CD4 + T cell differentiation is governed by gene regulatory and metabolic networks, with both networks being highly interconnected and able to adapt to external stimuli. Th17 and Tregs differentiation networks play a critical role in cancer, and their balance is affected by the tumor microenvironment (TME). Factors from the TME mediate recruitment and expansion of Th17 cells, but these cells can act with pro or anti-tumor immunity. Tregs cells are also involved in tumor development and progression by inhibiting antitumor immunity and promoting immunoevasion. Due to the complexity of the underlying molecular pathways, the modeling of biological systems has emerged as a promising solution for better understanding both CD4 + T cell differentiation and cancer cell behavior. In this review, we present a context-dependent vision of CD4 + T cell transcriptomic and metabolic network adaptability. We then discuss CD4 + T cell knowledge-based models to extract the regulatory elements of Th17 and Tregs differentiation in multiple CD4 + T cell levels. We highlight the importance of complementing these models with data from omics technologies such as transcriptomics and metabolomics, in order to better delineate existing Th17 and Tregs bifurcation mechanisms. We were able to recompilate promising regulatory components and mechanisms of Th17 and Tregs differentiation under normal conditions, which we then connected with biological evidence in the context of the TME to better understand CD4 + T cell behavior in cancer. From the integration of mechanistic models with omics data, the transcriptomic and metabolomic reprograming of Th17 and Tregs cells can be predicted in new models with potential clinical applications, with special relevance to cancer immunotherapy.

Background The overuse of antibiotics has led to increased antimicrobial resistance, but plant-derived biological response modifiers represent a potential alternative to these drugs. This investigation examined the immunomodulatory and antibacterial activities of Sida cordifolia (used in ethnomedicinal systems to treat infectious disease). Methods Successive extractions were performed from the roots of these plants in hexane, chloroform, methanol and water. Immunomodulatory activity was determined in a series of experiments measuring the responses of splenocytes, macrophages and an in vivo model of innate immunity (Galleria mellonella). Antibacterial activity was assessed by determining minimum inhibitory/bactericidal concentrations (MIC/MBCs) for various Gram-positive and Gram-negative bacterial strains. Results Immunomodulatory activity was confined to the aqueous extract, and further fractionation and biochemical analysis yielded a highly potent polysaccharide-enriched fraction (SCAF5). SCAF5 is a complex mixture of different polysaccharides with multiple immunomodulatory effects including immune cell proliferation, antibody secretion, phagocytosis, nitric oxide production, and increased expression of pro-inflammatory cytokines. Furthermore, Galleria mellonella pre-treated with SCAF5 produced more haemocytes and were more resistant (P < 0.001) to infection with methicillin-resistant Staphylococcus aureus (MRSA) with a 98% reduction in bacterial load in pre-treated larvae compared to the negative control. The antibacterial activity of Sida cordifolia was confined to the methanolic fraction. Extensive fractionation identified two compounds, rosmarinic acid and its 4-O-β-d-glucoside derivative, which had potent activity against Gram-positive antibiotic-resistant bacteria, including MRSA. Conclusions Sida cordifolia counters bacterial infections through a dual mechanism, and immunomodulatory polysaccharides from this plant should be isolated and characterised to realise their potential as anti-infective agents. Such properties could be developed as an antibiotic alternative (1) in the clinic and (2) alternative growth promoter for the agri-food industry.

This study used desorption electrospray ionisation mass spectrometry (DESI-MS) to analyse and detect and classify biomarkers in five different animal and plant sources of milk for the first time. A range of differences in terms of features was observed in the spectra of cow milk, goat milk, camel milk, soya milk, and oat milk. Chemometric modelling was then used to classify the mass spectra data, enabling unique or significant markers for each milk source to be identified. The classification of different milk sources was achieved with a cross-validation percentage rate of 100% through linear discriminate analysis (LDA) with high sensitivity to adulteration (0.1–5% v/v). The DESI-MS results from the milk samples analysed show the methodology to have high classification accuracy, and in the absence of complex sample clean-up which is often associated with authenticity testing, to be a rapid and efficient approach for milk fraud control.

The trend of the number of publications on a research field is often used to quantify research interest and effort, but this measure is biased by general publication record inflation. This study introduces a novel metric as an unbiased and quantitative tool for trend analysis and bibliometrics. The metric was used to reanalyze reported publication trends and perform in-depth trend analyses on patent groups and a broad range of field in the life-sciences. The analyses confirmed that inflation bias frequently results in the incorrect identification of field-specific increased growth. It was shown that the metric enables a more detailed, quantitative and robust trend analysis of peer reviewed publications and patents. Some examples of the metric’s uses are quantifying inflation-corrected growth in research regarding microplastics (51% ± 10%) between 2012 and 2018 and detecting inflation-corrected growth increase for transcriptomics and metabolomics compared to genomics and proteomics (Tukey post hoc p<0.0001). The developed trend-analysis tool removes inflation bias from bibliometric trend analyses. The metric improves evidence-driven decision-making regarding research effort investment and funding allocation.

A mid-level data fusion coupled with multivariate analysis approach is applied to dual-platform mass spectrometry data sets using Rapid Evaporative Ionization Mass Spectrometry and Inductively Coupled Plasma Mass Spectrometry to determine the correct classification of salmon origin and production methods. Salmon (n = 522) from five different regions and two production methods are used in the study. The method achieves a cross-validation classification accuracy of 100% and all test samples (n = 17) have their origins correctly determined, which is not possible with single-platform methods. Eighteen robust lipid markers and nine elemental markers are found, which provide robust evidence of the provenance of the salmon. Thus, we demonstrate that our mid-level data fusion - multivariate analysis strategy greatly improves the ability to correctly identify the geographical origin and production method of salmon, and this innovative approach can be applied to many other food authenticity applications.