW-RWR outperformed a typical local network-based analysis in coverage and also showed better accuracy and sensitivity than the original RWR method (global networkbased analysis). Our results suggest that the tight correlation among different cancer phenotypes could play an important role in cancer biomarker discovery.
Abstract The prototypic cancer-predisposition disease Fanconi Anemia (FA) is identified by biallelic mutations in any one of twenty-three FANC genes. Puzzlingly, inactivation of one Fanc gene alone in mice fails to faithfully model the pleiotropic human disease without additional external stress. Here we find that FA patients frequently display FANC co-mutations. Combining exemplary homozygous hypomorphic Brca2/Fancd1 and Rad51c/Fanco mutations in mice phenocopies human FA with bone marrow failure, rapid death by cancer, cellular cancer-drug hypersensitivity and severe replication instability. These grave phenotypes contrast the unremarkable phenotypes seen in mice with single gene-function inactivation, revealing an unexpected synergism between Fanc mutations. Beyond FA, breast cancer-genome analysis confirms that polygenic FANC tumor-mutations correlate with lower survival, expanding our understanding of FANC genes beyond an epistatic FA-pathway. Collectively, the data establish a polygenic replication stress concept as a testable principle, whereby co-occurrence of a distinct second gene mutation amplifies and drives endogenous replication stress, genome instability and disease.
The interest in indentifying novel biomarkers for early stage breast cancer (BRCA) detection has become grown significantly in recent years. From a view of network biology, one of the emerging themes today is to re-characterize a protein's biological functions in its molecular network. Although many methods have been presented, including network-based gene ranking for molecular biomarker discovery, and graph clustering for functional module discovery, it is still hard to find systems-level properties hidden in disease specific molecular networks. We reconstructed BRCA-related protein interaction network by using BRCA-associated genes/proteins as seeds, and expanding them in an integrated protein interaction database. We further developed a computational framework based on Ant Colony Optimization to rank network nodes. The task of ranking nodes is represented as the problem of finding optimal density distributions of "ant colonies" on all nodes of the network. Our results revealed some interesting systems-level pattern in BRCA-related protein interaction network.
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