Emerging roles of the Protein Phosphatase 1 (PP1) in the context of viral infections.

Protein Phosphatase 1 (PP1) is a major serine/threonine phosphatase in eukaryotes, participating in several cellular processes and metabolic pathways. Due to their low substrate specificity, PP1's catalytic subunits do not exist as free entities but instead bind to Regulatory Interactors of Protein Phosphatase One (RIPPO), which regulate PP1's substrate specificity and subcellular localization. Most RIPPOs bind to PP1 through combinations of short linear motifs (4-12 residues), forming highly specific PP1 holoenzymes. These PP1-binding motifs may, hence, represent attractive targets for the development of specific drugs that interfere with a subset of PP1 holoenzymes. Several viruses exploit the host cell protein (de)phosphorylation machinery to ensure efficient virus particle formation and propagation. While the role of many host cell kinases in viral life cycles has been extensively studied, the targeting of phosphatases by viral proteins has been studied in less detail. Here, we compile and review what is known concerning the role of PP1 in the context of viral infections and discuss how it may constitute a putative host-based target for the development of novel antiviral strategies.

Hands-on cybersecurity training behavior data for process mining.

The research on using process mining in learning analytics of cybersecurity exercises relies on datasets that reflect the real behavior of trainees. Although modern cyber ranges, in which training sessions are organized, can collect behavioral data in the form of event logs, the organization of such exercises is laborious. Moreover, the collected raw data has to be processed and transformed into a specific format required by process mining techniques. We present two datasets with slightly different characteristics. While the first exercise with 52 participants was not limited in time, the second supervised exercise with 42 trainees lasted two hours. Also, the cybersecurity tasks were slightly different. A total of 11757 events were collected. Of these, 3597 were training progress events, 5669 were Bash commands, and 2491 were Metasploit commands. Joint CSV files distilled from the raw event data can be used as input for existing process mining tools.

A mathematic equation derived from host-pathogen interactions elucidates the significance of integrating modern medicine with traditional Chinese medicine to treat infectious diseases.

The prognosis of infectious diseases is determined by host-pathogen interactions. Control of pathogens has been the central dogma of treating infectious diseases in modern medicine, but the pathogen-directed medicine is facing significant challenges, including a lack of effective antimicrobials for newly emerging pathogens, pathogen drug resistance, and drug side effects. Here, a mathematic equation (termed equation of host-pathogen interactions, HPI-Equation) is developed to dissect the key variables of host-pathogen interactions. It shows that control of pathogens does not necessarily lead to host recovery. Instead, a combination of promoting a host's power of self-healing and balancing immune responses provides the best benefit for host. Moreover, the HPI-Equation elucidates the scientific basis of traditional Chinese medicine (TCM), a host-based medicine that treats infectious diseases by promoting self-healing power and balancing immune responses. The importance of self-healing power elucidated in the HPI-Equation is confirmed by recent studies that the tolerance mechanism, which is discovered in plants and animals and conceptually similar to self-healing power, improves host survival without directly attacking pathogens. In summary, the HPI-Equation describes host-pathogen interactions with mathematical logic and precision; it translates the ancient wisdoms of TCM into apprehensible modern sciences and opens a new venue for integrating TCM and modern medicine for a future medicine. Sun J. A mathematic equation derived from host-pathogen interactions elucidates the significance of integrating modern medicine with traditional Chinese medicine to treat infectious diseases. J Integr Med. 2023; 21(4):324-331.

Discovery of Chalcone-Based Hybrid Structures as High Affinity and Site-Specific Inhibitors against SARS-CoV-2: A Comprehensive Structural Analysis Based on Various Host-Based and Viral Targets.

