Toll-Like Receptor Signaling and Its Role in Cell-Mediated Immunity

Visualizing Macrophage Phenotypes and Polarization in Diseases: From Biomarkers to Molecular Probes

Macrophage is a kind of immune cell and performs multiple functions including pathogen phagocytosis, antigen presentation and tissue remodeling. To fulfill their functionally distinct roles, macrophages undergo polarization towards a spectrum of phenotypes, particularly the classically activated (M1) and alternatively activated (M2) subtypes. However, the binary M1/M2 phenotype fails to capture the complexity of macrophages subpopulations in vivo. Hence, it is crucial to employ spatiotemporal imaging techniques to visualize macrophage phenotypes and polarization, enabling the monitoring of disease progression and assessment of therapeutic responses to drug candidates. This review begins by discussing the origin, function and diversity of macrophage under physiological and pathological conditions. Subsequently, we summarize the identified macrophage phenotypes and their specific biomarkers. In addition, we present the imaging probes locating the lesions by visualizing macrophages with specific phenotype in vivo. Finally, we discuss the challenges and prospects associated with monitoring immune microenvironment and disease progression through imaging of macrophage phenotypes.

Pregnancy Complications Are Associated with Premature Coronary Artery Disease: Linking Three Cohorts

Background: There is increasing evidence that women who experience placenta-mediated pregnancy complications and gestational diabetes mellitus (GDM) are at higher risk for the development of coronary artery disease (CAD) later in life. We hypothesized that there is an association between placenta-mediated pregnancy complications, GDM, and risk of premature CAD (PCAD). Methods: This research project involved a data linkage approach merging three databases of South Australian cohorts by using a retrospective, age-matched case-control study design. Cases (n = 721) were ascertained from the Coronary Angiogram Database of South Australia (CADOSA). Women <60 years from CADOSA were linked to South Australian Perinatal Statistics Collection (SAPSC) to ascertain their prior pregnancy outcomes. Controls (n = 194) were selected from North West Adelaide Health Study (NWAHS) and comprised women who were healthy or had other health conditions unrelated to CAD, age-matched to CADOSA (±5 years), and linked to SAPSC to determine their pregnancy outcomes. PCAD was defined as >50% stenosis in one or more coronary arteries at coronary angiography. Results: Compared with women without a history of PCAD, women who were diagnosed with PCAD were more likely to have experienced the placenta-mediated pregnancy complications of preterm birth (adjusted odds ratio [OR] = 2.46, 95% confidence interval [CI]: 1.21-5.00) or low-birth weight (adjusted OR = 2.44, 95% CI: 1.22-4.88), or have been diagnosed with active asthma during pregnancy (adjusted OR = 3.52, 95% CI: 1.05-11.76). Conclusion: Placenta-mediated pregnancy complications should be recognized as clear risk markers for future PCAD.

A robust platform for integrative spatial multi-omics analysis to map immune responses to SARS-CoV-2 infection in lung tissues

The SARS-CoV-2 (COVID-19) virus has caused a devastating global pandemic of respiratory illness. To understand viral pathogenesis, methods are available for studying dissociated cells in blood, nasal samples, bronchoalveolar lavage fluid and similar, but a robust platform for deep tissue characterization of molecular and cellular responses to virus infection in the lungs is still lacking. We developed an innovative spatial multi-omics platform to investigate COVID-19-infected lung tissues. Five tissue-profiling technologies were combined by a novel computational mapping methodology to comprehensively characterize and compare the transcriptome and targeted proteome of virus infected and uninfected tissues. By integrating spatial transcriptomics data (Visium, GeoMx and RNAScope) and proteomics data (CODEX and PhenoImager HT) at different cellular resolutions across lung tissues, we found strong evidence for macrophage infiltration and defined the broader microenvironment surrounding these cells. By comparing infected and uninfected samples, we found an increase in cytokine signalling and interferon responses at different sites in the lung and showed spatial heterogeneity in the expression level of these pathways. These data demonstrate that integrative spatial multi-omics platforms can be broadly applied to gain a deeper understanding of viral effects on cellular environments at the site of infection and to increase our understanding of the impact of SARS-CoV-2 on the lungs.

Clinical manifestations and immune response to tuberculosis

Tuberculosis is a far-reaching, high-impact disease. It is among the top ten causes of death worldwide caused by a single infectious agent; 1.6 million tuberculosis-related deaths were reported in 2021 and it has been estimated that a third of the world's population are carriers of the tuberculosis bacillus but do not develop active disease. Several authors have attributed this to hosts' differential immune response in which cellular and humoral components are involved, along with cytokines and chemokines. Ascertaining the relationship between TB development's clinical manifestations and an immune response should increase understanding of tuberculosis pathophysiological and immunological mechanisms and correlating such material with protection against Mycobacterium tuberculosis. Tuberculosis continues to be a major public health problem globally. Mortality rates have not decreased significantly; rather, they are increasing. This review has thus been aimed at deepening knowledge regarding tuberculosis by examining published material related to an immune response against Mycobacterium tuberculosis, mycobacterial evasion mechanisms regarding such response and the relationship between pulmonary and extrapulmonary clinical manifestations induced by this bacterium which are related to inflammation associated with tuberculosis dissemination through different routes.

A novel prognostic model based on pyroptosis-related genes for multiple myeloma

BACKGROUND

Multiple myeloma (MM) is an incurable and relapse-prone disease with apparently prognostic heterogeneity. At present, the risk stratification of myeloma is still incomplete. Pyroptosis, a type of programmed cell death, has been shown to regulate tumor growth and may have potential prognostic value. However, the role of pyroptosis-related genes (PRGs) in MM remains undetermined. The aims of this study were to identify potential prognostic biomarkers and to construct a predictive model related to PRGs.

METHODS

Sequencing and clinical data were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Non-negative matrix factorization (NMF) was performed to identify molecular subtype screening. LASSO regression was used to screen for prognostic markers, and then a risk score model was constructed. The Maxstat package was utilized to calculate the optimal cutoff value, according to which patients were divided into a high-risk group and a low-risk group, and the survival curves were plotted using the Kaplan-Meier (K-M) method. Nomograms and calibration curves were established using the rms package.

RESULTS

A total of 33 PRGs were extracted from the TCGA database underlying which 4 MM molecular subtypes were defined. Patients in cluster 1 had poorer survival than those in cluster 2 (p = 0.035). A total of 9 PRGs were screened out as prognostic markers, and the predictive ability of the 9-gene risk score for 3-year survival was best (AUC = 0.658). Patients in the high-risk group had worse survival than those in the low-risk group (p < 0.001), which was consistent with the results verified by the GSE2658 dataset. The nomogram constructed by gender, age, International Staging System (ISS) stage, and risk score had the best prognostic predictive performance with a c-index of 0.721.

CONCLUSION

Our model could enhance the predictive ability of ISS staging and give a reference for clinical decision-making. The new, prognostic, and pyroptosis-related markers screened out by us may facilitate the development of novel risk stratification for MM.

CLINICAL TRIAL REGISTRATION

Not applicable.