Innovative PNA-LB mediated allele-specific LAMP for KRAS mutation profiling on a compact lab-on-a-disc device.

Tailored healthcare, an approach focused on individual patients, requires integrating emerging interdisciplinary technologies to develop accurate and user-friendly diagnostic tools. KRAS mutations, prevalent in various common cancers, are crucial determinants in selecting patients for novel KRAS inhibitor therapies. This study presents a novel state-of-the-art Lab-on-a-Disc system utilizing peptide nucleic acids-loop backward (PNA-LB) mediated allele-specific loop-mediated isothermal amplification (LAMP) for detecting the frequent G12D KRAS mutation, signifying its superiority over alternative mutation detection approaches. The designed Lab-on-a-Disc system demonstrated exceptional preclinical and technical precision, accuracy, and versatility. By applying varying cutoff values to PNA- LB LAMP reactions, the assay's sensitivity and specificity were increased by 80 % and 90 %, respectively. The device's key advantages include a robust microfluidic Lab-on-a-Disc design, precise rotary control, and a cutting-edge induction heating module. These features enable multiplexing of LAMP reactions with high reproducibility and repeatability, with CV% values less than 3.5 % and 5.5 %, respectively. The device offers several methods for accurate endpoint result detection, including naked-eye observation, RGB image analysis using Python code, and time of fluorescence (Tf) values. Preclinical specificity and sensitivity, assessed using different cutoffs for Eva-Green fluorescence Tf values and pH-sensitive dyes, demonstrated comparable performance to the best standard methods. Overall, this study represents a significant step towards tailoring treatment strategies for cancer patients through precise and efficient mutation detection technologies.

Distinct functional neutrophil phenotypes in sepsis patients correlate with disease severity.

Purpose: Sepsis is a clinical syndrome defined as life-threatening organ dysfunction caused by a dysregulated host response to infection. Sepsis is a highly heterogeneous syndrome with distinct phenotypes that impact immune function and response to infection. To develop targeted therapeutics, immunophenotyping is needed to identify distinct functional phenotypes of immune cells. In this study, we utilized our Organ-on-Chip assay to categorize sepsis patients into distinct phenotypes using patient data, neutrophil functional analysis, and proteomics. Methods: Following informed consent, neutrophils and plasma were isolated from sepsis patients in the Temple University Hospital ICU (n=45) and healthy control donors (n=7). Human lung microvascular endothelial cells (HLMVEC) were cultured in the Organ-on-Chip and treated with buffer or cytomix ((TNF/IL-1ß/IFNγ). Neutrophil adhesion and migration across HLMVEC in the Organ-on-Chip were used to categorize functional neutrophil phenotypes. Quantitative label-free global proteomics was performed on neutrophils to identify differentially expressed proteins. Plasma levels of sepsis biomarkers and neutrophil extracellular traps (NETs) were determined by ELISA. Results: We identified three functional phenotypes in critically ill ICU sepsis patients based on ex vivo neutrophil adhesion and migration patterns. The phenotypes were classified as: Hyperimmune characterized by enhanced neutrophil adhesion and migration, Hypoimmune that was unresponsive to stimulation, and Hybrid with increased adhesion but blunted migration. These functional phenotypes were associated with distinct proteomic signatures and differentiated sepsis patients by important clinical parameters related to disease severity. The Hyperimmune group demonstrated higher oxygen requirements, increased mechanical ventilation, and longer ICU length of stay compared to the Hypoimmune and Hybrid groups. Patients with the Hyperimmune neutrophil phenotype had significantly increased circulating neutrophils and elevated plasma levels NETs. Conclusion: Neutrophils and NETs play a critical role in vascular barrier dysfunction in sepsis and elevated NETs may be a key biomarker identifying the Hyperimmune group. Our results establish significant associations between specific neutrophil functional phenotypes and disease severity and identify important functional parameters in sepsis pathophysiology that may provide a new approach to classify sepsis patients for specific therapeutic interventions.

The experiences and perceptions of people with chronic and rare diseases during political-economic sanctions in Iran: a qualitative study.