Previous studies indicated that natural-based chalcones have significant inhibitory effects on the coronavirus enzymes 3CLpro and PLpro as well as modulation of some host-based antiviral targets (HBATs). In this study, a comprehensive computational and structural study was performed to investigate the affinity of our compound library consisting of 757 chalcone-based structures (CHA-1 to CHA-757) for inhibiting the 3CLpro and PLpro enzymes and against twelve selected host-based targets. Our results indicated that CHA-12 (VUF 4819) is the most potent and multi-target inhibitor in our chemical library over all viral and host-based targets. Correspondingly, CHA-384 and its congeners containing ureide moieties were found to be potent and selective 3CLpro inhibitors, and benzotriazole moiety in CHA-37 was found to be a main fragment for inhibiting the 3CLpro and PLpro. Surprisingly, our results indicate that the ureide and sulfonamide moieties are integral fragments for the optimum 3CLpro inhibition while occupying the S1 and S3 subsites, which is fully consistent with recent reports on the site-specific 3CLpro inhibitors. Finding the multi-target inhibitor CHA-12, previously reported as an LTD4 antagonist for the treatment of inflammatory pulmonary diseases, prompted us to suggest it as a concomitant agent for relieving respiratory symptoms and suppressing COVID-19 infection.

A mathematic equation derived from host-pathogen interactions elucidates the significance of integrating modern medicine with traditional Chinese medicine to treat infectious diseases / 中西医结合学报

The prognosis of infectious diseases is determined by host-pathogen interactions. Control of pathogens has been the central dogma of treating infectious diseases in modern medicine, but the pathogen-directed medicine is facing significant challenges, including a lack of effective antimicrobials for newly emerging pathogens, pathogen drug resistance, and drug side effects. Here, a mathematic equation (termed equation of host-pathogen interactions, HPI-Equation) is developed to dissect the key variables of host-pathogen interactions. It shows that control of pathogens does not necessarily lead to host recovery. Instead, a combination of promoting a host's power of self-healing and balancing immune responses provides the best benefit for host. Moreover, the HPI-Equation elucidates the scientific basis of traditional Chinese medicine (TCM), a host-based medicine that treats infectious diseases by promoting self-healing power and balancing immune responses. The importance of self-healing power elucidated in the HPI-Equation is confirmed by recent studies that the tolerance mechanism, which is discovered in plants and animals and conceptually similar to self-healing power, improves host survival without directly attacking pathogens. In summary, the HPI-Equation describes host-pathogen interactions with mathematical logic and precision; it translates the ancient wisdoms of TCM into apprehensible modern sciences and opens a new venue for integrating TCM and modern medicine for a future medicine. Sun J. A mathematic equation derived from host-pathogen interactions elucidates the significance of integrating modern medicine with traditional Chinese medicine to treat infectious diseases. J Integr Med. 2023; 21(4):324-331.

Pediatric Tuberculosis Diagnostics: Present and Future.

The current diagnostic abilities for the detection of pediatric tuberculosis are suboptimal. Multiple factors contribute to the under-diagnosis of intrathoracic tuberculosis in children, namely the absence of pathognomonic features of the disease, low bacillary loads in respiratory specimens, challenges in sample collection, and inadequate access to diagnostic tools in high-burden settings. Nonetheless, the 2020s have witnessed encouraging progress in the area of novel diagnostics. Recent WHO-endorsed rapid molecular assays hold promise for use in service decentralization strategies, and new policy recommendations include stools as an alternative, child-friendly specimen for testing with the GeneXpert assay. The pipeline of promising assays in mid/late-stage development is expanding, and novel pediatric candidate biomarkers based on the host immune response are being identified for use in diagnostic and triage tests. For a new test to meet the pediatric target product profiles prioritized by the WHO, it is key that the peculiarities and needs of the hard-to-reach pediatric population are considered in the early planning phases of discovery, validation, and implementation studies.

Encrypted Web traffic dataset: Event logs and packet traces.

We present a dataset that captures seven days of monitoring data from eight servers hosting more than 800 sites across a large campus network. The dataset contains data from network monitoring and host-based monitoring. The first set of data are packet traces collected by a probe situated on the network link in front of the web servers. The traces contain encrypted HTTP over TLS 1.2 communication between clients and web servers. The second set of data is an event log captured directly on the web servers. The events are generated by the Internet Information Services (IIS) logging and include both the IIS default features and custom features, such as client port and transferred data volume. Anonymization of all features in the dataset has been carefully carried out to prevent private information leakage while preserving the information value of the dataset. The dataset is suitable mainly for training machine learning techniques for anomaly detection and the identification of relationships between network traffic and events on web servers. We also add tools, settings, and a guide to convert the packet traces to IP flows that are often preferred for network traffic analysis.

