Unsupervised Analysis Research Articles

The role of Passion and Self-Efficacy in entrepreneurial activities in the gig economy: An Unsupervised Machine Learning Analysis with Topic Modeling

This research examines passion and self-efficacy through experience and knowledge, as motivational factors that support entrepreneurs within the gig economy (GE). It sheds light on entrepreneurs’ sources of passion in the GE literature. The sample is composed of all the 1164 entrepreneurship activities offered worldwide through Airbnb, Tour by Locals, and Withlocals on 5 May 2022. The study is supported by unsupervised machine learning models and seeks to find latent topics emerging from the analysis of the entrepreneurs’ descriptions and exposes the main correlated clustered dimensions. There are six main motivators behind GE platforms as a first step toward entrepreneurship: Experience, Passion for share, Knowledge, Classic traditions, Empowered community and local activities, and Well-being. It also confirms the correlation between passion and self-efficacy through experience, pointing them as main factors behind entrepreneurship in the GE. Five of the six sources of passion previously pointed by theory were found: Passion for growth, for people, for product/service, for innovation and for social mission. This study discloses self-efficacy and the sources of passion and points directions to practitioners involved in entrepreneurial activities in the GE ecosystem. This work used machine learning models to access quantitatively a paradigm that is inductive by nature. The results point to well-being as a significant factor to be addressed in future research regarding entrepreneurship. This research only studies individuals involved in the GE; as such, further studies should cohort new populations from different fields.

Machine learning approaches identify immunologic signatures of total and intact HIV DNA during long-term antiretroviral therapy.

Understanding the interplay between the HIV reservoir and the host immune system may yield insights into HIV persistence during antiretroviral therapy (ART) and inform strategies for a cure. Here, we applied machine learning (ML) approaches to cross-sectional high-parameter HIV reservoir and immunology data in order to characterize host-reservoir associations and generate new hypotheses about HIV reservoir biology. High-dimensional immunophenotyping, quantification of HIV-specific T cell responses, and measurement of genetically intact and total HIV proviral DNA frequencies were performed on peripheral blood samples from 115 people with HIV (PWH) on long-term ART. Analysis demonstrated that both intact and total proviral DNA frequencies were positively correlated with T cell activation and exhaustion. Years of ART and select bifunctional HIV-specific CD4 T cell responses were negatively correlated with the percentage of intact proviruses. A leave-one-covariate-out inference approach identified specific HIV reservoir and clinical-demographic parameters, such as age and biological sex, that were particularly important in predicting immunophenotypes. Overall, immune parameters were more strongly associated with total HIV proviral frequencies than intact proviral frequencies. Uniquely, however, expression of the IL-7 receptor alpha chain (CD127) on CD4 T cells was more strongly correlated with the intact reservoir. Unsupervised dimension reduction analysis identified two main clusters of PWH with distinct immune and reservoir characteristics. Using reservoir correlates identified in these initial analyses, decision tree methods were employed to visualize relationships among multiple immune and clinical-demographic parameters and the HIV reservoir. Finally, using random splits of our data as training-test sets, ML algorithms predicted with approximately 70% accuracy whether a given participant had qualitatively high or low levels of total or intact HIV DNA . The techniques described here may be useful for assessing global patterns within the increasingly high-dimensional data used in HIV reservoir and other studies of complex biology.

Machine learning approaches identify immunologic signatures of total and intact HIV DNA during long-term antiretroviral therapy

