Spatial heterogeneity of immune regulators drives dynamic changes of local immune responses, affecting disease outcomes in pancreatic cancer.

PURPOSE: Pancreatic ductal adenocarcinoma (PDAC) is considered a low immunogenic tumor with "cold" tumor microenvironment (TME) and is mostly unresponsive to immune checkpoint blockade therapies. Here we decipher the impact of intratumoral heterogeneity of immune determinants on antitumor response. EXPERIMENTAL DESIGN: We performed spatial proteomic and transcriptomic analyses and multiplexed immunofluorescence on multiple tumor regions, including tumor center (TC) and invasive front (IF), from 220 PDAC-patients, classified according to their transcriptomic immune signaling into high-immunogenic (HI-PDACs, n=54) and low-immunogenic tumors (LI-PDACs, n=166). Spatial compartments (tumor: Pancytokeratin+/CD45- and leukocytes: Pancytokeratin-/CD45+) were defined by fluorescent imaging. RESULTS: HI-PDACs exhibited higher densities of cytotoxic T lymphocytes with upregulation of T-cell priming-associated immune determinants, including CD40, ITGAM, GITR, CXCL10, GZMB, IFNG and HLA-DR, which was significantly more prominent at the IF than the TC. In contrast, LI-PDACs exhibited immune evasive TMEs with downregulation of immune determinants and a negative gradient from TC to IF. Patients with HI-PDACs had significantly better outcomes; however, they showed more frequently exhausted immune phenotypes. CONCLUSIONS: Our results indicate strategic differences in the regulation of immune determinants, which lead to different levels of effectiveness of antitumor responses between high- and low-immunogenic tumors and dynamic spatial changes, which affect the evolution of immune evasion and patient outcomes. This supports coevolution of tumor and immune cells and may help define therapeutic vulnerabilities to improve antitumor immunity and harness the responsiveness to immune checkpoint inhibitors in PDAC patients.

Explainable AI-prioritized plasma and fecal metabolites in inflammatory bowel disease and their dietary associations.

Fecal metabolites effectively discriminate inflammatory bowel disease (IBD) and show differential associations with diet. Metabolomics and AI-based models, including explainable AI (XAI), play crucial roles in understanding IBD. Using datasets from the UK Biobank and the Human Microbiome Project Phase II IBD Multi'omics Database (HMP2 IBDMDB), this study uses multiple machine learning (ML) classifiers and Shapley additive explanations (SHAP)-based XAI to prioritize plasma and fecal metabolites and analyze their diet correlations. Key findings include the identification of discriminative metabolites like glycoprotein acetyl and albumin in plasma, as well as nicotinic acid metabolites andurobilin in feces. Fecal metabolites provided a more robust disease predictor model (AUC [95%]: 0.93 [0.87-0.99]) compared to plasma metabolites (AUC [95%]: 0.74 [0.69-0.79]), with stronger and more group-differential diet-metabolite associations in feces. The study validates known metabolite associations and highlights the impact of IBD on the interplay between gut microbial metabolites and diet.

Cross-species transcriptomics identifies obesity associated genes between human and mouse studies.

