Tissue Signatures Research Articles

Quantifying the impact of sample, instrument, and data processing on biological signatures in modern and fossil tissues detected with Raman spectroscopy

AbstractRaman spectroscopy is a popular tool for characterizing complex biological materials and their geological remains. Ordination methods, such as principal component analysis (PCA), use spectral variance to create a compositional space, the ChemoSpace, grouping samples based on spectroscopic manifestations reflecting different biological properties or geological processes. PCA allows to reduce the dimensionality of complex spectroscopic data and facilitates the extraction of informative features into formats suitable for downstream statistical analyses, thus representing a first step in the development of diagnostic biosignatures from complex modern and fossil tissues. For such samples, however, there is presently no systematic and accessible survey of the impact of sample, instrument, and spectral processing on the occupation of the ChemoSpace. Here, the influence of sample count, unwanted signals and different signal‐to‐noise ratios, spectrometer decalibration, baseline subtraction, and spectral normalization on ChemoSpace grouping is investigated and exemplified using synthetic spectra. Increase in sample size improves the dissociation of groups in the ChemoSpace, and our sample yields a representative and mostly stable pattern in occupation with less than 10 samples per group. The impact of systemic interference of different amplitude and frequency, periodical or random features that can be introduced by instrument or sample, on compositional biological signatures is reduced by PCA and allows to extract biological information even when spectra of differing signal‐to‐noise ratios are compared. Routine offsets ( 1 cm−1) in spectrometer calibration contribute in our sample to less than 0.1% of the total spectral variance captured in the ChemoSpace and do not obscure biological information. Standard adaptive baselining, together with normalization, increases spectral comparability and facilitates the extraction of informative features. The ChemoSpace approach to biosignatures represents a powerful tool for exploring, denoising, and integrating molecular information from modern and ancient organismal samples.

Optical signatures of thermal damage on ex-vivo brain, lung and heart tissues using time-domain diffuse optical spectroscopy.

Thermal therapies treat tumors by means of heat, greatly reducing pain, post-operation complications, and cost as compared to traditional methods. Yet, effective tools to avoid under- or over-treatment are mostly needed, to guide surgeons in laparoscopic interventions. In this work, we investigated the temperature-dependent optical signatures of ex-vivo calf brain, lung, and heart tissues based on the reduced scattering and absorption coefficients in the near-infrared spectral range (657 to 1107 nm). These spectra were measured by time domain diffuse optics, applying a step-like spatially homogeneous thermal treatment at 43 °C, 60 °C, and 80 °C. We found three main increases in scattering spectra, possibly due to the denaturation of collagen, myosin, and the proteins' secondary structure. After 75 °C, we found the rise of two new peaks at 770 and 830 nm in the absorption spectra due to the formation of a new chromophore, possibly related to hemoglobin or myoglobin. This research marks a significant step forward in controlling thermal therapies with diffuse optical techniques by identifying several key markers of thermal damage. This could enhance the ability to monitor and adjust treatment in real-time, promising improved outcomes in tumor therapy.

Integrated Metabolomics and Proteomics of Symptomatic and Early Presymptomatic States of Colitis.

Colitis has a multifactorial pathogenesis with a strong cross-talk among microbiota, hypoxia, and tissue metabolism. Here, we aimed to characterize the molecular signature of the disease in symptomatic and presymptomatic stages of the inflammatory process at the tissue and fecal level. The study is based on two different murine models for colitis, and HR-MAS NMR on "intact" colon tissues and LC-MS/MS on colon tissue extracts were used to derive untargeted metabolomics and proteomics information, respectively. Solution NMR was used to derive metabolomic profiles of the fecal extracts. By combining metabolomic and proteomic analyses of the tissues, we found increased anaerobic glycolysis, accompanied by an altered citric acid cycle and oxidative phosphorylation in inflamed colons; these changes associate with inflammation-induced hypoxia taking place in colon tissues. Different colitis states were also characterized by significantly different metabolomic profiles of fecal extracts, attributable to both the dysbiosis characteristic of colitis as well as the dysregulated tissue metabolism. Strong and distinctive tissue and fecal metabolomic signatures can be detected before the onset of symptoms. Therefore, untargeted metabolomics of tissues and fecal extracts provides a comprehensive picture of the changes accompanying the disease onset already at preclinical stages, highlighting the diagnostic potential of global metabolomics for inflammatory diseases.

Identification of molecular signatures in epicardial adipose tissue in heart failure with preserved ejection fraction.

