To model complex systems, individual-based models (IBMs), sometimes called \"agent-based models\" (ABMs), describe a simplification of the system through an adequate representation of the elements. IBMs simulate the actions and interaction of discrete individuals/agents within a system in order to discover the pattern of behavior that comes from these interactions. Examples of individuals/agents in biological systems are individual immune cells and bacteria that act independently with their own unique attributes defined by behavioral rules. In IBMs, each of these agents resides in a spatial environment and interactions are guided by predefined rules. These rules are often simple and can be easily implemented. It is expected that following the interaction guided by these rules we will have a better understanding of agent-agent interaction as well as agent-environment interaction. Stochasticity described by probability distributions must be accounted for. Events that seldom occur such as the accumulation of rare mutations can be easily modeled.Thus, IBMs are able to track the behavior of each individual/agent within the model while also obtaining information on the results of their collective behaviors. The influence of impact of one agent with another can be captured, thus allowing a full representation of both direct and indirect causation on the aggregate results. This means that important new insights can be gained and hypotheses tested.

Mathematical models have been used to study the spread of infectious diseases from person to person. More recently studies are developing within-host modeling which provides an understanding of how pathogens-bacteria, fungi, parasites, or viruses-develop, spread, and evolve inside a single individual and their interaction with the host\'s immune system.Such models have the potential to provide a more detailed and complete description of the pathogenesis of diseases within-host and identify other influencing factors that may not be detected otherwise. Mathematical models can be used to aid understanding of the global antibiotic resistance (ABR) crisis and identify new ways of combating this threat.ABR occurs when bacteria respond to random or selective pressures and adapt to new environments through the acquisition of new genetic traits. This is usually through the acquisition of a piece of DNA from other bacteria, a process called horizontal gene transfer (HGT), the modification of a piece of DNA within a bacterium, or through. Bacteria have evolved mechanisms that enable them to respond to environmental threats by mutation, and horizontal gene transfer (HGT): conjugation; transduction; and transformation. A frequent mechanism of HGT responsible for spreading antibiotic resistance on the global scale is conjugation, as it allows the direct transfer of mobile genetic elements (MGEs). Although there are several MGEs, the most important MGEs which promote the development and rapid spread of antimicrobial resistance genes in bacterial populations are plasmids and transposons. Each of the resistance-spread-mechanisms mentioned above can be modeled allowing us to understand the process better and to define strategies to reduce resistance.

There is an increasing need for new treatment regimens to combat antibiotic-resistant strains of bacteria. Staphylococcus aureus is a clinically important, opportunist pathogen that has developed resistance to a range of antibiotics. The zebrafish larval model of systemic disease has been increasingly utilized to elucidate S. aureus virulence mechanisms and host-pathogen interactions. Here, we outline how this model can be used to investigate the effects of different antibiotics alone and in combination against S. aureus.

Studies during the COVID-19 pandemic showed that children had heightened nasal innate immune responses compared with adults. To evaluate the role of nasal viruses and bacteria in driving these responses, we performed cytokine profiling and comprehensive, symptom-agnostic testing for respiratory viruses and bacterial pathobionts in nasopharyngeal samples from children tested for SARS-CoV-2 in 2021-22 (n = 467). Respiratory viruses and/or pathobionts were highly prevalent (82% of symptomatic and 30% asymptomatic children; 90 and 49% for children <5 years). Virus detection and load correlated with the nasal interferon response biomarker CXCL10, and the previously reported discrepancy between SARS-CoV-2 viral load and nasal interferon response was explained by viral coinfections. Bacterial pathobionts correlated with a distinct proinflammatory response with elevated IL-1β and TNF but not CXCL10. Furthermore, paired samples from healthy 1-year-olds collected 1-2 wk apart revealed frequent respiratory virus acquisition or clearance, with mucosal immunophenotype changing in parallel. These findings reveal that frequent, dynamic host-pathogen interactions drive nasal innate immune activation in children.

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Poxviruses have evolved a wide array of mechanisms to evade the immune response, and we provide an overview of the different immunomodulatory strategies. Poxviruses prevent the recognition of viral DNA that triggers the immune responses and inhibit signaling pathways within the infected cell. A unique feature of poxviruses is the production of secreted proteins that mimic cytokines and cytokine receptors, acting as decoy receptors to neutralize the activity of cytokines and chemokines. The capacity of these proteins to evade cellular immune responses by inhibiting cytokine activation is complemented by poxviruses\' strategies to block natural killer cells and cytotoxic T cells, often through interfering with antigen presentation pathways. Mechanisms that target complement activation are also encoded by poxviruses. Virus-encoded proteins that target immune molecules and pathways play a major role in immune modulation, and their contribution to viral pathogenesis, facilitating virus replication or preventing immunopathology, is discussed.