Understanding the interplay between the HIV reservoir and the host immune system may yield insights into HIV persistence during antiretroviral therapy (ART) and inform strategies for a cure. Here, we applied machine learning (ML) approaches to cross-sectional high-parameter HIV reservoir and immunology data in order to characterize host–reservoir associations and generate new hypotheses about HIV reservoir biology. High-dimensional immunophenotyping, quantification of HIV-specific T cell responses, and measurement of genetically intact and total HIV proviral DNA frequencies were performed on peripheral blood samples from 115 people with HIV (PWH) on long-term ART. Analysis demonstrated that both intact and total proviral DNA frequencies were positively correlated with T cell activation and exhaustion. Years of ART and select bifunctional HIV-specific CD4 T cell responses were negatively correlated with the percentage of intact proviruses. A leave-one-covariate-out inference approach identified specific HIV reservoir and clinical–demographic parameters, such as age and biological sex, that were particularly important in predicting immunophenotypes. Overall, immune parameters were more strongly associated with total HIV proviral frequencies than intact proviral frequencies. Uniquely, however, expression of the IL-7 receptor alpha chain (CD127) on CD4 T cells was more strongly correlated with the intact reservoir. Unsupervised dimension reduction analysis identified two main clusters of PWH with distinct immune and reservoir characteristics. Using reservoir correlates identified in these initial analyses, decision tree methods were employed to visualize relationships among multiple immune and clinical–demographic parameters and the HIV reservoir. Finally, using random splits of our data as training-test sets, ML algorithms predicted with approximately 70% accuracy whether a given participant had qualitatively high or low levels of total or intact HIV DNA . The techniques described here may be useful for assessing global patterns within the increasingly high-dimensional data used in HIV reservoir and other studies of complex biology.

Unsupervised Pattern Analysis to Differentiate Multiple Sclerosis Phenotypes Using Principal Component Analysis on Various MRI Sequences.

Background: Multiple sclerosis (MS) has two main phenotypes: relapse-remitting MS (RRMS) and progressive MS (PMS), distinguished by disability profiles and treatment response. Differentiating them using conventional MRI is challenging. Objective: This study explores the use of scaled subprofile modelling using principal component analysis (SSM/PCA) on MRI data to distinguish between MS phenotypes. Methods: MRI scans were performed on patients with RRMS (n = 30) and patients with PMS (n = 20), using the standard sequences T1w, T2w, T2w-FLAIR, and the myelin-sensitive sequences magnetisation transfer (MT) ratio (MTR), quantitative MT (qMT), inhomogeneous MT ratio (ihMTR), and quantitative inhomogeneous MT (qihMT). Results: SSM/PCA analysis of qihMT images best differentiated PMS from RRMS, with the highest specificity (87%) and positive predictive value (PPV) (83%), but a lower sensitivity (67%) and negative predictive value (NPV) (72%). Conversely, T1w data analysis showed the highest sensitivity (93%) and NPV (89%), with a lower PPV (67%) and specificity (53%). Phenotype classification agreement between T1w and qihMT was observed in 57% of patients. In the subset with concordant classifications, the sensitivity, specificity, PPV, and NPV were 100%, 88%, 90%, and 100%, respectively. Conclusions: SSM/PCA on MRI data revealed distinctive patterns for MS phenotypes. Optimal discrimination occurred with qihMT and T1w sequences, with qihMT identifying PMS and T1w identifying RRMS. When qihMT and T1w analyses align, MS phenotype prediction improves.

AI-based cluster analysis enables outcomes prediction among patients with increased LVM.

The traditional classification of left ventricular hypertrophy (LVH), which relies on left ventricular geometry, fails to correlate with outcomes among patients with increased LV mass (LVM). To identify unique clinical phenotypes of increased LVM patients using unsupervised cluster analysis, and to explore their association with clinical outcomes. Among the UK Biobank participants, increased LVM was defined as LVM index ≥72 g/m2 for men, and LVM index ≥55 g/m2 for women. Baseline demographic, clinical, and laboratory data were collected from the database. Using Ward's minimum variance method, patients were clustered based on 27 variables. The primary outcome was a composite of all-cause mortality with heart failure (HF) admissions, ventricular arrhythmia, and atrial fibrillation (AF). Cox proportional hazard model and Kaplan-Meier survival analysis were applied. Increased LVM was found in 4,255 individuals, with an average age of 64 ± 7 years. Of these patients, 2,447 (58%) were women. Through cluster analysis, four distinct subgroups were identified. Over a median follow-up period of 5 years (IQR: 4-6), 100 patients (2%) died, 118 (2.8%) were admissioned due to HF, 29 (0.7%) were admissioned due to VA, and 208 (5%) were admissioned due to AF. Univariate Cox analysis demonstrated significantly elevated risks of major events for patients in the 2nd (HR = 1.6; 95% CI 1.2-2.16; p < .001), 3rd (HR = 2.04; 95% CI 1.49-2.78; p < .001), and 4th (HR = 2.64; 95% CI 1.92-3.62; p < .001) clusters compared to the 1st cluster. Further exploration of each cluster revealed unique clinical phenotypes: Cluster 2 comprised mostly overweight women with a high prevalence of chronic lung disease; Cluster 3 consisted mostly of men with a heightened burden of comorbidities; and Cluster 4, mostly men, exhibited the most abnormal cardiac measures. Unsupervised cluster analysis identified four outcomes-correlated clusters among patients with increased LVM. This phenotypic classification holds promise in offering valuable insights regarding clinical course and outcomes of patients with increased LVM.