The molecular signatures in epicardial adipose tissue (EAT) that contribute to the pathogenesis of heart failure with preserved ejection fraction (HFpEF) are poorly characterized. In this study, we sought to elucidate molecular signatures including genetic transcripts and long non-coding RNAs (lncRNAs) in EAT that might modulate HFpEF development. RNA sequencing (RNA-seq) was performed to identify differentially expressed lncRNAs and mRNAs in EAT samples from patients with HFpEF (n=5) and without HF (control, n=5) who underwent coronary artery bypass grafting. The sequencing results were validated using quantitative real-time PCR (qRT-PCR). Bioinformatics analysis (Gene Ontology and Kyoto Encyclopedia of Genes and Genomes) of differentially expressed RNAs was performed to predict enriched functions. HFpEF patients had higher EAT thickness and NT-proBNP levels than the control group. A total of 64471 transcripts were detected including 35395 protein-coding sequences, corresponding to 16854 genes in EAT. RNA-seq identified a total of 741 dysregulated mRNA transcripts (394 up-regulated and 347 down-regulated) and 334 differentially expressed lncRNA transcripts (222 up-regulated and 112 down-regulated) in the HFpEF group compared with the control group (P<0.05). qRT-PCR analysis confirmed that two lncRNAs ENST00000561775 (P=0.0194) and ENST00000519093 (P=0.027) and an mRNA POSTN (P=0.003) were differentially expressed. Functional enrichment analysis of the differentially expressed mRNAs suggested their potential roles in immune response involving cytokine interaction and chemokine signalling. We are the first group to report on the lncRNA and mRNA landscape in EAT in HFpEF patients. Our study suggests the possible role of lncRNAs in EAT.

Consequences of COVID-19 on Adipose Tissue Signatures.

Since the emergence of coronavirus disease-19 (COVID-19) in 2019, it has been crucial to investigate the causes of severe cases, particularly the higher rates of hospitalization and mortality in individuals with obesity. Previous findings suggest that adipocytes may play a role in adverse COVID-19 outcomes in people with obesity. The impact of COVID-19 vaccination and infection on adipose tissue (AT) is currently unclear. We therefore analyzed 27 paired biopsies of visceral and subcutaneous AT from donors of the Leipzig Obesity BioBank that have been categorized into three groups (1: no infection/no vaccination; 2: no infection but vaccinated; 3: infected and vaccinated) based on COVID-19 antibodies to spike (indicating vaccination) and/or nucleocapsid proteins. We provide additional insights into the impact of COVID-19 on AT biology through a comprehensive histological transcriptome and serum proteome analysis. This study demonstrates that COVID-19 infection is associated with smaller average adipocyte size. The impact of infection on gene expression was significantly more pronounced in subcutaneous than in visceral AT and mainly due to immune system-related processes. Serum proteome analysis revealed the effects of the infection on circulating adiponectin, interleukin 6 (IL-6), and carbonic anhydrase 5A (CA5A), which are all related to obesity and blood glucose abnormalities.

Recent molecular techniques to strengthen ecological studies in echinoderms

Introduction: Echinoderms, an integral component of marine ecosystems worldwide, have captivated scientific interest for centuries. Despite this longstanding attention, comprehending key facets such as trophic relationships, diet composition, and host-microbiota relationships still represents a challenge using traditional techniques. Recent years, however, have witnessed a transformative shift, thanks to the emergence of advanced molecular techniques, offering new approaches to strengthen ecological studies in echinoderms.&#x0D; Objective: Explore how recent advancements in molecular tools have impacted ecological research on echinoderms. Specifically, we aim to investigate the potential of these tools to shed light on trophic interactions, diet composition, and the characterization of gut microbial communities in these organisms.&#x0D; Methods: Available literature was used to clarify how novel molecular techniques can improve ecological studies. The focus is diet, trophic relationships, and gut microbiota.&#x0D; Results: Traditionally, studies of stomach contents using compound microscopy have provided an idea of ingested material; nevertheless, sometimes a simple magnified visualization of dietary content does not allow exhaustive identification of the entire food spectrum, as it is limited due to the rapid digestion and maceration of food items within the echinoderm’s digestive tract. The use of DNA-metabarcoding, targeting specific DNA regions, such as the mitochondrial COI gene, has allowed us to enhance the accuracy and precision of diet characterization by enabling the identification of prey items down to the species or even genetic variant level, providing valuable insights into specific dietary preferences. Another approach is the use of stable isotopes, particularly carbon and nitrogen, which provide a powerful tool to trace the origin and flow of nutrients through food webs. By analyzing the isotopic signatures in muscular tissues and food items, we can discern the sources of their primary food items and gain insights into their trophic position within the ecosystem. Lastly, a third new technique used to elucidate the characterization of the prokaryotic community is 16S rRNA sequencing. This method allows us to explore the composition and dynamics of the digestive tract microbial communities.&#x0D; Conclusions: This is a promising era for ecological research on echinoderms, where advances of molecular tools have enabled an unprecedented level of detail, resolving longstanding challenges in comprehending their trophic interactions, diet composition, and host-microbiota relationships, and opening new avenues of investigation in ecological studies.