BACKGROUND: Fundamentally defined by an imbalance in energy consumption and energy expenditure, obesity is a significant risk factor of several musculoskeletal conditions including osteoarthritis (OA). High-fat diets and sedentary lifestyle leads to increased adiposity resulting in systemic inflammation due to the endocrine properties of adipose tissue producing inflammatory cytokines and adipokines. We previously showed serum levels of specific adipokines are associated with biomarkers of bone remodelling and cartilage volume loss in knee OA patients. Whilst more recently we find the metabolic consequence of obesity drives the enrichment of pro-inflammatory fibroblast subsets within joint synovial tissues in obese individuals compared to those of BMI defined 'health weight'. As such this present study identifies obesity-associated genes in OA joint tissues which are conserved across species and conditions. METHODS: The study utilised 6 publicly available bulk and single-cell transcriptomic datasets from human and mice studies downloaded from Gene Expression Omnibus (GEO). Machine learning models were employed to model and statistically test datasets for conserved gene expression profiles. Identified genes were validated in OA tissues from obese and healthy weight individuals using quantitative PCR method (N = 38). Obese and healthy-weight patients were categorised by BMI > 30 and BMI between 18 and 24.9 respectively. Informed consent was obtained from all study participants who were scheduled to undergo elective arthroplasty. RESULTS: Principal component analysis (PCA) was used to investigate the variations between classes of mouse and human data which confirmed variation between obese and healthy populations. Differential gene expression analysis filtered on adjusted p-values of p < 0.05, identified differentially expressed genes (DEGs) in mouse and human datasets. DEGs were analysed further using area under curve (AUC) which identified 12 genes. Pathway enrichment analysis suggests these genes were involved in the biosynthesis and elongation of fatty acids and the transport, oxidation, and catabolic processing of lipids. qPCR validation found the majority of genes showed a tendency to be upregulated in joint tissues from obese participants. Three validated genes, IGFBP2 (p = 0.0363), DOK6 (0.0451) and CASP1 (0.0412) were found to be significantly different in obese joint tissues compared to lean-weight joint tissues. CONCLUSIONS: The present study has employed machine learning models across several published obesity datasets to identify obesity-associated genes which are validated in joint tissues from OA. These results suggest obesity-associated genes are conserved across conditions and may be fundamental in accelerating disease in obese individuals. Whilst further validations and additional conditions remain to be tested in this model, identifying obesity-associated genes in this way may serve as a global aid for patient stratification giving rise to the potential of targeted therapeutic interventions in such patient subpopulations.

Fecal microbiota and volatile metabolome pattern alterations precede late-onset meningitis in preterm neonates.

OBJECTIVE: The fecal microbiota and metabolome are hypothesized to be altered before late-onset neonatal meningitis (LOM), in analogy to late-onset sepsis (LOS). The present study aimed to identify fecal microbiota composition and volatile metabolomics preceding LOM. METHODS: Cases and gestational age-matched controls were selected from a prospective, longitudinal preterm cohort study (born <30 weeks' gestation) at nine neonatal intensive care units. The microbial composition (16S rRNA sequencing) and volatile metabolome (gas chromatography-ion mobility spectrometry (GC-IMS) and GC-time-of-flight-mass spectrometry (GC-TOF-MS)), were analyzed in fecal samples 1-10 days pre-LOM. RESULTS: Of 1397 included infants, 21 were diagnosed with LOM (1.5%), and 19 with concomitant LOS (90%). Random Forest classification and MaAsLin2 analysis found similar microbiota features contribute to the discrimination of fecal pre-LOM samples versus controls. A Random Forest model based on six microbiota features accurately predicts LOM 1-3 days before diagnosis with an area under the curve (AUC) of 0.88 (n=147). Pattern recognition analysis by GC-IMS revealed an AUC of 0.70-0.76 (P<0.05) in the three days pre-LOM (n=92). No single discriminative metabolites were identified by GC-TOF-MS (n=66). CONCLUSION: Infants with LOM could be accurately discriminated from controls based on preclinical microbiota composition, while alterations in the volatile metabolome were moderately associated with preclinical LOM.

Network dynamics and therapeutic aspects of mRNA and protein markers with the recurrence sites of pancreatic cancer.

Pancreatic ductal adenocarcinoma (PDAC) is a deadly disease that typically manifests late patient presentation and poor outcomes. Furthermore, PDAC recurrence is a common challenge. Distinct patterns of PDAC recurrence have been associated with differential activation of immune pathway-related genes and specific inflammatory responses in their tumour microenvironment. However, the molecular associations between and within cellular components that underpin PDAC recurrence require further development, especially from a multi-omics integration perspective. In this study, we identified stable molecular associations across multiple PDAC recurrences and utilised integrative analytics to identify stable and novel associations via simultaneous feature selection. Spatial transcriptome and proteome datasets were used to perform univariate analysis, Spearman partial correlation analysis, and univariate analyses by Machine Learning methods, including regularised canonical correlation analysis and sparse partial least squares. Furthermore, networks were constructed for reported and new stable associations. Our findings revealed gene and protein associations across multiple PDAC recurrence groups, which can provide a better understanding of the multi-layer disease mechanisms that contribute to PDAC recurrence. These findings may help to provide novel association targets for clinical studies for constructing precision medicine and personalised surveillance tools for patients with PDAC recurrence.

