Analysis of Staphylococcus aureus proteins secreted inside infected human epithelial cells

The role of pathogens in diabetes pathogenesis and the potential of immunoproteomics as a diagnostic and prognostic tool

Diabetes mellitus (DM) is a group of metabolic diseases marked by hyperglycemia, which increases the risk of systemic infections. DM patients are at greater risk of hospitalization and mortality from bacterial, viral, and fungal infections. Poor glycemic control can result in skin, blood, bone, urinary, gastrointestinal, and respiratory tract infections and recurrent infections. Therefore, the evidence that infections play a critical role in DM progression and the hazard ratio for a person with DM dying from any infection is higher. Early diagnosis and better glycemic control can help prevent infections and improve treatment outcomes. Perhaps, half (49.7%) of the people living with DM are undiagnosed, resulting in a higher frequency of infections induced by the hyperglycemic milieu that favors immune dysfunction. Novel diagnostic and therapeutic markers for glycemic control and infection prevention are desirable. High-throughput blood-based immunoassays that screen infections and hyperglycemia are required to guide timely interventions and efficiently monitor treatment responses. The present review aims to collect information on the most common infections associated with DM, their origin, pathogenesis, and the potential of immunoproteomics assays in the early diagnosis of the infections. While infections are common in DM, their role in glycemic control and disease pathogenesis is poorly described. Nevertheless, more research is required to identify novel diagnostic and prognostic markers to understand DM pathogenesis and management of infections. Precise monitoring of diabetic infections by immunoproteomics may provide novel insights into disease pathogenesis and healthy prognosis.

Proteomic Approaches to Unravel Mechanisms of Antibiotic Resistance and Immune Evasion of Bacterial Pathogens

The profound effects of and distress caused by the global COVID-19 pandemic highlighted what has been known in the health sciences a long time ago: that bacteria, fungi, viruses, and parasites continue to present a major threat to human health. Infectious diseases remain the leading cause of death worldwide, with antibiotic resistance increasing exponentially due to a lack of new treatments. In addition to this, many pathogens share the common trait of having the ability to modulate, and escape from, the host immune response. The challenge in medical microbiology is to develop and apply new experimental approaches that allow for the identification of both the microbe and its drug susceptibility profile in a time-sensitive manner, as well as to elucidate their molecular mechanisms of survival and immunomodulation. Over the last three decades, proteomics has contributed to a better understanding of the underlying molecular mechanisms responsible for microbial drug resistance and pathogenicity. Proteomics has gained new momentum as a result of recent advances in mass spectrometry. Indeed, mass spectrometry-based biomedical research has been made possible thanks to technological advances in instrumentation capability and the continuous improvement of sample processing and workflows. For example, high-throughput applications such as SWATH or Trapped ion mobility enable the identification of thousands of proteins in a matter of minutes. This type of rapid, in-depth analysis, combined with other advanced, supportive applications such as data processing and artificial intelligence, presents a unique opportunity to translate knowledge-based findings into measurable impacts like new antimicrobial biomarkers and drug targets. In relation to the Research Topic “Proteomic Approaches to Unravel Mechanisms of Resistance and Immune Evasion of Bacterial Pathogens,” this review specifically seeks to highlight the synergies between the powerful fields of modern proteomics and microbiology, as well as bridging translational opportunities from biomedical research to clinical practice.

Sputum Proteome Signatures of Mechanically Ventilated Intensive Care Unit Patients Distinguish Samples with or without Anti-pneumococcal Activity

Mechanically ventilated patients are at risk of contracting pneumonia. Therefore, these patients often receive prophylactic systemic antimicrobial therapy. Intriguingly however, a previous study showed that antimicrobial activity in bronchoalveolar aspirates (here referred to as “sputa”) from ventilated patients was only partially explained by antibiotic therapy. Here we report that sputa from these patients presented distinct proteome signatures depending on the presence or absence of antimicrobial activity. Moreover, we show that the same distinction applied to antibodies against Streptococcus pneumoniae, which is a major causative agent of pneumonia. Specifically, the investigated sputa that inhibited growth of S. pneumoniae, while containing subinhibitory levels of the antibiotic cefotaxime, presented elevated levels of proteins implicated in innate immune defenses, including complement and apolipoprotein-associated proteins. In contrast, S. pneumoniae-inhibiting sputa with relatively high cefotaxime concentrations or noninhibiting sputa contained higher levels of proteins involved in inflammatory responses, such as neutrophil elastase-associated proteins. In an immunoproteomics analysis, 18 out of 55 S. pneumoniae antigens tested showed significantly increased levels of IgGs in inhibiting sputa. Hence, proteomics and immunoproteomics revealed elevated levels of antimicrobial host proteins or S. pneumoniae antigen-specific IgGs in pneumococcal growth-inhibiting sputa, thus explaining their anti-pneumococcal activity.

IMPORTANCE Respiratory pathogens like Streptococcus pneumoniae can cause severe pneumonia. Nonetheless, mechanically ventilated intensive care patients, who have a high risk of contracting pneumonia, rarely develop pneumococcal pneumonia. This suggests the presence of potentially protective antimicrobial agents in their lung environment. Our present study shows for the first time that bronchoalveolar aspirates, “sputa,” of ventilated patients in a Dutch intensive care unit were characterized by three distinct groups of proteome abundance signatures that can explain their anti-pneumococcal activity. Importantly, this anti-pneumococcal sputum activity was related either to elevated levels of antimicrobial host proteins or to antibiotics and S. pneumoniae-specific antibodies. Further, the sputum composition of some patients changed over time. Therefore, we conclude that our study may provide a novel tool to measure changes that are indicative of infection-related conditions in the lungs of mechanically ventilated patients.

Phenotype and multi-omics comparison of Staphylococcus and Streptococcus uncovers pathogenic traits and predicts zoonotic potential

BACKGROUND

Staphylococcus and Streptococcus species can cause many different diseases, ranging from mild skin infections to life-threatening necrotizing fasciitis. Both genera consist of commensal species that colonize the skin and nose of humans and animals, and of which some can display a pathogenic phenotype.

RESULTS

We compared 235 Staphylococcus and 315 Streptococcus genomes based on their protein domain content. We show the relationships between protein persistence and essentiality by integrating essentiality predictions from two metabolic models and essentiality measurements from six large-scale transposon mutagenesis experiments. We identified clusters of strains within species based on proteins associated to similar biological processes. We built Random Forest classifiers that predicted the zoonotic potential. Furthermore, we identified shared attributes between of Staphylococcus aureus and Streptococcus pyogenes that allow them to cause necrotizing fasciitis.

CONCLUSIONS

Differences observed in clustering of strains based on functional groups of proteins correlate with phenotypes such as host tropism, capability to infect multiple hosts and drug resistance. Our method provides a solid basis towards large-scale prediction of phenotypes based on genomic information.