Identification of ferroptosis-related subtypes, characteristics of TME infiltration and development of prognostic models in gastric cancer

BackgroundFerroptosis is a distinct form of cell death characterized by unique morphology, biochemistry, and genetics, playing a crucial role in the initiation, progression, prognosis, and therapeutic strategies of tumors. However, the impact of ferroptosis-related genes (FRGs) on the tumor microenvironment (TME) remains unclear. This study may advance the existing knowledge of FRGs in gastric cancer, and push ahead with more effective prognostic assessment and the development of more effective immunotherapy approaches. MethodsFRGs were acquired from the FerrDb database and a consensus clustering technique was adopted to categorize patients with GC into groups in line with the expression profiles of 44 FRGs in order to further investigate the expression properties of these proteins. Assessment of the immune status, microsatellite instability (MSI) and cancer stem cell (CSC) index between the high- and low- risk groups to assess the proportion of TIICs in the TME, ssGSVA was adopted to detect the abundance of infiltrating immune cells from the low-risk and high-risk groups. Expression levels of eight ferroptosis-related genes of prognostic signature in GC tissues and adjacent normal tissues was detected by RT-PCR. ResultsIn the GC cohort, TP53 has the highest mutation frequency (44 %), and was shown to be highly linked with the expression levels of 11 FRGs. In accordance with the Kaplan-Meier curve, the overall survival time of patients with subtype A (Low FRG-score) discernibly exceeded that of patients with subtype B (High FRG-score).In addition, there is a significant difference in the infiltration of most immune cells between subtype A and subtype B, and some important immune checkpoints (CTLA4, PDCD1, CD274, LAG3, PDCD1LG2, and HAVCR2) have higher expression in cluster A. Finally, low FRG-scores were significantly associated with MSI-H status, while high FRG-scores were significantly associated with microsatellite stable status (MSS). FRG-score is negatively related to the cancer stem cell (CSC). ConclusionLow FRG-score, due to its high microsatellite instability (MSI-H), high mutational load and immune activation, indicates the possible advantage of OS. In addition, the FRG-score was closely related to the cancer stem cell (CSC) index and the sensitive degree of chemotherapeutic drug.

The role of microRNAs in acrylamide toxicity.

The chemical compound known as Acrylamide (AA) is employed in different industries worldwide and is also found in thermal-processed food. AA has been acting as a reproductive toxicant, carcinogen, and neurotoxic in various animals, which may promote several toxic impacts in animal and human species. Up to now, various studies have focused on the harmful mechanisms and intervention actions of AA. However, the underlying mechanisms that AA and its toxic effects can exert have remained uncertain. MicroRNAs (miRNAs) are a class of short, non-coding RNAs that are able to act as epigenetic regulators. These molecules can regulate a wide range of cellular and molecular processes. In this regard, it has been shown that different chemical agents can dysregulate miRNAs. To determine the possible AA targets along with mechanisms of its toxicity, it is helpful to study the alteration in the profiles of miRNA regulation following AA intake. The current research aimed to evaluate the miRNAs' mediatory roles upon the AA's toxic potentials. This review study discussed the AA, which is made within the food matrix, the way it is consumed, and the potential impacts of AA on miRNAs and its association with different cancer types and degenerative diseases. The findings of this review paper indicated that AA might be capable of altering miRNA signatures in different tissues and exerting its carcinogen effects.

Metabolomic characterization of human glioblastomas and patient plasma: a pilot study