Current Potential Therapeutic Approaches against SARS-CoV-2: A Review.

The ongoing SARS-CoV-2 pandemic is a serious threat to public health worldwide and, to date, no effective treatment is available. Thus, we herein review the pharmaceutical approaches to SARS-CoV-2 infection treatment. Numerous candidate medicines that can prevent SARS-CoV-2 infection and replication have been proposed. These medicines include inhibitors of serine protease TMPRSS2 and angiotensin converting enzyme 2 (ACE2). The S protein of SARS-CoV-2 binds to the receptor in host cells. ACE2 inhibitors block TMPRSS2 and S protein priming, thus preventing SARS-CoV-2 entry to host cells. Moreover, antiviral medicines (including the nucleotide analogue remdesivir, the HIV protease inhibitors lopinavir and ritonavir, and wide-spectrum antiviral antibiotics arbidol and favipiravir) have been shown to reduce the dissemination of SARS-CoV-2 as well as morbidity and mortality associated with COVID-19.

Dataset of shell commands used by participants of hands-on cybersecurity training.

We present a dataset of 13446 shell commands from 175 participants who attended cybersecurity training and solved assignments in the Linux terminal. Each acquired data record contains a command with its arguments and metadata, such as a timestamp, working directory, and host identification in the emulated training infrastructure. The commands were captured in Bash, ZSH, and Metasploit shells. The data are stored as JSON records, enabling vast possibilities for their further use in research and development. These include educational data mining, learning analytics, student modeling, and evaluating machine learning models for intrusion detection. The data were collected from 27 cybersecurity training sessions using an open-source logging toolset and two open-source interactive learning environments. Researchers and developers may use the dataset or deploy the learning environments with the logging toolset to generate their own data in the same format. Moreover, we provide a set of common analytical queries to facilitate the exploratory analysis of the dataset.

Host-Directed FDA-Approved Drugs with Antiviral Activity against SARS-CoV-2 Identified by Hierarchical In Silico/In Vitro Screening Methods.

The unprecedent situation generated by the COVID-19 global emergency has prompted us to actively work to fight against this pandemic by searching for repurposable agents among FDA approved drugs to shed light into immediate opportunities for the treatment of COVID-19 patients. In the attempt to proceed toward a proper rationalization of the search for new antivirals among approved drugs, we carried out a hierarchical in silico/in vitro protocol which successfully combines virtual and biological screening to speed up the identification of host-directed therapies against COVID-19 in an effective way. To this end a multi-target virtual screening approach focused on host-based targets related to viral entry, followed by the experimental evaluation of the antiviral activity of selected compounds, has been carried out. As a result, five different potentially repurposable drugs interfering with viral entry-cepharantine, clofazimine, metergoline, imatinib and efloxate-have been identified.

Advances in host-based screening for compounds with intracellular anti-mycobacterial activity.

Intracellular pathogens interact with host systems in intimate ways to sustain a pathogenic lifestyle. Consequently, these interactions can potentially be targets of host-directed interventions against infectious diseases. In case of tuberculosis (TB), caused by the bacterium Mycobacterium tuberculosis (Mtb), while effective anti-tubercular compounds are available, the long treatment duration and emerging drug resistance necessitate identification of new class of molecules with anti-TB activity, as well as new treatment strategies. A significant part of the effort in finding new anti-TB drugs is focused on bacterial targets in bacterial systems. However, the host environment plays a major role in pathogenesis mechanisms and must be considered actively in these efforts. On the one hand, the bacterial origin targets must be relevant and accessible in the host, while on the other hand, new host origin targets required for the bacterial survival can be targeted. Such targets are good candidates for host-directed therapeutics, a strategy gaining traction as an adjunct in TB treatment. In this review, we will summarise the screening platforms used to identify compounds with anti-tubercular activities inside different host environments and outline recent technical advances in these platforms. Finally, while the examples given are specific to mycobacteria, the methods and principles outlined are broadly applicable to most intracellular infections.