The utility of DNA methylation profiling in the diagnosis of un-, de- and trans-differentiated melanoma: a series of 11 cases.

Melanomas are recognised for their remarkable morphological plasticity. Some tumours may lose conventional features and/or acquire non-melanocytic characteristics, referred to as undifferentiated, dedifferentiated and transdifferentiated melanoma. Despite this phenotypical variability, melanomas typically maintain their cancer driver aberrations, affecting genes such as BRAF, NRAS and NF1. Currently, little is known about whether the DNA methylation profile follows the loss or change of differentiation or is retained despite extensive morphological transformation. In this study we analysed 11 melanoma cases, comprising six males and five females, with a median age of 67 years, including five undifferentiated, four trans-differentiated and two de-differentiated melanomas. Undifferentiated and trans-differentiated tumours either arose in a patient with known melanoma and/or presented in the groin/axilla with molecular alterations consistent with melanoma. Cases with heterologous differentiation resembled chondrosarcoma, osteosarcoma, angiosarcoma and rhabdomyosarcoma both morphologically and immunohistochemically, while undifferentiated tumours resembled undifferentiated pleomorphic sarcoma. Methylome profiling was performed, and unsupervised clustering analysis revealed nine cases (five undifferentiated, three trans-differentiated and one de-differentiated) to cluster closely together with conventional melanomas from a reference set. Two cases clustered separately with a distinct group of conventional melanomas exhibiting H3K27me3 loss. Despite loss of differentiation and phenotypical plasticity, methylation patterns seem to be retained in undifferentiated, de-differentiated and trans-differentiated melanomas and represent useful diagnostic tools to enhance diagnostic precision in these diagnostically challenging cases.

Mesenchymal-like immune-altered is the fourth robust triple-negative breast cancer molecular subtype.

Robust molecular subtyping of triple-negative breast cancer (TNBC) is a prerequisite for the success of precision medicine. Today, there is a clear consensus on three TNBC molecular subtypes: luminal androgen receptor (LAR), basal-like immune-activated (BLIA), and basal-like immune-suppressed (BLIS). However, the debate about the robustness of other subtypes is still open. An unprecedented number (n = 1942) of TNBC patient data was collected. Microarray- and RNAseq-based cohorts were independently investigated. Unsupervised analyses were conducted using k-means consensus clustering. Clusters of patients were then functionally annotated using different approaches. Prediction of response to chemotherapy and targeted therapies, immune checkpoint blockade, and radiotherapy were also screened for each TNBC subtype. Four TNBC subtypes were identified in the cohort: LAR (19.36%); mesenchymal stem-like (MSL/MES) (17.35%); BLIA (31.06%); and BLIS (32.23%). Regarding the MSL/MES subtype, we suggest renaming it to mesenchymal-like immune-altered (MLIA) to emphasize its specific histological background and nature of immune response. Treatment response prediction results show, among other things, that despite immune activation, immune checkpoint blockade is probably less or completely ineffective in MLIA, possibly caused by mesenchymal background and/or an enrichment in dysfunctional cytotoxic T lymphocytes. TNBC subtyping results were included in the bc-GenExMiner v5.0 webtool ( http://bcgenex.ico.unicancer.fr ). The mesenchymal TNBC subtype is characterized by an exhausted and altered immune response, and resistance to immune checkpoint inhibitors. Consensus for molecular classification of TNBC subtyping and prediction of cancer treatment responses helps usher in the era of precision medicine for TNBC patients.