Hand, foot, and mouth disease (HFMD) is a common infectious disease caused by enterovirus 71 (EV71) that frequently affects children, leading to severe infections in some cases. In general, when infection occurs, the body upregulates inflammatory responses to eliminate pathogenic microorganisms to protect the host from infection. However, EV71 may inhibit host\'s innate immunity to promote virus infection. At present, it is not fully understood how EV71 hijack the host cells for its own replication. Toll-like receptor 4 (TLR4), a natural immune receptor, historically associated with bacterial endotoxin-induced inflammatory responses. However, it is still unclear whether and how TLR4 is altered during EV71 infection. In this study, we observed a reduction in both TLR4 protein and gene transcript levels in RD, GES-1, and Vero cells following EV71 infection, as detected by RT-qPCR, immunofluorescence staining and western blot. Furthermore, we observed that the TLR4 downstream molecules of MYD88, p-NF-κB p65, p-TBK1 and related inflammatory cytokines were also reduced, suggesting that antiviral innate immune and inflammatory response were suppressed. To determine the impact of TLR4 changes on EV71 infection, we interfered EV71-infected RD cells with TLR4 agonist or inhibitor and the results showed that activation of TLR4 inhibited EV71 replication, while inhibition of TLR4 promote EV71 replication. Besides, EV71 replication was also promoted in TLR4 siRNA-transfected and EV71-infected RD cells. This suggests that down-regulation the expression of TLR4 by EV71 can inhibit host immune defense to promote EV71 self-replication. This novel mechanism may be a strategy for EV71 to evade host immunity.

Human T-lymphotropic virus type-1 (HTLV-1) was the first discovered human oncogenic retrovirus, the etiological agent of two serious diseases have been identified as adult T-cell leukaemia/lymphoma malignancy and HTLV-1-associated myelopathy/tropical spastic paraparesis (HAM/TSP), a debilitating chronic neuro-myelopathy. Despite more than 40 years of molecular, histopathological and immunological studies on HTLV-1-associated diseases, the virulence and pathogenicity of this virus are yet to be clarified. The reason why the majority of HTLV-1-infected individuals (∼95%) remain asymptomatic carriers is still unclear. The deterioration of the immune system towards oncogenicity and autoimmunity makes HTLV-1 a natural probe for the study of malignancy and neuro-inflammatory diseases. Additionally, its slow worldwide spreading has prompted public health authorities and researchers, as urged by the WHO, to focus on eradicating HTLV-1. In contrast, neither an effective therapy nor a protective vaccine has been introduced. This comprehensive review focused on the most relevant studies of the neuro-inflammatory propensity of HTLV-1-induced HAM/TSP. Such an emphasis on the virus-host interactions in the HAM/TSP pathogenesis will be critically discussed epigenetically. The findings may shed light on future research venues in designing and developing proper HTLV-1 therapeutics.

Airway microbiota are known to contribute to lung diseases, such as cystic fibrosis (CF), but their contributions to pathogenesis are still unclear. To improve our understanding of host-microbe interactions, we have developed an integrated analytical and bioinformatic mass spectrometry (MS)-based metaproteomics workflow to analyze clinical bronchoalveolar lavage (BAL) samples from people with airway disease. Proteins from BAL cellular pellets were processed and pooled together in groups categorized by disease status (CF vs. non-CF) and bacterial diversity, based on previously performed small subunit rRNA sequencing data. Proteins from each pooled sample group were digested and subjected to liquid chromatography tandem mass spectrometry (MS/MS). MS/MS spectra were matched to human and bacterial peptide sequences leveraging a bioinformatic workflow using a metagenomics-guided protein sequence database and rigorous evaluation. Label-free quantification revealed differentially abundant human peptides from proteins with known roles in CF, like neutrophil elastase and collagenase, and proteins with lesser-known roles in CF, including apolipoproteins. Differentially abundant bacterial peptides were identified from known CF pathogens (e.g., Pseudomonas), as well as other taxa with potentially novel roles in CF. We used this host-microbe peptide panel for targeted parallel-reaction monitoring validation, demonstrating for the first time an MS-based assay effective for quantifying host-microbe protein dynamics within BAL cells from individual CF patients. Our integrated bioinformatic and analytical workflow combining discovery, verification, and validation should prove useful for diverse studies to characterize microbial contributors in airway diseases. Furthermore, we describe a promising preliminary panel of differentially abundant microbe and host peptide sequences for further study as potential markers of host-microbe relationships in CF disease pathogenesis.IMPORTANCEIdentifying microbial pathogenic contributors and dysregulated human responses in airway disease, such as CF, is critical to understanding disease progression and developing more effective treatments. To this end, characterizing the proteins expressed from bacterial microbes and human host cells during disease progression can provide valuable new insights. We describe here a new method to confidently detect and monitor abundance changes of both microbe and host proteins from challenging BAL samples commonly collected from CF patients. Our method uses both state-of-the art mass spectrometry-based instrumentation to detect proteins present in these samples and customized bioinformatic software tools to analyze the data and characterize detected proteins and their association with CF. We demonstrate the use of this method to characterize microbe and host proteins from individual BAL samples, paving the way for a new approach to understand molecular contributors to CF and other diseases of the airway.

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