An overview of potential therapeutic agents to treat COVID-19.

The emerging novel coronavirus disease (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has swept across the world and become a global threat to public health. More than 200 countries and territories worldwide are suffering from this COVID-19 pandemic. Worryingly, no specific vaccines or drugs have been approved for the prevention or treatment of COVID-19. Under the pressure of a sustained rise in the incidence and mortality of COVID-19, an unprecedented global effort is being implemented to identify effective drugs to combat the current coronavirus. As the understanding of SARS-CoV-2 virology, the underlying mechanism by which it attacks host cells, and the host response to the infection rapidly evolves, drugs are being repurposed and novel drugs are being identified and designed to target the SARS-CoV-2 pathogenesis. Presented here is a brief overview of both virus-based and host-based potential therapeutic drugs that are currently being investigated.

Developing Novel Host-Based Therapies Targeting Microbicidal Responses in Macrophages and Neutrophils to Combat Bacterial Antimicrobial Resistance.

Antimicrobial therapy has provided the main component of chemotherapy against bacterial pathogens. The effectiveness of this strategy has, however, been increasingly challenged by the emergence of antimicrobial resistance which now threatens the sustained utility of this approach. Humans and animals are constantly exposed to bacteria and have developed effective strategies to control pathogens involving innate and adaptive immune responses. Impaired pathogen handling by the innate immune system is a key determinant of susceptibility to bacterial infection. However, the essential components of this response, specifically those which are amenable to re-calibration to improve host defense, remain elusive despite extensive research. We provide a mini-review focusing on therapeutic targeting of microbicidal responses in macrophages and neutrophils to de-stress reliance on antimicrobial therapy. We highlight pre-clinical and clinical data pointing toward potential targets and therapies. We suggest that developing focused host-directed therapeutic strategies to enhance "pauci-inflammatory" microbial killing in myeloid phagocytes that maximizes pathogen clearance while minimizing the harmful consequences of the inflammatory response merits particular attention. We also suggest the importance of One Health approaches in developing host-based approaches through model development and comparative medicine in informing our understanding of how to deliver this strategy.

Perspectives for repurposing drugs for the coronavirus disease 2019.

The newly emerged 2019 novel coronavirus (CoV), named as severe acute respiratory syndrome CoV-2 (SARS-CoV-2), like SARS-CoV (now, SARS-CoV-1) and Middle East respiratory syndrome CoV (MERS-CoV), has been associated with high infection rates with over 36,405 deaths. In the absence of approved marketed drugs against coronaviruses, the treatment and management of this novel CoV disease (COVID-19) worldwide is a challenge. Drug repurposing that has emerged as an effective drug discovery approach from earlier approved drugs could reduce the time and cost compared to de novo drug discovery. Direct virus-targeted antiviral agents target specific nucleic acid or proteins of the virus while host-based antivirals target either the host innate immune responses or the cellular machineries that are crucial for viral infection. Both the approaches necessarily interfere with viral pathogenesis. Here we summarize the present status of both virus-based and host-based drug repurposing perspectives for coronaviruses in general and the SARS-CoV-2 in particular.

Evaluation of host-based molecular markers for the early detection of human sepsis.

We have identified 24 molecular markers, based on circulating nucleic acids (CNA) originating from the human genome, which in combination can be used in a quantitative real-time PCR (qPCR) assay to identify the presence of human sepsis, starting two to three days before the first clinical signs develop and including patients who meet the SEPSIS-3 criteria. The accuracy was more than 87 % inside of the same patient cohort for which the markers were developed and up to 81 % in blind studies of patient cohorts which were not included in the marker development. As our markers are host-based, they can be used to capture bacterial as well as fungal sepsis, unlike the current PCR-based tests, which require species-specific primer sets for each organism causing human sepsis. Our assay directly uses an aliquot of cell-free blood as the substrate for the PCR reaction, thus allowing to obtain the diagnostic results in three to four hours after the collection of the blood samples.

