RationaleUnderstanding mechanisms of resistance to M. tuberculosis (M.tb) infection in humans could identify novel therapeutic strategies as it has for other infectious diseases, such as HIV.ObjectivesTo compare the early transcriptional response of M.tb-infected monocytes between Ugandan household contacts of tuberculosis patients who demonstrate clinical resistance to M.tb infection (cases) and matched controls with latent tuberculosis infection.MethodsCases (n = 10) and controls (n = 18) were selected from a long-term household contact study in which cases did not convert their tuberculin skin test (TST) or develop tuberculosis over two years of follow up. We obtained genome-wide transcriptional profiles of M.tb-infected peripheral blood monocytes and used Gene Set Enrichment Analysis and interaction networks to identify cellular processes associated with resistance to clinical M.tb infection.Measurements and main resultsWe discovered gene sets associated with histone deacetylases that were differentially expressed when comparing resistant and susceptible subjects. We used small molecule inhibitors to demonstrate that histone deacetylase function is important for the pro-inflammatory response to in-vitro M.tb infection in human monocytes.ConclusionsMonocytes from individuals who appear to resist clinical M.tb infection differentially activate pathways controlled by histone deacetylase in response to in-vitro M.tb infection when compared to those who are susceptible and develop latent tuberculosis. These data identify a potential cellular mechanism underlying the clinical phenomenon of resistance to M.tb infection despite known exposure to an infectious contact.
15Motivation: The prediction of drug resistance and the identification of its mechanisms in bacteria 16 such as Mycobacterium tuberculosis, the etiological agent of tuberculosis, is a challenging problem. 17 Modern methods based on testing against a catalogue of previously identified mutations often yield 18 poor predictive performance. On the other hand, machine learning techniques have demonstrated 19 high predictive accuracy, but lack interpretability to aid in identifying specific mutations which lead 20 to resistance. We propose a novel technique, inspired by the group testing problem and Boolean 21 compressed sensing, which yields highly accurate predictions and interpretable results at the same 22 time. 23Results: We develop a modified version of the Boolean compressed sensing problem for identifying 24 drug resistance, and implement its formulation as an integer linear program. This allows us to 25 characterize the predictive accuracy of the technique and select an appropriate metric to optimize. 26 A simple adaptation of the problem also allows us to quantify the sensitivity-specificity trade-off of 27 our model under different regimes. We test the predictive accuracy of our approach on a variety 28 of commonly used antibiotics in treating tuberculosis and find that it has accuracy comparable to 29 that of standard machine learning models and points to several genes with previously identified 30 association to drug resistance. 31 Availability: https://github.com/WGS-TB/DrugResistance/tree/RB_learning 32 Contact: hooman_zabeti@sfu.ca 33 34 2012 ACM Subject Classification Applied computing -Life and medical sciences -Computational 35 biology -Molecular sequence analysis 36 1 Introduction 43 Drug resistance is the phenomenon by which an infectious organism (also known as pathogen) 44 develops resistance to one or more drugs that are commonly used in treatment [36]. In 45 this paper we focus our attention on Mycobacterium tuberculosis, the etiological agent of 46 tuberculosis, which is the largest infectious killer in the world today, responsible for over 10 47 million new cases and 2 million deaths every year [37]. 48 The development of resistance to common drugs used in treatment is a serious public health 49 threat, not only in low and middle-income countries, but also in high-income countries where 50 it is particularly problematic in hospital settings [39]. It is estimated that, without the urgent 51 development of novel antimicrobial drugs, the total mortality due to drug resistance will 52 exceed 10 million people a year by 2050, a number exceeding the annual mortality due to 53 cancer today [35]. 54 Existing models for predicting drug resistance from whole-genome sequence (WGS) data 55 broadly fall into two classes. The first, which we refer to as "catalogue methods," involves 56 testing the WGS data of an isolate for the presence of point mutations (typically single-57 nucleotide polymorphisms, or SNPs) associated with known drug resistance. If one or 58more such mutations is identified,...
Motivation Prediction of drug resistance and identification of its mechanisms in bacteria such as Mycobacterium tuberculosis, the etiological agent of tuberculosis, is a challenging problem. Solving this problem requires a transparent, accurate, and flexible predictive model. The methods currently used for this purpose rarely satisfy all of these criteria. On the one hand, approaches based on testing strains against a catalogue of previously identified mutations often yield poor predictive performance; on the other hand, machine learning techniques typically have higher predictive accuracy, but often lack interpretability and may learn patterns that produce accurate predictions for the wrong reasons. Current interpretable methods may either exhibit a lower accuracy or lack the flexibility needed to generalize them to previously unseen data. Contribution In this paper we propose a novel technique, inspired by group testing and Boolean compressed sensing, which yields highly accurate predictions, interpretable results, and is flexible enough to be optimized for various evaluation metrics at the same time. Results We test the predictive accuracy of our approach on five first-line and seven second-line antibiotics used for treating tuberculosis. We find that it has a higher or comparable accuracy to that of commonly used machine learning models, and is able to identify variants in genes with previously reported association to drug resistance. Our method is intrinsically interpretable, and can be customized for different evaluation metrics. Our implementation is available at github.com/hoomanzabeti/INGOT_DR and can be installed via The Python Package Index (Pypi) under ingotdr. This package is also compatible with most of the tools in the Scikit-learn machine learning library.
MicroRNAs (miRNAs) are short non-coding RNAs which bind to mRNAs and regulate their expression. MiRNAs have been found to be associated with initiation and progression of many complex diseases. Investigating miRNAs and their targets can thus help develop new therapies by designing anti-miRNA oligonucleotides. While existing computational approaches can predict miRNA targets, these predictions have low accuracy. In this paper, we propose a two-step approach to refine the results of sequence-based prediction algorithms. The first step, which is based on our previous work, uses an ensemble learning approach that combines multiple existing methods. The second step utilizes support vector machine (SVM) classifiers in one- and two-class modes to infer miRNA-mRNA interactions based on both binding features, as well as network features extracted from gene regulatory network. Experimental results using two real data sets from TCGA indicate that the use of two-class SVM classification significantly improves the precision of miRNA-mRNA prediction.
Testicular cancer is the most common cancer in men aged between 15 and 35 and more than 90% of testicular neoplasms are originated at germ cells. Recent research has shown the impact of microRNAs (miRNAs) in different types of cancer, including testicular germ cell tumor (TGCT). MicroRNAs are small non-coding RNAs which affect the development and progression of cancer cells by binding to mRNAs and regulating their expressions. The identification of functional miRNA-mRNA interactions in cancers, i.e. those that alter the expression of genes in cancer cells, can help delineate post-regulatory mechanisms and may lead to new treatments to control the progression of cancer. A number of sequence-based methods have been developed to predict miRNA-mRNA interactions based on the complementarity of sequences. While necessary, sequence complementarity is, however, not sufficient for presence of functional interactions. Alternative methods have thus been developed to refine the sequence-based interactions using concurrent expression profiles of miRNAs and mRNAs. This study aims to find functional cancer-specific miRNA-mRNA interactions in TGCT. To this end, the sequence-based predicted interactions are first refined using an ensemble learning method, based on two well-known methods of learning miRNA-mRNA interactions, namely, TaLasso and GenMiR++. Additional functional analyses were then used to identify a subset of interactions to be most likely functional and specific to TGCT. The final list of 13 miRNA-mRNA interactions can be potential targets for identifying TGCT-specific interactions and future laboratory experiments to develop new therapies.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.