MaMaDroid: Detecting Android malware by building markov chains of behavioral models (extended version)

As Android has become increasingly popular, so has malware targeting it, thus motivating the research community to propose different detection techniques. However, the constant evolution of the Android ecosystem, and of malware itself, makes it hard to design robust tools that can operate for long periods of time without the need for modifications or costly re-training. Aiming to address this issue, we set to detect malware from a behavioral point of view, modeled as the sequence of abstracted API calls. We introduce MaMaDroid, a static-analysis based system that abstracts app’s API calls to their class, package, or family, and builds a model from their sequences obtained from the call graph of an app as Markov chains. This ensures that the model is more resilient to API changes and the features set is of manageable size. We evaluate MaMaDroid using a dataset of 8.5K benign and 35.5K malicious apps collected over a period of six years, showing that it effectively detects malware (with up to 0.99 F-measure) and keeps its detection capabilities for long periods of time (up to 0.87 F-measure two years after training). We also show that MaMaDroid remarkably overperforms DroidAPIMiner, a state-of-the-art detection system that relies on the frequency of (raw) API calls. Aiming to assess whether MaMaDroid’s effectiveness mainly stems from the API abstraction or from the sequencing modeling, we also evaluate a variant of it that uses frequency (instead of sequences), of abstracted API calls. We find that it is not as accurate, failing to capture maliciousness when trained on malware samples that include API calls that are equally or more frequently used by benign apps

Extracellular non-coding RNA signatures of the metacestode stage of Echinococcus multilocularis

Extracellular RNAs (ex-RNAs) are secreted by cells through different means that may involve association with proteins, lipoproteins or extracellular vesicles (EV). In the context of parasitism, ex-RNAs represent new and exciting communication intermediaries with promising potential as novel biomarkers. In the last years, it was shown that helminth parasites secrete ex-RNAs, however, most work mainly focused on RNA secretion mediated by EV. Ex-RNA study is of special interest in those helminth infections that still lack biomarkers for early and/or follow-up diagnosis, such as echinococcosis, a neglected zoonotic disease caused by cestodes of the genus Echinococcus. In this work, we have characterised the ex-RNA profile secreted by in vitro grown metacestodes of Echinococcus multilocularis, the casuative agent of alveolar echinococcosis. We have used high throughput RNA-sequencing together with RT-qPCR to characterise the ex-RNA profile secreted towards the extra- and intra-parasite milieus in EV-enriched and EV-depleted fractions. We show that a polarized secretion of small RNAs takes place, with microRNAs mainly secreted to the extra-parasite milieu and rRNA- and tRNA-derived sequences mostly secreted to the intra-parasite milieu. In addition, we show by nanoparticle tracking analyses that viable metacestodes secrete EV mainly into the metacestode inner vesicular fluid (MVF); however, the number of nanoparticles in culture medium and MVF increases > 10-fold when metacestodes show signs of tegument impairment. Interestingly, we confirm the presence of host miRNAs in the intra-parasite milieu, implying their internalization and transport through the tegument towards the MVF. Finally, our assessment of the detection of Echinococcus miRNAs in patient samples by RT-qPCR yielded negative results suggesting the tested miRNAs may not be good biomarkers for this disease. A comprehensive study of the secretion mechanisms throughout the life cycle of these parasites will help to understand parasite interaction with the host and also, improve current diagnostic tools

Proteomic analysis of plasma exosomes from cystic echinococcosis patients provides in vivo support for distinct immune response profiles in active vs inactive infection and suggests potential biomarkers

The reference diagnostic method of human abdominal Cystic Echinococcosis (CE) is imaging, particularly ultrasound, supported by serology when imaging is inconclusive. However, current diagnostic tools are neither optimal nor widely available. The availability of a test detecting circulating biomarkers would considerably improve CE diagnosis and cyst staging (active vs inactive), as well as treatments and follow-up of patients. Exosomes are extracellular vesicles involved in intercellular communication, including immune system responses, and are a recognized source of biomarkers. With the aim of identifying potential biomarkers, plasma pools from patients infected by active or inactive CE, as well as from control subjects, were processed to isolate exosomes for proteomic label-free quantitative analysis. Results were statistically processed and subjected to bioinformatics analysis to define distinct features associated with parasite viability. First, a few parasite proteins were identified that were specifically associated with either active or inactive CE, which represent potential biomarkers to be validated in further studies. Second, numerous identified proteins of human origin were common to active and inactive CE, confirming an overlap of several immune response pathways. However, a subset of human proteins specific to either active or inactive CE, and central in the respective protein-protein interaction networks, were identified. These include the Src family kinases Src and Lyn, and the immune-suppressive cytokine TGF-β in active CE, and Cdc42 in inactive CE. The Src and Lyn Kinases were confirmed as potential markers of active CE in totally independent plasma pools. In addition, insights were obtained on immune response profiles: largely consistent with previous evidence, our observations hint to a Th1/Th2/regulatory immune environment in patients with active CE and a Th1/inflammatory environment with a component of the wound healing response in the presence of inactive CE. Of note, our results were obtained for the first time from the analysis of samples obtained in vivo from a well-characterized, large cohort of human subjects