BACKGROUND: Economic sanctions aim to exert pressure on political and economic foundations. Hypothesizing that sanctions might affect various aspects of population health, this study, as a component of a broader investigation to ascertain the trend effects of sanctions on selected health outcomes in Iran, seeks to explore the experiences of Iranian citizens associated with the imposed sanctions. METHODS: This is a qualitative study. We conducted 31 semi-structured interviews with randomly selected patients diagnosed with at least one chronic and rare disease from diverse backgrounds across four provinces in Iran. We analyzed data using an inductive content analysis approach, facilitated by the MAXQDA10 software. RESULTS: We identified three primary themes: direct effects, side effects, and coping strategies. The immediate effects were perceived to be manifested through the restriction of healthcare service availability and affordability for citizens. The side effects included the economic hardships experienced in individuals' lives and the perceived devastation caused by these difficulties. Some coping mechanisms adopted by patients or their families/relatives included prioritizing comorbidities, prioritizing health needs within families with multiple ill members, and readjusting health/illness requirements in light of daily living needs. CONCLUSION: In addition to the inherent burden of their illness, patients faced substantial healthcare costs as a result of sanctions, restricted access to medications, and availability of low-quality medications. We advocate considering these challenges within the healthcare system resilience framework as a crucial first step for policymakers, aiming to determine actionable measures and mitigate the adverse effects of sanctions on citizens, particularly the most vulnerable groups.

Bioinformatics analysis and machine learning approach applied to the identification of novel key genes involved in non-alcoholic fatty liver disease.

Non-alcoholic fatty liver disease (NAFLD) comprises a range of chronic liver diseases that result from the accumulation of excess triglycerides in the liver, and which, in its early phases, is categorized NAFLD, or hepato-steatosis with pure fatty liver. The mortality rate of non-alcoholic steatohepatitis (NASH) is more than NAFLD; therefore, diagnosing the disease in its early stages may decrease liver damage and increase the survival rate. In the current study, we screened the gene expression data of NAFLD patients and control samples from the public dataset GEO to detect DEGs. Then, the correlation betweenbetween the top selected DEGs and clinical data was evaluated. In the present study, two GEO datasets (GSE48452, GSE126848) were downloaded. The dysregulated expressed genes (DEGs) were identified by machine learning methods (Penalize regression models). Then, the shared DEGs between the two training datasets were validated using validation datasets. ROC-curve analysis was used to identify diagnostic markers. R software analyzed the interactions between DEGs, clinical data, and fatty liver. Ten novel genes, including ABCF1, SART3, APC5, NONO, KAT7, ZPR1, RABGAP1, SLC7A8, SPAG9, and KAT6A were found to have a differential expression between NAFLD and healthy individuals. Based on validation results and ROC analysis, NR4A2 and IGFBP1b were identified as diagnostic markers. These key genes may be predictive markers for the development of fatty liver. It is recommended that these key genes are assessed further as possible predictive markers during the development of fatty liver.

Natriuretic Peptides in Gastrointestinal Cancer: Biomarkers and Potential Therapeutic Targets.

Gastrointestinal (GI) cancers are an important health problem globally. Natriuretic peptides are hormones that have a crucial role in human physiology. There are a variety of treatments for GI cancer, but conventional therapies have side effects and low efficacy. Studies have demonstrated that natriuretic peptides are therapeutic in different cancer types. Natriuretic peptides are best known for their involvement in regulating blood pressure and blood volume. The anti-tumor effect exerted by natriuretic peptides is via their inhibitory effects on DNA synthesis and by their effects on apoptosis. The anti-proliferative role of natriuretic peptides has been shown in human breast cancer, prostate, colon, pancreatic, lung, ovarian, and other tumors. The roles of natriuretic peptides in these cancers are diverse and not well understood. Therefore, we have reviewed the recent literature on natriuretic peptides in GI cancers as a common malignancy in adults to assess the pathways that NPs are involved in the progression of GI cancers and its effect on the prevention or treatment of GI cancers.

The Relationships between Social Capital, Metabolic, and Behavioral Risk Factors of Non-Communicable Diseases: A Systematic Review.