Background Glioblastoma (GBM) is a clinically challenging primary brain tumor with poor survival outcome despite surgical resection and intensive chemoradiation. The metabolic heterogeneity of GBM can become biomarkers for treatment response, resistance, and outcome prediction. The aim of the study is to investigate metabolic distinctions between primary and recurrent GBM tissue and patient plasma to establish feasibility for metabolic profiling. Methods A single-center cohort study analyzed tissue and blood samples from 15 patients with GBM using untargeted metabolomic/lipidomic assays. Metabolomic, lipidomic, and biogenic amine analyses were conducted on GBM tissue and patient plasma at diagnosis and recurrence using untargeted mass spectrometry. The study utilized a small but longitudinally collected cohort to evaluate alteration in metabolites, lipids, and biogenic amines between specimens at diagnosis and recurrence. Results Exploratory analysis revealed significant alteration in metabolites, lipids, and biogenic amines between diagnostic and recurrent states in both tumor and plasma specimens. Notable metabolites differed at recurrence, including N-alpha-methylhistamine, glycerol-3-phosphate, phosphocholine, and succinic acid in tissue, and indole-3-acetate, and urea in plasma. Principal component analysis revealed distinct metabolomic profiles between tumor tissue and patient plasma. Distinct metabolic profiles were observed in GBM tissue and patient plasma at recurrence, demonstrating the feasibility of using metabolomic methodologies for longitudinal studies. One patient exhibited a unique tumor resistance signature at diagnosis, possibly indicating a high-risk metabolomic phenotype. Conclusions In this small cohort, the findings suggest the potential of metabolomic signatures of GBM tissue and patient plasma for risk stratification, outcome prediction, and the development of novel adjuvant metabolic-targeting therapies. The findings suggest metabolic discrepancies at diagnosis and recurrence in tissue and plasma, highlighting potential implications for evaluation of clinical response. The identification of significant changes in metabolite abundance emphasizes the need for larger studies using targeted metabolomics to validate and further explore these profiles.

Differentiation of acute coronary syndrome with radiomics of pericoronary adipose tissue.

To assess the potential values of radiomics signatures of pericoronary adipose tissue (PCAT) in identifying patients with acute coronary syndrome (ACS). In total, 149, 227, and 244 patients were clinically diagnosed with ACS, chronic coronary syndrome (CCS), and without coronary artery disease (CAD), respectively, and were retrospectively analysed and randomly divided into training and testing cohorts at a 2:1 ratio. From the PCATs of the proximal left anterior descending branch, left circumflex branch, and right coronary artery (RCA), the pericoronary fat attenuation index (FAI) value and radiomics signatures were calculated, among which features closely related to ACS were screened out. The ACS differentiation models AC1, AC2, AC3, AN1, AN2, and AN3 were constructed based on the FAI value of RCA and the final screened out first-order and texture features, respectively. The FAI values were all higher in patients with ACS than in those with CCS and no CAD (all P < .05). For the identification of ACS and CCS, the area-under-the-curve (AUC) values of AC1, AC2, and AC3 were 0.92, 0.94, and 0.91 and 0.91, 0.86, and 0.88 in the training and testing cohorts, respectively. For the identification of ACS and no CAD, the AUC values of AN1, AN2, and AN3 were 0.95, 0.94, and 0.94 and 0.93, 0.87, and 0.89 in the training and testing cohorts, respectively. Identification models constructed based on the radiomics signatures of PCAT are expected to be an effective tool for identifying patients with ACS. The radiomics signatures of PCAT and FAI values are expected to differentiate between patients with ACS, CCS and those without CAD on imaging.

Magnetic Resonance Imaging Tissue Signatures Associated With White Matter Changes Due to Sporadic Cerebral Small Vessel Disease Indicate That White Matter Hyperintensities Can Regress.

White matter hyperintensities (WMHs) might regress and progress contemporaneously, but we know little about underlying mechanisms. We examined WMH change and underlying quantitative magnetic resonance imaging tissue measures over 1 year in patients with minor ischemic stroke with sporadic cerebral small vessel disease. We defined areas of stable normal-appearing white matter, stable WMHs, progressing and regressing WMHs based on baseline and 1-year brain magnetic resonance imaging. In these areas we assessed tissue characteristics with quantitative T1, fractional anisotropy (FA), mean diffusivity (MD), and neurite orientation dispersion and density imaging (baseline only). We compared tissue signatures cross-sectionally between areas, and longitudinally within each area. WMH change masks were available for N=197. Participants' mean age was 65.61 years (SD, 11.10), 59% had a lacunar infarct, and 68% were men. FA and MD were available for N=195, quantitative T1 for N=182, and neurite orientation dispersion and density imaging for N=174. Cross-sectionally, all 4 tissue classes differed for FA, MD, T1, and Neurite Density Index. Longitudinally, in regressing WMHs, FA increased with little change in MD and T1 (difference estimate, 0.011 [95% CI, 0.006-0.017]; -0.002 [95% CI, -0.008 to 0.003] and -0.003 [95% CI, -0.009 to 0.004]); in progressing and stable WMHs, FA decreased (-0.022 [95% CI, -0.027 to -0.017] and -0.009 [95% CI, -0.011 to -0.006]), whereas MD and T1 increased (progressing WMHs, 0.057 [95% CI, 0.050-0.063], 0.058 [95% CI, 0.050 -0.066]; stable WMHs, 0.054 [95% CI, 0.045-0.063], 0.049 [95% CI, 0.039-0.058]); and in stable normal-appearing white matter, MD increased (0.004 [95% CI, 0.003-0.005]), whereas FA and T1 slightly decreased and increased (-0.002 [95% CI, -0.004 to -0.000] and 0.005 [95% CI, 0.001-0.009]). Quantitative magnetic resonance imaging shows that WMHs that regress have less abnormal microstructure at baseline than stable WMHs and follow trajectories indicating tissue improvement compared with stable and progressing WMHs.