The P-Loop Domain of Yeast Clp1 Mediates Interactions Between CF IA and CPF Factors in Pre-mRNA 3′ End Formation

Cleavage factor IA (CF IA), cleavage and polyadenylation factor (CPF), constitute major protein complexes required for pre-mRNA 3′ end formation in yeast. The Clp1 protein associates with Pcf11, Rna15 and Rna14 in CF IA but its functional role remained unclear. Clp1 carries an evolutionarily conserved P-loop motif that was previously shown to bind ATP. Interestingly, human and archaean Clp1 homologues, but not the yeast protein, carry 5′ RNA kinase activity. We show that depletion of Clp1 in yeast promoted defective 3′ end formation and RNA polymerase II termination; however, cells expressing Clp1 with mutant P-loops displayed only minor defects in gene expression. Similarly, purified and reconstituted mutant CF IA factors that interfered with ATP binding complemented CF IA depleted extracts in coupled in vitro transcription/3′ end processing reactions. We found that Clp1 was required to assemble recombinant CF IA and that certain P-loop mutants failed to interact with the CF IA subunit Pcf11. In contrast, mutations in Clp1 enhanced binding to the 3′ endonuclease Ysh1 that is a component of CPF. Our results support a structural role for the Clp1 P-loop motif. ATP binding by Clp1 likely contributes to CF IA formation and cross-factor interactions during the dynamic process of 3′ end formation

Systematic identification of functional modules and cis-regulatory elements in Arabidopsis thaliana

<p>Abstract</p> <p>Background</p> <p>Several large-scale gene co-expression networks have been constructed successfully for predicting gene functional modules and cis-regulatory elements in Arabidopsis (<it>Arabidopsis thaliana</it>)<it>.</it> However, these networks are usually constructed and analyzed in an <it>ad hoc</it> manner. In this study, we propose a completely parameter-free and systematic method for constructing gene co-expression networks and predicting functional modules as well as cis-regulatory elements.</p> <p>Results</p> <p>Our novel method consists of an automated network construction algorithm, a parameter-free procedure to predict functional modules, and a strategy for finding known cis-regulatory elements that is suitable for consensus scanning without prior knowledge of the allowed extent of degeneracy of the motif. We apply the method to study a large collection of gene expression microarray data in Arabidopsis. We estimate that our co-expression network has ~94% of accuracy, and has topological properties similar to other biological networks, such as being scale-free and having a high clustering coefficient. Remarkably, among the ~300 predicted modules whose sizes are at least 20, 88% have at least one significantly enriched functions, including a few extremely significant ones (ribosome, <it>p</it> < 1E-300, photosynthetic membrane, <it>p</it> < 1.3E-137, proteasome complex, <it>p</it> < 5.9E-126). In addition, we are able to predict cis-regulatory elements for 66.7% of the modules, and the association between the enriched cis-regulatory elements and the enriched functional terms can often be confirmed by the literature. Overall, our results are much more significant than those reported by several previous studies on similar data sets. Finally, we utilize the co-expression network to dissect the promoters of 19 Arabidopsis genes involved in the metabolism and signaling of the important plant hormone gibberellin, and achieved promising results that reveal interesting insight into the biosynthesis and signaling of gibberellin.</p> <p>Conclusions</p> <p>The results show that our method is highly effective in finding functional modules from real microarray data. Our application on Arabidopsis leads to the discovery of the largest number of annotated Arabidopsis functional modules in the literature. Given the high statistical significance of functional enrichment and the agreement between cis-regulatory and functional annotations, we believe our Arabidopsis gene modules can be used to predict the functions of unknown genes in Arabidopsis, and to understand the regulatory mechanisms of many genes.</p

DcE2F, a functional plant E2F-like transcriptional activator from Daucus carota

In animal cells the progression of the cell cycle through G1/S transition and S phase is under the control of the pRB/E2F regulatory pathway. The E2F transcription factors are key activators of genes coding for several regulatory proteins and for enzymes involved in nucleotide and DNA synthesis. In this report we have detected the presence of E2F-like DNA binding activities in carrot nuclear extracts, and we have isolated a carrot cDNA (DcE2F) encoding a plant E2F homologue. The DcE2F gene is expressed in proliferating cells and is induced during the G1/S transition of the cell cycle. Supershift experiments using anti-DcE2F antiserum have confirmed that the DcE2F protein is a component of the carrot E2F-like nuclear activities. DNA binding assays have demonstrated that the DcE2F protein can recognize a canonical E2F cis-element in association with a mammalian DP protein. Furthermore, transactivation assays have revealed that DcE2F is a functional transcription factor that can transactivate, together with a DP partner, an E2F-responsive reporter gene in both plant and mammalian cells