Background: Contextual risk factors such as social capital have a vital role in affecting behavioral and biological risk factors of NCDs. We aimed to systematically identify the relationship between different aspects of social capital (SC) with metabolic, and behavioral risk factors of non-communicable diseases (NCDs). Methods: This is a systematic review. The period of study was 2000-2021. We searched the English international databases, i.e. PubMed/Medline, Scopus, and Web of Science. Studies that reported NCDs' metabolic and behavioral risk factors as independent variables, were excluded. We also included studies if they analyzed the association between SC and metabolic and behavioral risk factors of NCDs. Results: After the primary and quality appraisal process, 97 studies were entered in the final phase of the analysis. Five out of 18 studies reported an inverse association between SC and the level of alcohol drinking. Twenty-seven out of 32 studies reported a significant inverse association between SC and smoking and tobacco use, while only one study reported a significant positive association. Nine studies reported a significant inverse association between SC and high blood pressure. Three studies showed a significant inverse association between SC and diabetes. Seventeen studies indicated a significant positive association between SC and physical activity. Thirteen out of 17 studies reported a significant inverse association between SC, body mass index (BMI) and overweight. Conclusion: High SC, people's participation and interaction are vital in tackling NCDs. Evidence shows positive effects of SC on prevention, control and improvement of NCDs' metabolic and behavioral risk factors.

Aberrations in temporal dynamics of cognitive processing induced by Parkinson's disease and Levodopa.

The motor symptoms of Parkinson's disease (PD) have been shown to significantly improve by Levodopa. However, despite the widespread adoption of Levodopa as a standard pharmaceutical drug for the treatment of PD, cognitive impairments linked to PD do not show visible improvement with Levodopa treatment. Furthermore, the neuronal and network mechanisms behind the PD-induced cognitive impairments are not clearly understood. In this work, we aim to explain these cognitive impairments, as well as the ones exacerbated by Levodopa, through examining the differential dynamic patterns of the phase-amplitude coupling (PAC) during cognitive functions. EEG data recorded in an auditory oddball task performed by a cohort consisting of controls and a group of PD patients during both on and off periods of Levodopa treatment were analyzed to derive the temporal dynamics of the PAC across the brain. We observed distinguishing patterns in the PAC dynamics, as an indicator of information binding, which can explain the slower cognitive processing associated with PD in the form of a latency in the PAC peak time. Thus, considering the high-level connections between the hippocampus, the posterior and prefrontal cortices established through the dorsal and ventral striatum acting as a modulatory system, we posit that the primary issue with cognitive impairments of PD, as well as Levodopa's cognitive deficit side effects, can be attributed to the changes in temporal dynamics of dopamine release influencing the modulatory function of the striatum.

Smart Wearable Nanopaper Patch for Continuous Multiplexed Optical Monitoring of Sweat Parameters.

Notwithstanding the substantial progress in optical wearable sensing devices, developing wearable optical sensors for simultaneous, real-time, and continuous monitoring of multiple biomarkers is still an important, yet unmet, demand. Aiming to address this need, we introduced for the first time a smart wearable optical sensor (SWOS) platform combining a multiplexed sweat sensor sticker with its IoT-enabled readout module. We employed our SWOS system for on-body continuous, real-time, and simultaneous fluorimetric monitoring of sweat volume (physical parameter) and pH (chemical marker). Herein, a variation in moisture (5-45 µL) or pH (4.0-7.0) causes a color/fluorescence change in the copper chloride/fluorescein immobilized within a transparent chitin nanopaper (ChNP) in a selective and reversible manner. Human experiments conducted on athletic volunteers during exercise confirm that our developed SWOS platform can be efficiently exploited for smart perspiration analysis toward personalized health monitoring. Moreover, our system can be further extended for the continuous and real-time multiplexed monitoring of various biomarkers (metabolites, proteins, or drugs) of sweat or other biofluids (for example, analyzing exhaled breath by integrating onto a facemask).

The Prognostic Value of ASPHD1 and ZBTB12 in Colorectal Cancer: A Machine Learning-Based Integrated Bioinformatics Approach.