Integrated analysis revealing novel associations between dietary patterns and the immune system in older adults.

With the expanding ageing population, there is a growing interest in the maintenance of immune health to support healthy ageing. Enthusiasm exists for unravelling the impact of diet on the immune system and its therapeutic potential. However, a key challenge is the lack of studies investigating the effect of dietary patterns and nutrients on immune responses. Thus, we have used an integrative analysis approach to improve our understanding of diet-immune system interactions in older adults. To do so, dietary data were collected in parallel with performing immunophenotyping and functional assays from healthy older (n = 40) participants. Food Frequency Questionnaire (FFQ) was utilised to derive food group intake and multi-colour flow cytometry was performed for immune phenotypic and functional analysis. Spearman correlation revealed the strength of association between all combinations of dietary components, micronutrients, and hallmarks of immunesenescence. In this study, we propose for the first time that higher adherence to the Mediterranean diet is associated with a positive immune-ageing trajectory (Lower IMM-AGE score) in older adults due to the immune protective effects of high dietary fibre and PUFA intake in combating accumulation or pro-inflammatory senescent T cells. Furthermore, a diet rich in Vit A, Vit B6 and Vit B12 is associated with fewer features of immunesenescence [such as accumulation of terminally differentiated memory CD8 T cells] in older adults. Based on our findings we propose a future nutrition-based intervention study evaluating the efficacy of adherence to the MED diet alongside a multi-nutrient supplementation on immune ageing in older adults to set reliable dietary recommendations with policymakers that can be given to geriatricians and older adults. Insight box: There is a growing interest in the maintenance of immune health to boost healthy ageing. However, a key challenge is the lack of studies investigating the effect of dietary patterns and nutrients on immune responses. Thus, to do so we collected dietary data in parallel with performing immunophenotyping and functional assays on healthy older (n = 40) participants, followed by an integrative analysis approach to improve our understanding of diet-immune system interactions in older adults. We strongly believe that these new findings are appropriate for IB and will be of considerable interest to its broad audience.

Cerebrospinal fluid metabolomes of treatment-resistant depression subtypes and ketamine response: a pilot study.

Depression is a disorder with variable presentation. Selecting treatments and dose-finding is, therefore, challenging and time-consuming. In addition, novel antidepressants such as ketamine have sparse optimization evidence. Insights obtained from metabolomics may improve the management of patients. The objective of this study was to determine whether compounds in the cerebrospinal fluid (CSF) metabolome correlate with scores on questionnaires and response to medication. We performed a retrospective pilot study to evaluate phenotypic and metabolomic variability in patients with treatment-resistant depression using multivariate data compression algorithms. Twenty-nine patients with treatment-resistant depression provided fasting CSF samples. Over 300 metabolites were analyzed in these samples with liquid chromatography-mass spectrometry. Chart review provided basic demographic information, clinical status with self-reported questionnaires, and response to medication. Of the 300 metabolites analyzed, 151 were present in all CSF samples and used in the analyses. Hypothesis-free multivariate analysis compressed the resultant data set into two dimensions using Principal Component (PC) analysis, accounting for ~ 32% of the variance. PC1 accounted for 16.9% of the variance and strongly correlated with age in one direction and 5-methyltetrahydrofolate, homocarnosine, and depression and anxiety scores in the opposite direction. PC2 accounted for 15.4% of the variance, with one end strongly correlated with autism scores, male gender, and cognitive fatigue scores, and the other end with bipolar diagnosis, lithium use, and ethylmalonate disturbance. This small pilot study suggests that complex treatment-resistant depression can be mapped onto a 2-dimensional pathophysiological domain. The results may have implications for treatment selection for depression subtypes.

A novel generative adversarial networks modelling for the class imbalance problem in high dimensional omics data.