CT-based radiomics signature of visceral adipose tissue and bowel lesions for identifying patients with Crohn’s disease resistant to infliximab

PurposeTo develop a CT-based radiomics model combining with VAT and bowel features to improve the predictive efficacy of IFX therapy on the basis of bowel model.MethodsThis retrospective study included 231 CD patients (training cohort, n = 112; internal validation cohort, n = 48; external validation cohort, n = 71) from two tertiary centers. Machine-learning VAT model and bowel model were developed separately to identify CD patients with primary nonresponse to IFX. A comprehensive model incorporating VAT and bowel radiomics features was further established to verify whether CT features extracted from VAT would improve the predictive efficacy of bowel model. Area under the curve (AUC) and decision curve analysis were used to compare the prediction performance. Clinical utility was assessed by integrated differentiation improvement (IDI).ResultsVAT model and bowel model exhibited comparable performance for identifying patients with primary nonresponse in both internal (AUC: VAT model vs bowel model, 0.737 (95% CI, 0.590–0.854) vs. 0.832 (95% CI, 0.750–0.896)) and external validation cohort [AUC: VAT model vs. bowel model, 0.714 (95% CI, 0.595–0.815) vs. 0.799 (95% CI, 0.687–0.885)), exhibiting a relatively good net benefit. The comprehensive model incorporating VAT into bowel model yielded a satisfactory predictive efficacy in both internal (AUC, 0.840 (95% CI, 0.706–0.930)) and external validation cohort (AUC, 0.833 (95% CI, 0.726–0.911)), significantly better than bowel alone (IDI = 4.2% and 3.7% in internal and external validation cohorts, both p < 0.05).ConclusionVAT has an effect on IFX treatment response. It improves the performance for identification of CD patients at high risk of primary nonresponse to IFX therapy with selected features from RM.Critical relevance statementOur radiomics model (RM) for VAT-bowel analysis captured the pathophysiological changes occurring in VAT and whole bowel lesion, which could help to identify CD patients who would not response to infliximab at the beginning of therapy.Key points• Radiomics signatures with VAT and bowel alone or in combination predicting infliximab efficacy.• VAT features contribute to the prediction of IFX treatment efficacy.• Comprehensive model improved the performance compared with the bowel model alone.Graphical abstract

Differential modulation of allergic rhinitis nasal transcriptome by dupilumab and allergy immunotherapy.

Nasal epithelial cells are important regulators of barrier function and immune signaling; however, in allergic rhinitis (AR) these functions can be disrupted by inflammatory mediators. We aimed to better discern AR disease mechanismsusing transcriptome data from nasal brushing samples from individuals with and without AR. Data were drawn from a feasibility study of individuals with and without AR to Timothy grass and from a clinical trial evaluating 16 weeks of treatment with the following: dupilumab, a monoclonal antibody that binds interleukin (IL)-4Rα and inhibits type 2 inflammation by blocking signaling of both IL-4/IL-13; subcutaneous immunotherapy with Timothy grass (SCIT), which inhibits allergic responses through pleiotropic effects; SCIT + dupilumab; or placebo. Using nasal brushing samples from these studies, we defined distinct gene signatures in nasal tissue of AR disease and after nasal allergen challenge (NAC) and assessed how these signatures were modulated by study drug(s). Treatment with dupilumab (normalized enrichment score [NES] = -1.73, p = .002) or SCIT + dupilumab (NES = -2.55, p < .001), but not SCIT alone (NES = +1.16, p = .107), significantly repressed the AR disease signature. Dupilumab (NES = -2.55, p < .001), SCIT (NES = -2.99, p < .001), and SCIT + dupilumab (NES = -3.15, p < .001) all repressed the NAC gene signature. These results demonstrate type 2 inflammation is an important contributor to the pathophysiology of AR disease and that inhibition of the type 2 pathway with dupilumab may normalize nasal tissue gene expression.