Introduction: Colorectal cancer (CRC) is a common cancer associated with poor outcomes, underscoring a need for the identification of novel prognostic and therapeutic targets to improve outcomes. This study aimed to identify genetic variants and differentially expressed genes (DEGs) using genome-wide DNA and RNA sequencing followed by validation in a large cohort of patients with CRC. Methods: Whole genome and gene expression profiling were used to identify DEGs and genetic alterations in 146 patients with CRC. Gene Ontology, Reactom, GSEA, and Human Disease Ontology were employed to study the biological process and pathways involved in CRC. Survival analysis on dysregulated genes in patients with CRC was conducted using Cox regression and Kaplan-Meier analysis. The STRING database was used to construct a protein-protein interaction (PPI) network. Moreover, candidate genes were subjected to ML-based analysis and the Receiver operating characteristic (ROC) curve. Subsequently, the expression of the identified genes was evaluated by Real-time PCR (RT-PCR) in another cohort of 64 patients with CRC. Gene variants affecting the regulation of candidate gene expressions were further validated followed by Whole Exome Sequencing (WES) in 15 patients with CRC. Results: A total of 3576 DEGs in the early stages of CRC and 2985 DEGs in the advanced stages of CRC were identified. ASPHD1 and ZBTB12 genes were identified as potential prognostic markers. Moreover, the combination of ASPHD and ZBTB12 genes was sensitive, and the two were considered specific markers, with an area under the curve (AUC) of 0.934, 1.00, and 0.986, respectively. The expression levels of these two genes were higher in patients with CRC. Moreover, our data identified two novel genetic variants-the rs925939730 variant in ASPHD1 and the rs1428982750 variant in ZBTB1-as being potentially involved in the regulation of gene expression. Conclusions: Our findings provide a proof of concept for the prognostic values of two novel genes-ASPHD1 and ZBTB12-and their associated variants (rs925939730 and rs1428982750) in CRC, supporting further functional analyses to evaluate the value of emerging biomarkers in colorectal cancer.

Therapeutic Potential of Targeting the Cytochrome P450 Enzymes Using Lopinavir/Ritonavir in Colorectal Cancer: A Study in Monolayers, Spheroids and In Vivo Models.

Cytochrome P450 (CYP450) enzyme has been shown to be expressed in colorectal cancer (CRC) and its dysregulation is linked to tumor progression and a poor prognosis. Here we investigated the therapeutic potential of targeting CYP450 using lopinavir/ritonavir in CRC. The integrative systems biology method and RNAseq were utilized to investigate the differential levels of genes associated with patients with colorectal cancer. The antiproliferative activity of lopinavir/ritonavir was evaluated in both monolayer and 3-dimensional (3D) models, followed by wound-healing assays. The effectiveness of targeting CYP450 was examined in a mouse model, followed by histopathological analysis, biochemical tests (MDA, SOD, thiol, and CAT), and RT-PCR. The data of dysregulation expressed genes (DEG) revealed 1268 upregulated and 1074 down-regulated genes in CRC. Among the top-score genes and dysregulated pathways, CYPs were detected and associated with poor prognosis of patients with CRC. Inhibition of CYP450 reduced cell proliferation via modulating survivin, Chop, CYP13a, and induction of cell death, as detected by AnnexinV/PI staining. This agent suppressed the migratory behaviors of cells by induction of E-cadherin. Moreover, lopinavir/ritonavir suppressed tumor growth and fibrosis, which correlated with a reduction in SOD/thiol levels and increased MDA levels. Our findings illustrated the therapeutic potential of targeting the CYP450 using lopinavir/ritonavir in colorectal cancer, supporting future investigations on this novel therapeutic approach for the treatment of CRC.

Identification of ZMYND19 as a novel biomarker of colorectal cancer: RNA-sequencing and machine learning analysis.

Colorectal cancer (CRC) is the third most common cause of cancer-related deaths. The five-year relative survival rate for CRC is estimated to be approximately 90% for patients diagnosed with early stages and 14% for those diagnosed at an advanced stages of disease, respectively. Hence, the development of accurate prognostic markers is required. Bioinformatics enables the identification of dysregulated pathways and novel biomarkers. RNA expression profiling was performed in CRC patients from the TCGA database using a Machine Learning approach to identify differential expression genes (DEGs). Survival curves were assessed using Kaplan-Meier analysis to identify prognostic biomarkers. Furthermore, the molecular pathways, protein-protein interaction, the co-expression of DEGs, and the correlation between DEGs and clinical data have been evaluated. The diagnostic markers were then determined based on machine learning analysis. The results indicated that key upregulated genes are associated with the RNA processing and heterocycle metabolic process, including C10orf2, NOP2, DKC1, BYSL, RRP12, PUS7, MTHFD1L, and PPAT. Furthermore, the survival analysis identified NOP58, OSBPL3, DNAJC2, and ZMYND19 as prognostic markers. The combineROC curve analysis indicated that the combination of C10orf2 -PPAT- ZMYND19 can be considered as diagnostic markers with sensitivity, specificity, and AUC values of 0.98, 1.00, and 0.99, respectively. Eventually, ZMYND19 gene was validated in CRC patients. In conclusion, novel biomarkers of CRC have been identified that may be a promising strategy for early diagnosis, potential treatment, and better prognosis.