Class imbalance remains a large problem in high-throughput omics analyses, causing bias towards the over-represented class when training machine learning-based classifiers. Oversampling is a common method used to balance classes, allowing for better generalization of the training data. More naive approaches can introduce other biases into the data, being especially sensitive to inaccuracies in the training data, a problem considering the characteristically noisy data obtained in healthcare. This is especially a problem with high-dimensional data. A generative adversarial network-based method is proposed for creating synthetic samples from small, high-dimensional data, to improve upon other more naive generative approaches. The method was compared with 'synthetic minority over-sampling technique' (SMOTE) and 'random oversampling' (RO). Generative methods were validated by training classifiers on the balanced data.

High hypoxia status in pancreatic cancer is associated with multiple hallmarks of an immunosuppressive tumor microenvironment.

Introduction: Pancreatic ductal adenocarcinoma (PDAC), the most common form of pancreatic cancer, is a particularly lethal disease that is often diagnosed late and is refractory to most forms of treatment. Tumour hypoxia is a key hallmark of PDAC and is purported to contribute to multiple facets of disease progression such as treatment resistance, increased invasiveness, metabolic reprogramming, and immunosuppression. Methods: We used the Buffa gene signature as a hypoxia score to profile transcriptomics datasets from PDAC cases. We performed cell-type deconvolution and gene expression profiling approaches to compare the immunological phenotypes of cases with low and high hypoxia scores. We further supported our findings by qPCR analyses in PDAC cell lines cultured in hypoxic conditions. Results: First, we demonstrated that this hypoxia score is associated with increased tumour grade and reduced survival suggesting that this score is correlated to disease progression. Subsequently, we compared the immune phenotypes of cases with high versus low hypoxia score expression (HypoxiaHI vs. HypoxiaLOW) to show that high hypoxia is associated with reduced levels of T cells, NK cells and dendritic cells (DC), including the crucial cDC1 subset. Concomitantly, immune-related gene expression profiling revealed that compared to HypoxiaLOW tumours, mRNA levels for multiple immunosuppressive molecules were notably elevated in HypoxiaHI cases. Using a Random Forest machine learning approach for variable selection, we identified LGALS3 (Galectin-3) as the top gene associated with high hypoxia status and confirmed its expression in hypoxic PDAC cell lines. Discussion: In summary, we demonstrated novel associations between hypoxia and multiple immunosuppressive mediators in PDAC, highlighting avenues for improving PDAC immunotherapy by targeting these immune molecules in combination with hypoxia-targeted drugs.

Metabolic profiling of aortic stenosis and hypertrophic cardiomyopathy identifies mechanistic contrasts in substrate utilization.

Aortic stenosis (AS) and hypertrophic cardiomyopathy (HCM) are distinct disorders leading to left ventricular hypertrophy (LVH), but whether cardiac metabolism substantially differs between these in humans remains to be elucidated. We undertook an invasive (aortic root, coronary sinus) metabolic profiling in patients with severe AS and HCM in comparison with non-LVH controls to investigate cardiac fuel selection and metabolic remodeling. These patients were assessed under different physiological states (at rest, during stress induced by pacing). The identified changes in the metabolome were further validated by metabolomic and orthogonal transcriptomic analysis, in separately recruited patient cohorts. We identified a highly discriminant metabolomic signature in severe AS in all samples, regardless of sampling site, characterized by striking accumulation of long-chain acylcarnitines, intermediates of fatty acid transport across the inner mitochondrial membrane, and validated this in a separate cohort. Mechanistically, we identify a downregulation in the PPAR-α transcriptional network, including expression of genes regulating fatty acid oxidation (FAO). In silico modeling of ß-oxidation demonstrated that flux could be inhibited by both the accumulation of fatty acids as a substrate for mitochondria and the accumulation of medium-chain carnitines which induce competitive inhibition of the acyl-CoA dehydrogenases. We present a comprehensive analysis of changes in the metabolic pathways (transcriptome to metabolome) in severe AS, and its comparison to HCM. Our results demonstrate a progressive impairment of ß-oxidation from HCM to AS, particularly for FAO of long-chain fatty acids, and that the PPAR-α signaling network may be a specific metabolic therapeutic target in AS.

Link prediction in complex network using information flow.