P1035 CT-based radiomics signature of visceral adipose tissue and bowel lesions for identifying patients with Crohn’s disease resistant to infliximab

Abstract Background Data describing the effect of visceral adipose tissue (VAT) on infliximab treatment response in patients with Crohn’s disease (CD) remains scarce. We aimed to develop and validate a VAT-based radiomics model (RM) using computed tomography (CT) images to identify CD patients at high risk of primary nonresponse to infliximab, and explored whether it can improve the prediction efficacy of an established bowel lesions-based RM. Methods This retrospective study included 231 CD patients (training cohort, n=112; internal validation cohort, n=48; external validation cohort, n=71) from two tertiary centers. Machine-learning VAT model and bowel model were developed separately to identify CD patients with primary nonresponse to IFX. A comprehensive model incorporating VAT and bowel radiomics features was further established to verify whether CT features extracted from VAT would improve the predictive efficacy of bowel model. Area under the curve (AUC) and decision curve analysis were used to compare the prediction performance. Clinical utility was assessed by integrated differentiation improvement (IDI). Results VAT model and bowel model exhibited comparable performance for identifying patients with primary nonresponse in both internal [AUC: VAT model vs bowel model, 0.737(95% CI, 0.590-0.854) vs. 0.832 (95% CI, 0.750 - 0.896)] and external validation cohort [AUC: VAT model vs. bowel model, 0.714(95% CI, 0.595-0.815) vs. 0.799(95% CI, 0.687-0.885)], exhibiting a relatively good net benefit. The comprehensive model incorporating VAT into bowel model yielded a satisfactory predictive efficacy in both internal [AUC,0.840(95%CI, 0.706-0.930)] and external validation cohort [AUC,0.833(95%CI, 0.726-0.911)], significantly better than bowel alone (IDI=4.2% and 3.7% in internal and external validation cohorts, both P&amp;lt;0.05). Conclusion VAT has an effect on IFX treatment response. It improves the performance for identification of CD patients at high risk of primary nonresponse to IFX therapy with selected features from RM.

DOP62 A core molecular resistome characterised by extracellular matrix dysregulation and neutrophil degranulation predicts resistance to multiple therapies in Ulcerative Colitis

Abstract Background Several transcript signatures have been published that predict anti-TNF resistance in ulcerative colitis (UC). However, clinical translation is hampered by suboptimal validation performance, partly because of insufficient emphasis on potential pathophysiology. Moreover, predictors of ustekinumab response are lacking. We interrogated transcriptomic datasets and anti-TNF predictive signatures to investigate resistance mechanisms and applicability to ustekinumab. Methods Transcript profiles of colonic biopsies taken before treatment for moderate-to-severe UC were analysed from Gene Expression Omnibus. Patients were then treated with infliximab (n = 24 and n = 22), golimumab (n = 84) or ustekinumab (n = 358). Molecular pathways from the Reactome database that were differentially enriched between mucosal healing responders and non-responders post-induction were found, along with shared pathways between cohorts. The largest cohort was used to calculate enrichment of published resistance signatures in actively inflamed mucosa using Spearman correlation. Hierarchical clustering was undertaken for non-responders. Analyses were performed using R 4.2.2 (Vienna, Austria). Results Many biological pathways were associated with resistance across the cohorts (1079 pathways, adjusted p value&amp;lt;0.01). Sixty pathways were shared between 3 or 4 datasets. Those relating to extracellular matrix (ECM) activity, neutrophil degranulation and interleukin signalling were most prominent. Network analyses revealed that proteins forming these pathways interact in vivo, forming a discrete functional module. The enrichment of published transcript signatures correlated highly. For instance, correlation between enrichment of UC1 (Czarnewski et al, 2019) and M5 (Friedrich et al 2021) was 0.96. Over-representation analyses showed that these signatures represent processes such as interleukin signalling and neutrophil activity. Hierarchical clustering of non-responders revealed two distinct groups, including those with high (NRhi) and low (NRlo) signature enrichment (Figure 1). Cellular deconvolution analyses showed that neutrophils are more prevalent in NRhi compared to NRlo and that eosinophils may be more frequent in NRlo than NRhi. Conclusion ECM dysregulation and neutrophil activity may underlie a core ‘resistome’ that heralds poor treatment outcomes to anti-TNFs and ustekinumab. Supporting this, the enrichment scores of published transcript signatures in inflamed tissue correlate highly, suggesting that many of these represent shared, or similar, molecular mechanisms of resistance. However, there is a separate resistant cohort with different molecular drivers. These findings have implications for precision medicine approaches in UC.