Diagnostic accuracy of anti-DGP (IgG) for celiac disease.

Background: Celiac disease (CD) is an autoimmune enteropathy that is caused by the intake of gluten-containing grains in genetically susceptible humans. The gliadin and glutenin parts of wheat gluten are the essential factors that cause intestinal damage. Objectives: We analyzed the performance of a time-resolved immunofluorometric assay (TR-IFMA) in the diagnosis of CD in children. For this purpose, we compared the performance of IgA anti-tissue transglutaminase antibodies (IgA anti-TTG) and IgG antibodies against deamidated gliadin peptides (IgG anti-DGP) for the diagnosis of CD. Materials and Methods: In this cross-sectional study conducted in 2021 for a duration of about 6 months, 200 patients with suspected CD symptoms, children who needed screening due to Down syndrome or Turner syndrome, and the first-degree relatives of CD patients who underwent diagnostic evaluation were enrolled in a census study. Results: This study compares existing point-of-care anti-DGP Ab (IgG) and anti-TTG Ab (IgA) tests against each other using the gold standard of duodenal biopsy and pathology. Serology as a screening test was acceptable (93.6% for anti-DGP vs. 94.2% for anti-TTG) for both of them. This equivalent sensitivity of serum TTG and the DGP tests validates its potential as a basic tool for serological testing. Furthermore, endoscopy is carried out in patients positive for both. Conclusions: Our study showed that for the diagnosis of CD, anti-DGP antibodies had comparable characteristics with anti-TTG (IgA) in terms of diagnostic specificity and sensitivity. Specify of anti-DGP was higher than that of anti-TTG.

Recent advances in isolation and detection of exosomal microRNAs related to Alzheimer's disease.

Alzheimer's disease, a progressive neurological condition, is associated with various internal and external risk factors in the disease's early stages. Early diagnosis of Alzheimer's disease is essential for treatment management. Circulating exosomal microRNAs could be a new class of valuable biomarkers for early Alzheimer's disease diagnosis. Different kinds of biosensors have been introduced in recent years for the detection of these valuable biomarkers. Isolation of the exosomes is a crucial step in the detection process which is traditionally carried out by multi-step ultrafiltration. Microfluidics has improved the efficiency and costs of exosome isolation by implementing various effects and forces on the nano and microparticles in the microchannels. This paper reviews recent advancements in detecting Alzheimer's disease related exosomal microRNAs based on methods such as electrochemical, fluorescent, and SPR. The presented devices' pros and cons and their efficiencies compared with the gold standard methods are reported. Moreover, the application of microfluidic devices to detect Alzheimer's disease related biomarkers is summarized and presented. Finally, some challenges with the performance of novel technologies for isolating and detecting exosomal microRNAs are addressed.

Robotic digital microfluidics: a droplet-based total analysis system.

Developing automated platforms for point-of-need testing is a crucial global demand. Digital microfluidics is a promising solution for expanding integrated testing devices featuring ultimate control over the chemical and biological reactions in micro/nanoliter droplets. In this study, robotic digital microfluidics (RDMF) is introduced for the mechanical manipulation of the droplets precisely and inexpensively. A controllable and multifunctional arm equipped with several actuators is responsible for dispensing and manipulating droplets on a disposable superhydrophobic cartridge. The platform has been demonstrated with diverse functions, including droplet dispensing, transport, mixing, aliquoting, and splitting. Moreover, incorporating magnetic and heating modules into the system can realize particle manipulation and droplet heating. The liquid handling operations are investigated from both experimental and modeling perspectives. Handling a wide range of droplet sizes without needing high-voltage electric sources, integrability with different detection techniques, and ease of manufacturing are the main advantages of the RDMF platform compared to conventional digital microfluidic systems. The availability of a complete fluidic toolbox and multiple detection choices make RDMF promising for droplet-based total analysis technology. The system was applied for a urinalysis test to show its versatility in handling complex biochemical assays. The results entirely matched those obtained based on laboratory gold standard techniques.

