Metabolome and microbiome multi-omics integration from a murine lung inflammation model of bronchopulmonary dysplasia

Attempts to Understand Oral Mucositis in Head and Neck Cancer Patients through Omics Studies: A Narrative Review

Oral mucositis (OM) is a common and clinically impactful side effect of cytotoxic cancer treatment, particularly in patients with head and neck squamous cell carcinoma (HNSCC) who undergo radiotherapy with or without concomitant chemotherapy. The etiology and pathogenic mechanisms of OM are complex, multifaceted and elicit both direct and indirect damage to the mucosa. In this narrative review, we describe studies that use various omics methodologies (genomics, transcriptomics, microbiomics and metabolomics) in attempts to elucidate the biological pathways associated with the development or severity of OM. Integrating different omics into multi-omics approaches carries the potential to discover links among host factors (genomics), host responses (transcriptomics, metabolomics), and the local environment (microbiomics).

Multiomic Investigations into Lung Health and Disease

Diseases of the lung account for more than 5 million deaths worldwide and are a healthcare burden. Improving clinical outcomes, including mortality and quality of life, involves a holistic understanding of the disease, which can be provided by the integration of lung multi-omics data. An enhanced understanding of comprehensive multiomic datasets provides opportunities to leverage those datasets to inform the treatment and prevention of lung diseases by classifying severity, prognostication, and discovery of biomarkers. The main objective of this review is to summarize the use of multiomics investigations in lung disease, including multiomics integration and the use of machine learning computational methods. This review also discusses lung disease models, including animal models, organoids, and single-cell lines, to study multiomics in lung health and disease. We provide examples of lung diseases where multi-omics investigations have provided deeper insight into etiopathogenesis and have resulted in improved preventative and therapeutic interventions.

Perinatal origins of bronchopulmonary dysplasia-deciphering normal and impaired lung development cell by cell

Bronchopulmonary dysplasia (BPD) is a multifactorial disease occurring as a consequence of premature birth, as well as antenatal and postnatal injury to the developing lung. BPD morbidity and severity depend on a complex interplay between prenatal and postnatal inflammation, mechanical ventilation, and oxygen therapy as well as associated prematurity-related complications. These initial hits result in ill-explored aberrant immune and reparative response, activation of pro-fibrotic and anti-angiogenic factors, which further perpetuate the injury. Histologically, the disease presents primarily by impaired lung development and an arrest in lung microvascular maturation. Consequently, BPD leads to respiratory complications beyond the neonatal period and may result in premature aging of the lung. While the numerous prenatal and postnatal stimuli contributing to BPD pathogenesis are relatively well known, the specific cell populations driving the injury, as well as underlying mechanisms are still not well understood. Recently, an effort to gain a more detailed insight into the cellular composition of the developing lung and its progenitor populations has unfold. Here, we provide an overview of the current knowledge regarding perinatal origin of BPD and discuss underlying mechanisms, as well as novel approaches to study the perturbed lung development.

Differential co-expression networks of the gut microbiota are associated with depression and anxiety treatment resistance among psychiatric inpatients

BACKGROUND

Comorbid anxiety and depression are common and are associated with greater disease burden than either alone. Our recent efforts have identified an association between gut microbiota dysfunction and severity of anxiety and depression. In this follow-up, we applied Differential Co-Expression Analysis (DiffCoEx) to identify potential gut microbiota biomarker(s) candidates of treatment resistance among psychiatric inpatients.

METHODS

In a sample of convenience, 100 psychiatric inpatients provided clinical data at admission and discharge; fecal samples were collected early during the hospitalization. Whole genome shotgun sequencing methods were used to process samples. DiffCoEx was used to identify clusters of microbial features significantly different based on treatment resistance status. Once overlapping features were identified, a knowledge-mining tool was used to review the literature using a list of microbial species/pathways and a select number of medical subject headlines (MeSH) terms relevant for depression, anxiety, and brain-gut-axis dysregulation. Network analysis used overlapping features to identify microbial interactions that could impact treatment resistance.

RESULTS

DiffCoEx analyzed 10,403 bacterial features: 43/44 microbial features associated with depression treatment resistance overlapped with 43/114 microbial features associated with anxiety treatment resistance. Network analysis resulted in 8 biological interactions between 16 bacterial species. Clostridium perfringens evidenced the highest connection strength (0.95). Erysipelotrichaceae bacterium 6_1_45 has been most widely examined, is associated with inflammation and dysbiosis, but has not been associated with depression or anxiety.

CONCLUSION

DiffCoEx potentially identified gut bacteria biomarker candidates of depression and anxiety treatment-resistance. Future efforts in psychiatric microbiology should examine the mechanistic relationship of identified pro-inflammatory species, potentially contributing to a biomarker-based algorithm for treatment resistance.