Regulation of soil nitrogen cycling by shrubs in grasslands

Globally, grasslands are experiencing shrub expansion, but the effects of such plant community shifts on soil N cycling are seldom investigated. We explored the spatial heterogeneity of total soil N content by grid sampling down to a soil depth of 1.2 m, and analyzed net ammonification and nitrification rates in the top 0.2 m, during the growing season, in response to the patchiness of grasses and four shrub species in a grazed meadow on the Tibetan Plateau. The δ15N analyses of plant, soil, and mineral N (NH4+ and NO3−) revealed differences in N absorption strategies between shrubs and grasses. Heterogeneity in the total soil N distribution was common to at least a depth of 1.2 m. The lower δ15N values of the 1.2 m soil profile under shrubs were consistent with the lower δ15N values of leaves, aboveground litter, and roots of shrubs compared to grasses, reflecting the imprint of long-term N cycling in the soil as affected by shrubs. Increases in total soil N stock in the entire 1.2 m profile by 28%–32% under non-leguminous shrubs relative to grasses were ascribed to N accumulation in the lower 0.8 m. Consequently, deep sampling is essential for accurate estimation of soil N storage in shrubby meadows. The total N stock in soil under the leguminous shrub species was similar to that under grasses, which showed that the effect of shrubification on soil N storage depends on the shrub species. Nitrification rates in the topsoil under all shrubs were greater than under grasses, producing higher 15N-depleted NO3− contents under shrubs. Therefore, the lower δ15N signatures of shrub tissues compared to grasses reflected a preferential or adaptive uptake of NO3− by shrubs. The expansion of shrubs in grasslands creates high heterogeneity in N cycling and storage down to a considerable soil depth.

Abstract C043: Spatial proteomic immune profiling of pancreatic ductal adenocarcinoma and the tumor microenvironment

Abstract Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal disease with poor prognosis and treatment options. Technological challenges in dissecting the immune constituents of the PDAC tumor microenvironment (TME) has limited our understanding of the complex disease mechanisms in vivo, which may in part explain the lack of novel therapeutics for this recalcitrant disease. Here, we performed spatial proteomics using the multiplexed ion beam imaging (MIBI) approach to quantify 36 spatial signatures in tissues from a cohort of twelve treatment naïve PDAC patients. We applied a deep learning segmentation algorithm to better resolve expression levels at the single cell resolution, and robustly stratify phenotypes through an iterative process of clustering and annotation, with expert visual validation. The combination of phenotypic quantification, functional marker evaluation within the cell types identified, as well as machine-learning based topic modeling to understand cellular neighborhoods, can better explain the cellular and functional components within the intricate PDAC TME. Our analysis revealed markedly lower B and effector T cell infiltration in the treatment-naive TME of patients who eventually relapsed to date following adjuvant chemotherapy. We additionally observed the enrichment of myeloid cell populations, including tumor-associated macrophages and neutrophils, associated with PDAC tumorigenesis and worse prognosis. A deeper analysis of our functional protein expression exhibited the enrichment of GATA3 in regulatory T cells in relapsed patients, a marker which has been associated with increased accumulation and infiltration of Tregs at inflammatory sites. Our study demonstrates the importance of a targeted dissection of the spatially orchestrated immune responses and dysregulation associated with PDAC, and exemplifies how a deeper understanding of the immune and tumor components and functional aspects in situ can help improve our understanding for patients with this disease. Citation Format: Jason Yeung, Yunhao Bai, Yao Yu Yeo, Huaying Qiu, Bokai Zhu, Lara R. Heij, Enas Abu Shah, Garry P. Nolan, Shivan Sivakumar, Sizun Jiang. Spatial proteomic immune profiling of pancreatic ductal adenocarcinoma and the tumor microenvironment [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Pancreatic Cancer; 2023 Sep 27-30; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(2 Suppl):Abstract nr C043.

Diagnostic Role of Cell-Free miRNAs in Identifying Placenta Accreta Spectrum during First-Trimester Screening.