Therapeutic Potential of Herbal Medicine against Non-alcoholic Fatty Liver Disease.

Non-alcoholic fatty liver disease (NAFLD) is a common metabolic disorder associated with obesity, diabetes mellitus, dyslipidemia, and cardiovascular disease. A "multiple hit" model has been a widely accepted explanation for the disease's complicated pathogenesis. Despite advances in our knowledge of the processes underlying NAFLD, no conventional pharmaceutical therapy exists. The only currently approved option is to make lifestyle modifications, such as dietary and physical activity changes. The use of medicinal plants in the treatment of NAFLD has recently gained interest. Thus, we review the current knowledge about these agents based on clinical and preclinical studies. Moreover, the association between NAFLD and colorectal cancer (CRC), one of the most common and lethal malignancies, has recently emerged as a new study area. We overview the shared dysregulated pathways and the potential therapeutic effect of herbal medicines for CRC prevention in patients with NAFLD.

The lessons of COVID-19 pandemic for communicable diseases surveillance system in Kurdistan Region of Iraq.

Objectives: This study aimed to determine the opportunities of and barriers to communicable diseases surveillance system (CDSS) during the COVID-19 pandemic and the extent to which the disease integrated into the CDSS in the Kurdistan region of Iraq. Study design: A descriptive qualitative approach was applied. Methods: We conducted seven semi-structured interviews and seven interviewee in a focus group discussion (FGD) with purposefully identified Key Informants (KI) from June to December 2020. All interviews were digitally recorded and transcribed verbatim. We adopted a mixed deductive-inductive approach for thematic data analysis, facilitated by using MAXQDA20 software for data management. Results: Although the CDSS was considered appropriate and flexible, the COVID-19 was interpreted not to be integrated into the system due to political influence. The main concerns regarding core and support activities were the lack of epidemic preparedness, timeliness, and partial cessation of training and supervision during the pandemic. The existence of reasonable surveillance infrastructure, i.e., trained staff, was identified as an opportunity for improvement. The main challenges include staff deficiency, absence of motivation and financial support for present staff, scarce logistics, managerial and administrative issues, and lack of cooperation, particularly among stakeholders and surveillance staff. Conclusion: Our findings revealed that the CDSS in the Kurdistan region requires substantial enhancement in epidemic preparedness, strengthening human resources, and logistics. the system can be developed by fostering meaningful intersectoral collaboration. We advocate that the health authorities and policy-makers prioritise the surveillance and effective management of communicable diseases.

Ex-Vivo and In-Vivo Expansion of Spermatogonial Stem Cells Using Cell-Seeded Microfluidic Testis Scaffolds and Animal Model.

AIMS: This study was designed to provide both ex-vivo and in-vivo methods for the extraction and expansion of spermatogonial stem cells (SSCs). METHODS: For in-vivo experiments, azoospermic mouse model was performed with Busulfan. Isolation, culture, and characterization of neonate mouse SSC were also achieved. We performed an in-vivo injection of labeled SSCs to the testes with azoospermia. In ex-vivo experiments, extracted SSCs were seeded on the fabricated scaffold consisting of hyaluronic acid (HA) and decellularized testis tissues (DTT). Immunofluorescence staining with PLZF, TP1, and Tekt 1 was performed for SSCs differentiation and proliferation. RESULTS: Several studies demonstrated efficient spermatogenic arrest in seminiferous tubules and proved the absence of spermatogenesis. Transplanted SSCs moved and settled in the basement covering the seminiferous tubules. Most of the cells were positive for Dil, after 4 weeks. An epithelium containing spermatogonia-like cells with Sertoli-like, and Leydig cells were evident in the seminiferous tubules of biopsies, and the IHC staining was significantly positive, 4 weeks after injection of SSCs. The results of the ex-vivo experiments showed positive staining for all markers, which was significantly enhanced in scaffolds of ex-vivo experiments compared with in-vitro seeded scaffolds. CONCLUSION: Ex-vivo SSC differentiation and proliferation using cell-seeded microfluidic testis scaffolds maybe effective for treatment of the azoospermia.