Link prediction in complex networks has recently attracted a great deal of attraction in diverse scientific domains, including social and biological sciences. Given a snapshot of a network, the goal is to predict links that are missing in the network or that are likely to occur in the near future. This problem has both theoretical and practical significance; it not only helps us to identify missing links in a network more efficiently by avoiding the expensive and time consuming experimental processes, but also allows us to study the evolution of a network with time. To address the problem of link prediction, numerous attempts have been made over the recent years that exploit the local and the global topological properties of the network to predict missing links in the network. In this paper, we use parametrised matrix forest index (PMFI) to predict missing links in a network. We show that, for small parameter values, this index is linked to a heat diffusion process on a graph and therefore encodes geometric properties of the network. We then develop a framework that combines the PMFI with a local similarity index to predict missing links in the network. The framework is applied to numerous networks obtained from diverse domains such as social network, biological network, and transport network. The results show that the proposed method can predict missing links with higher accuracy when compared to other state-of-the-art link prediction methods.

Analysis of the opinions of individuals on the COVID-19 vaccination on social media.

The COVID-19 pandemic continues to threaten public health globally. To develop effective interventions and campaigns to raise vaccination rates, policy makers need to understand people's attitudes towards vaccination. We examine the perspectives of people in India, the United States, Canada, and the United Kingdom on the administration of different COVID-19 vaccines. We analyse how public opinion and emotional tendencies regarding the COVID-19 vaccines relate to popular issues on social media. We employ machine learning algorithms to forecast thoughts based on the social media posts. The prevailing emotional tendency indicates that individuals have faith in immunisation. However, there is a likelihood that significant statements or events on a national, international, or political scale influence public perception of vaccinations. We show how public health officials can track public attitudes and opinions towards vaccine-related information in a geo-aware manner, respond to the sceptics, and increase the level of vaccine trust in a particular region or community.

Correction: Evaluating the detection ability of a range of epistasis detection methods on simulated data for pure and impure epistatic models.

[This corrects the article DOI: 10.1371/journal.pone.0263390.].

NAD depletion mediates cytotoxicity in human neurons with autophagy deficiency.

Autophagy is a homeostatic process critical for cellular survival, and its malfunction is implicated in human diseases including neurodegeneration. Loss of autophagy contributes to cytotoxicity and tissue degeneration, but the mechanistic understanding of this phenomenon remains elusive. Here, we generated autophagy-deficient (ATG5-/-) human embryonic stem cells (hESCs), from which we established a human neuronal platform to investigate how loss of autophagy affects neuronal survival. ATG5-/- neurons exhibit basal cytotoxicity accompanied by metabolic defects. Depletion of nicotinamide adenine dinucleotide (NAD) due to hyperactivation of NAD-consuming enzymes is found to trigger cell death via mitochondrial depolarization in ATG5-/- neurons. Boosting intracellular NAD levels improves cell viability by restoring mitochondrial bioenergetics and proteostasis in ATG5-/- neurons. Our findings elucidate a mechanistic link between autophagy deficiency and neuronal cell death that can be targeted for therapeutic interventions in neurodegenerative and lysosomal storage diseases associated with autophagic defect.

The ultra-acute steroid response to traumatic injury: a cohort study.

OBJECTIVE: Trauma-induced steroid changes have been studied post-hospital admission, resulting in a lack of understanding of the speed and extent of the immediate endocrine response to injury. The Golden Hour study was designed to capture the ultra-acute response to traumatic injury. DESIGN: We conducted an observational cohort study including adult male trauma patients <60 years, with blood samples drawn ≤1 h of major trauma by pre-hospital emergency responders. METHODS: We recruited 31 adult male trauma patients (mean age 28 [range 19-59] years) with a mean injury severity score (ISS) of 16 (IQR 10-21). The median time to first sample was 35 (range 14-56) min, with follow-up samples collected 4-12 and 48-72 h post-injury. Serum steroids in patients and age- and sex-matched healthy controls (HCs) (n = 34) were analysed by tandem mass spectrometry. RESULTS: Within 1 h of injury, we observed an increase in glucocorticoid and adrenal androgen biosynthesis. Cortisol and 11-hydroxyandrostendione increased rapidly, whilst cortisone and 11-ketoandrostenedione decreased, reflective of increased cortisol and 11-oxygenated androgen precursor biosynthesis by 11ß-hydroxylase and increased cortisol activation by 11ß-hydroxysteroid dehydrogenase type 1. Active classic gonadal androgens testosterone and 5α-dihydrotestosterone decreased, whilst the active 11-oxygenated androgen 11-ketotestosterone maintained pre-injury levels. CONCLUSIONS: Changes in steroid biosynthesis and metabolism occur within minutes of traumatic injury. Studies that address whether ultra-early changes in steroid metabolism are associated with patient outcomes are now required.