IoTDS: A One-Class Classification Approach to Detect Botnets in Internet of Things Devices.

Internet of Things (IoT) devices have become increasingly widespread. Despite their potential of improving multiple application domains, these devices have poor security, which can be explored by attackers to build large-scale botnets. In this work, we propose a host-based approach to detect botnets in IoT devices, named IoTDS (Internet of Things Detection System). It relies on one-class classifiers, which model only the legitimate device behaviour for further detection of deviations, avoiding the manual labelling process. The proposed solution is underpinned by a novel agent-manager architecture based on HTTPS, which prevents the IoT device from being overloaded by the training activities. To analyse the device's behaviour, the approach extracts features from the device's CPU utilisation and temperature, memory consumption, and number of running tasks, meaning that it does not make use of network traffic data. To test our approach, we used an experimental IoT setup containing a device compromised by bot malware. Multiple scenarios were made, including three different IoT device profiles and seven botnets. Four one-class algorithms (Elliptic Envelope, Isolation Forest, Local Outlier Factor, and One-class Support Vector Machine) were evaluated. The results show the proposed system has a good predictive performance for different botnets, achieving a mean F1-score of 94% for the best performing algorithm, the Local Outlier Factor. The system also presented a low impact on the device's energy consumption, and CPU and memory utilisation.

Characterization of Coxiella burnetii strains from ruminants in a Galleria mellonella host-based model.

Coxiella burnetii is a small Gram-negative intracellular bacterium and is the causative agent of Q fever, which is a zoonotic disease with a worldwide distribution. Domesticated ruminants are the main reservoir of the disease, but the bacterium is able to infect a wide range of hosts, including humans, arthropods and invertebrates. Virulence studies of Coxiella strains usually require a suitable animal model. However, mammalian models are costly and are associated with many ethical constraints. An alternative infection model using Galleria mellonella has been used to study the virulence of several bacterial as well as fungal pathogens. Moreover, the G. mellonella larvae model has been used to identify virulence genes using phase II C. burnetii strain Nine Mile mutants. In our study we describe its use for the characterization of C. burnetii strains isolated from ruminants.

From prediction to function using evolutionary genomics: human-specific ecotypes of Lactobacillus reuteri have diverse probiotic functions.

The vertebrate gut symbiont Lactobacillus reuteri has diversified into separate clades reflecting host origin. Strains show evidence of host adaptation, but how host-microbe coevolution influences microbial-derived effects on hosts is poorly understood. Emphasizing human-derived strains of L. reuteri, we combined comparative genomic analyses with functional assays to examine variations in host interaction among genetically distinct ecotypes. Within clade II or VI, the genomes of human-derived L. reuteri strains are highly conserved in gene content and at the nucleotide level. Nevertheless, they share only 70-90% of total gene content, indicating differences in functional capacity. Human-associated lineages are distinguished by genes related to bacteriophages, vitamin biosynthesis, antimicrobial production, and immunomodulation. Differential production of reuterin, histamine, and folate by 23 clade II and VI strains was demonstrated. These strains also differed with respect to their ability to modulate human cytokine production (tumor necrosis factor, monocyte chemoattractant protein-1, interleukin [IL]-1ß, IL-5, IL-7, IL-12, and IL-13) by myeloid cells. Microarray analysis of representative clade II and clade VI strains revealed global regulation of genes within the reuterin, vitamin B12, folate, and arginine catabolism gene clusters by the AraC family transcriptional regulator, PocR. Thus, human-derived L. reuteri clade II and VI strains are genetically distinct and their differences affect their functional repertoires and probiotic features. These findings highlight the biological impact of microbe:host coevolution and illustrate the functional significance of subspecies differences in the human microbiome. Consideration of host origin and functional differences at the subspecies level may have major impacts on probiotic strain selection and considerations of microbial ecology in mammalian species.