LEUKOCYTE PHENOTYPING IN SEPSIS USING OMICS, FUNCTIONAL ANALYSIS, AND IN SILICO MODELING.

ABSTRACT: Sepsis is a major health issue and a leading cause of death in hospitals globally. The treatment of sepsis is largely supportive, and there are no therapeutics available that target the underlying pathophysiology of the disease. The development of therapeutics for the treatment of sepsis is hindered by the heterogeneous nature of the disease. The presence of multiple, distinct immune phenotypes ranging from hyperimmune to immunosuppressed can significantly impact the host response to infection. Recently, omics, biomarkers, cell surface protein expression, and immune cell profiles have been used to classify immune status of sepsis patients. However, there has been limited studies of immune cell function during sepsis and even fewer correlating omics and biomarker alterations to functional consequences. In this review, we will discuss how the heterogeneity of sepsis and associated immune cell phenotypes result from changes in the omic makeup of cells and its correlation with leukocyte dysfunction. We will also discuss how emerging techniques such as in silico modeling and machine learning can help in phenotyping sepsis patients leading to precision medicine.

The critical role of neutrophil-endothelial cell interactions in sepsis: new synergistic approaches employing organ-on-chip, omics, immune cell phenotyping and in silico modeling to identify new therapeutics.

Sepsis is a global health concern accounting for more than 1 in 5 deaths worldwide. Sepsis is now defined as life-threatening organ dysfunction caused by a dysregulated host response to infection. Sepsis can develop from bacterial (gram negative or gram positive), fungal or viral (such as COVID) infections. However, therapeutics developed in animal models and traditional in vitro sepsis models have had little success in clinical trials, as these models have failed to fully replicate the underlying pathophysiology and heterogeneity of the disease. The current understanding is that the host response to sepsis is highly diverse among patients, and this heterogeneity impacts immune function and response to infection. Phenotyping immune function and classifying sepsis patients into specific endotypes is needed to develop a personalized treatment approach. Neutrophil-endothelium interactions play a critical role in sepsis progression, and increased neutrophil influx and endothelial barrier disruption have important roles in the early course of organ damage. Understanding the mechanism of neutrophil-endothelium interactions and how immune function impacts this interaction can help us better manage the disease and lead to the discovery of new diagnostic and prognosis tools for effective treatments. In this review, we will discuss the latest research exploring how in silico modeling of a synergistic combination of new organ-on-chip models incorporating human cells/tissue, omics analysis and clinical data from sepsis patients will allow us to identify relevant signaling pathways and characterize specific immune phenotypes in patients. Emerging technologies such as machine learning can then be leveraged to identify druggable therapeutic targets and relate them to immune phenotypes and underlying infectious agents. This synergistic approach can lead to the development of new therapeutics and the identification of FDA approved drugs that can be repurposed for the treatment of sepsis.

Molecular Framework of Mouse Endothelial Cell Dysfunction during Inflammation: A Proteomics Approach.

A key aspect of cytokine-induced changes as observed in sepsis is the dysregulated activation of endothelial cells (ECs), initiating a cascade of inflammatory signaling leading to leukocyte adhesion/migration and organ damage. The therapeutic targeting of ECs has been hampered by concerns regarding organ-specific EC heterogeneity and their response to inflammation. Using in vitro and in silico analysis, we present a comprehensive analysis of the proteomic changes in mouse lung, liver and kidney ECs following exposure to a clinically relevant cocktail of proinflammatory cytokines. Mouse lung, liver and kidney ECs were incubated with TNF-α/IL-1ß/IFN-γ for 4 or 24 h to model the cytokine-induced changes. Quantitative label-free global proteomics and bioinformatic analysis performed on the ECs provide a molecular framework for the EC response to inflammatory stimuli over time and organ-specific differences. Gene Ontology and PANTHER analysis suggest why some organs are more susceptible to inflammation early on, and show that, as inflammation progresses, some protein expression patterns become more uniform while additional organ-specific proteins are expressed. These findings provide an in-depth understanding of the molecular changes involved in the EC response to inflammation and can support the development of drugs targeting ECs within different organs. Data are available via ProteomeXchange (identifier PXD031804).