Spatially restricted tumour-associated and host-associated immune drivers correlate with the recurrence sites of pancreatic cancer.

OBJECTIVE: Most patients with pancreatic ductal adenocarcinoma (PDAC) will experience recurrence after resection. Here, we investigate spatially organised immune determinants of PDAC recurrence. DESIGN: PDACs (n=284; discovery cohort) were classified according to recurrence site as liver (n=93/33%), lung (n=49/17%), local (n=31/11%), peritoneal (n=38/13%) and no-recurrence (n=73/26%). Spatial compartments were identified by fluorescent imaging as: pancytokeratin (PanCK)+CD45- (tumour cells); CD45+PanCK- (leucocytes) and PanCK-CD45- (stromal cells), followed by transcriptomic (72 genes) and proteomic analysis (51 proteins) for immune pathway targets. Results from next-generation sequencing (n=194) were integrated. Finally, 10 tumours from each group underwent immunophenotypic analysis by multiplex immunofluorescence. A validation cohort (n=109) was examined in parallel. RESULTS: No-recurrent PDACs show high immunogenicity, adaptive immune responses and are rich in pro-inflammatory chemokines, granzyme B and alpha-smooth muscle actin+ fibroblasts. PDACs with liver and/or peritoneal recurrences display low immunogenicity, stemness phenotype and innate immune responses, whereas those with peritoneal metastases are additionally rich in FAP+ fibroblasts. PDACs with local and/or lung recurrences display interferon-gamma signalling and mixed adaptive and innate immune responses, but with different leading immune cell population. Tumours with local recurrences overexpress dendritic cell markers whereas those with lung recurrences neutrophilic markers. Except the exclusive presence of RNF43 mutations in the no-recurrence group, no genetic differences were seen. The no-recurrence group exhibited the best, whereas liver and peritoneal recurrences the poorest prognosis. CONCLUSIONS: Our findings demonstrate distinct inflammatory/stromal responses in each recurrence group, which might affect dissemination patterns and patient outcomes. These findings may help to inform personalised adjuvant/neoadjuvant and surveillance strategies in PDAC, including immunotherapeutic modalities.

Artificial intelligence to enhance clinical value across the spectrum of cardiovascular healthcare.

Artificial intelligence (AI) is increasingly being utilized in healthcare. This article provides clinicians and researchers with a step-wise foundation for high-value AI that can be applied to a variety of different data modalities. The aim is to improve the transparency and application of AI methods, with the potential to benefit patients in routine cardiovascular care. Following a clear research hypothesis, an AI-based workflow begins with data selection and pre-processing prior to analysis, with the type of data (structured, semi-structured, or unstructured) determining what type of pre-processing steps and machine-learning algorithms are required. Algorithmic and data validation should be performed to ensure the robustness of the chosen methodology, followed by an objective evaluation of performance. Seven case studies are provided to highlight the wide variety of data modalities and clinical questions that can benefit from modern AI techniques, with a focus on applying them to cardiovascular disease management. Despite the growing use of AI, further education for healthcare workers, researchers, and the public are needed to aid understanding of how AI works and to close the existing gap in knowledge. In addition, issues regarding data access, sharing, and security must be addressed to ensure full engagement by patients and the public. The application of AI within healthcare provides an opportunity for clinicians to deliver a more personalized approach to medical care by accounting for confounders, interactions, and the rising prevalence of multi-morbidity.