Placenta accreta spectrum (PAS) is a severe complication of pregnancy associated with excessive invasion of cytotrophoblast cells at the sites of the endometrial-myometrial interface and the myometrium itself in cases of adherent (creta) and invasive (increta and percreta) forms, respectively. This leads to a high risk of massive blood loss, maternal hysterectomy, and preterm birth. Despite advancements in ultrasound protocols and found associations of alpha-fetoprotein, PAPP-A, hCG, PLGF, sFlt-1, IL-8, and IL-33 peripheral blood levels with PAS, there is a high need for an additional non-invasive test to improve the diagnostic accuracy and to select the real PAS from the suspected ones in the first-trimester screening. miRNA signatures of placental tissue, myometrium, and blood plasma from women with PAS in the third trimester of pregnancy, as well as miRNA profiles in exosomes from the blood serum of women in the first trimester with physiologically progressing pregnancy, complicated by PAS or pre-eclampsia, were obtained using deep sequencing. Two logistic regression models were constructed, both featuring statistically significant parameters related to the levels of miR-26a-5p, miR-17-5p, and miR-101-3p, quantified by real-time PCR in native blood serum. These models demonstrated 100% sensitivity in detecting PAS during the first pregnancy screening. These miRNAs were identified as specific markers for PAS, showing significant differences in their blood serum levels during the first trimester in the PAS group compared to those in physiological pregnancies, early- or late-onset pre-eclampsia groups. Furthermore, these miRNAs exhibited differential expression in the PAS placenta and/or myometrium in the third trimester and, according to data from the literature, control angiogenesis. Significant correlations were found between extracellular hsa-miR-101-3p and nuchal translucency thickness, hsa-miR-17-5p and uterine artery pulsatility index, and hsa-miR-26a-5p and hsa-miR-17-5p with PLGF. The developed test system for early non-invasive PAS diagnosis based on the blood serum level of extracellular miR-26a-5p, miR-17-5p, and miR-101-3p can serve as an auxiliary method for first-trimester screening of pregnant women, subject to validation with independent test samples.

Multi-omics analysis reveals the impact of influenza a virus host adaptation on immune signatures in pig tracheal tissue.

Influenza A virus (IAV) infection is a global respiratory disease, which annually leads to 3-5 million cases of severe illness, resulting in 290,000-650,000 deaths. Additionally, during the past century, four global IAV pandemics have claimed millions of human lives. The epithelial lining of the trachea plays a vital role during IAV infection, both as point of viral entry and replication as well as in the antiviral immune response. Tracheal tissue is generally inaccessible from human patients, which makes animal models crucial for the study of the tracheal host immune response. In this study, pigs were inoculated with swine- or human-adapted H1N1 IAV to gain insight into how host adaptation of IAV shapes the innate immune response during infection. In-depth multi-omics analysis (global proteomics and RNA sequencing) of the host response in upper and lower tracheal tissue was conducted, and results were validated by microfluidic qPCR. Additionally, a subset of samples was selected for histopathological examination. A classical innate antiviral immune response was induced in both upper and lower trachea after infection with either swine- or human-adapted IAV with upregulation of genes and higher abundance of proteins associated with viral infection and recognition, accompanied by a significant induction of interferon stimulated genes with corresponding higher proteins concentrations. Infection with the swine-adapted virus induced a much stronger immune response compared to infection with a human-adapted IAV strain in the lower trachea, which could be a consequence of a higher viral load and a higher degree of inflammation. Central components of the JAK-STAT pathway, apoptosis, pyrimidine metabolism, and the cytoskeleton were significantly altered depending on infection with swine- or human-adapted virus and might be relevant mechanisms in relation to antiviral immunity against putative zoonotic IAV. Based on our findings, we hypothesize that during host adaptation, IAV evolve to modulate important host cell elements to favor viral infectivity and replication.

Construction of immune-related molecular diagnostic and predictive models of hepatocellular carcinoma based on machine learning

BackgroundTo exploit hepatocellular carcinoma (HCC) diagnostic substances, we identify potential predictive markers based on machine learning and to explore the significance of immune cell infiltration in this pathology. MethodThree HCC gene expression datasets were used for weighted gene co-expression network analysis (WGCNA) and differential expression analysis. Least Absolute Shrinkage and Selection Operator (LASSO) and Random Forest were applied to identify candidate biomarkers. The diagnostic value of HCC diagnostic gene biomarkers was further assessed by the area under the ROC curve observed in the validation dataset. CIBERSORT was used to analyze 22 immune cell fractions from HCC patients and to analyze their correlation with diagnostic markers. In addition, the prognostic value of the markers and the sensitivity of the drugs were analyzed. ResultWGCNA and differential expression analysis were used to screen 396 distinct gene signatures in HCC tissues. They were mostly engaged in cytoplasmic fusion and the cell division cycle, according to gene enrichment analyses. Five genes were shown to have a high diagnostic value for use as diagnostic biomarkers for HCC, including EFHD1 (AUC = 0.77), KIF4A (AUC = 0.97), UBE2C (AUC = 0.96), SMYD3 (AUC = 0.91), and MCM7 (AUC = 0.93). T cells, NK cells, macrophages, and dendritic cells were found to be related to diagnostic markers in HCC tissues by immune cell infiltration analysis, indicating that these cells are intimately linked to the onset and spread of HCC. Concurrently, these five genes and their constructed models have considerable prognostic value. ConclusionThese five genes (EFHD1, KIF4A, UBE2C, SMYD3, and MCM7) may serve as new candidate molecular markers for HCC, providing new insights for future diagnosis, prognosis, and molecular therapy of HCC.