Using Bioinformatics and Machine Learning to Predict the Genetic Characteristics of Ferroptosis-Cuproptosis-Related Genes Associated with Sleep Deprivation.

Sleep deprivation (SD), a common sleep disease in clinic, has certain risks, and its pathogenesis is still unclear. This study aimed to identify ferroptosis-cuproptosis-related genes (FCRGs) associated with SD through bioinformatics and machine learning, thus elucidating their biological significance and clinical value. SD-DEGs were obtained from GEO. We intersected key WGCNA module genes of DE-FCRGs with SD-DEGs to obtain SD-DE-FCRGs. GO and KEGG analyses were performed. Machine learning was used to screen SD-DE-FCRGs, and filtered genes were intersected to obtain SD characteristic genes. ROC curves were used to evaluate the accuracy of SD characteristic genes. CIBERSORT was used to analyze the correlation between SD-DE-FCRGs and immune cells. We constructed a ceRNA network of SD-DE-FCRGs and used DGIbd to predict gene drug targets. The 156 DEGs were identified from GSE98566. Five SD-DE-FCRGs from DE- FCRGs and SD-DEGs were analyzed via WGCNA, and enrichment analysis involved mainly ribosome regulation, mitochondrial pathways, and neurodegenerative diseases. Machine learning was used to obtain Four SD-DE-FCRGs (IKZF1, JCHAIN, MGST3, and UQCR11), and these gene analyses accurately evaluated the distribution model (AUC=0.793). Immune infiltration revealed that SD hub genes were correlated with most immune cells. Unsupervised cluster analysis revealed significant differential expression of immune-related genes between two subtypes. GSVA and GSEA revealed that enriched biological functions included oxidative phosphorylation, ribonucleic acid, metabolic diseases, activation of oxidative phosphorylation, and other pathways. Four SD-DE-FCRGs associated with 29 miRNAs were identified via the construction of a ceRNA network. The important target lenalidomide of IKZF1 was predicted. We first used bioinformatics and machine learning to screen four SD-DE-FCRGs. These genes may affect the involvement of infiltrating immune cells in pathogenesis of SD by regulating FCRGs. We predicted that lenalidomide may target IKZF1 from SD-DE-FCRGs.

Epigenomic and Transcriptomic Profiling of Solitary Fibrous Tumors Identifies Site-Specific Patterns and Candidate Genes Regulated by DNA Methylation

A solitary fibrous tumor (SFT) is a rare mesenchymal neoplasm that can arise at any anatomical site and is characterized by recurrent NAB2::STAT6 fusions and metastatic progression in 10% to 30%. The cell of origin has not been identified. Despite some progress in understanding the contribution of heterogeneous fusion types and secondary mutations to SFT biology, epigenetic alterations in extrameningeal SFT remain largely unexplored, and most sarcoma research to date has focused on the use of methylation profiling for tumor classification. We interrogated genome-wide DNA methylation in 79 SFTs to identify informative epigenetic changes. RNA-seq data from targeted panels and data from the Cancer Genome Atlas (TCGA) were used for orthogonal validation of selected findings. In unsupervised clustering analysis, the top 500 most variable CpGs segregated SFTs by primary anatomical site. Differentially methylated genes associated with the primary SFT site included EGFR; TBX15; multiple HOX genes; and their cofactors EBF1, EBF3, and PBX1; as well as RUNX1 and MEIS1. Of the 20 DMGs interrogated on the RNA-seq panel, 12 were significantly differentially expressed according to site. However, except TBX15, most of these also showed differential expression according to NAB2::STAT6 fusion type, suggesting that the fusion oncogene contributes to the transcriptional regulation of these genes. Transcriptomic data confirmed an inverse correlation between gene methylation and the expression of TBX15 in both SFT and TCGA sarcomas. TBX15 also showed differential mRNA expression and 5′ UTR methylation between tumors in different anatomical sites in TCGA data. In all analyses, TBX15 methylation and mRNA expression retained the strongest association with tissue of origin in SFT and other sarcomas, suggesting a possible marker to distinguish metastatic tumors from new primaries without genomic profiling. Epigenetic signatures may further help to identify SFT progenitor cells at different anatomical sites.

P016 METAPROTEOMICS ANALYSIS IN HEALTHY INDIVIDUALS REVEALS A HIGH CARDIOVASCULAR RISK PROFILE ACROSS BLOOD PRESSURE, DIET, HUMAN AND MICROBIAL PROTEINS

Background/Objective: The gut microbiota is a crucial link between diet and cardiovascular health by producing beneficial metabolites, such as short-chain fatty acids (SCFAs), through fibre fermentation. However, most studies have focused on microbial DNA to infer microbiota function. Here, we employed metaproteomics, a novel method to capture gut human and microbiota protein expression profiles concurrently. We aimed to investigate the crosstalk between gut microbiota, diet, host gut health, and blood pressure (BP). Methods: Mass spectrometry-based metaproteomic analysis was performed on 61 faecal samples with paired ambulatory BP monitoring, food intake (food frequency questionnaire), and demographic data. Results: We identified 21,594 microbial and 1,072 human proteins. Traditional risk factors (age, BMI, sex, BP) explained only ∼10% of the proteome variance. However, unsupervised clustering analysis of both human and microbial proteome revealed two distinct clusters (low-risk and high-risk) with significantly different 24-hour systolic BP (mean±SD, low-risk 116±13mmHg vs high-risk: 126±15mmHg, P=0.016), women prevalence (72.4% vs 40.6%, P=0.025), and diet quality (assessed as Athlete Diet Index, 61.6±6.7 vs 57.8±7.6, P=0.044). In the human proteome, the low-risk group had lower expression of the VEGFA-VEGFR2 signalling pathway and tight junction proteins and higher SCFA production (particularly propionate), suggesting an anti-inflammatory gut environment. In the microbial proteome, the low-risk group had higher activity of inorganic ion transport and metabolism-related proteins, with increased expression of acetate kinase that produces both the SCFAs acetate and propionate, particularly in primary fibre-fermenting bacteria, including Phocaeicola dorei and Blautia wexlerae. Finally, via co-expression networks, we identified profilin-1 (PFN1), which contributes to hypertension and atherosclerosis, as a crucial human protein associated with several bacteria present in the high-risk group. Conclusions: Employing an unsupervised clustering based on human and microbial proteomics, we identified two groups of participants with varying traditional and emerging cardiovascular risk factors. This was explained by complex interactions among diet, gut microbiota, and BP. This underscores the need for sophisticated models to understand multifactorial risk factors for cardiovascular disease.

Presubicular VIP expressing interneurons receive facilitating excitation from anterior thalamus

The presubiculum is part of the parahippocampal cortex and plays a fundamental role for orientation in space. Many principal neurons of the presubiculum signal head direction, and show persistent firing when the head of an animal is oriented in a specific preferred direction. GABAergic neurons of the presubiculum control the timing, sensitivity and selectivity of head directional signals from the anterior thalamic nuclei. However, the role of vasoactive intestinal peptide (VIP) expressing interneurons in the presubicular microcircuit has not yet been addressed. Here, we examined the intrinsic properties of VIP interneurons as well as their input connectivity following photostimulation of anterior thalamic axons. We show that presubicular VIP interneurons are more densely distributed in superficial than in deep layers. They are highly excitable. Three groups emerged from the unsupervised cluster analysis of their electrophysiological properties. We demonstrate a frequency dependent recruitment of VIP cells by thalamic afferences and facilitating synaptic input dynamics. Our data provide initial insight into the contribution of VIP interneurons for the integration of thalamic head direction information in the presubiculum.

Transition in Quality Of Life Among Older Patients With Bladder Cancer and Its Association With Treatment: A Clustering Approach

Purpose: To classify older patients with bladder cancer (BC) based on quality-of-life (QoL) measures and explore the clusters transitions over a 2-year period and patient characteristics, BC stages, and treatment. Patients with BC report a significant decline in functional status and QoL. The association of this transition and patient characteristics and treatment over time is not known. We aim to explore this association. Methods: Data were obtained from patients with BC in the SEER-MHOS database (1998-2021). Unsupervised cluster analysis was employed to identify homogeneous clusters of patients with similar functionality based on age, physical and mental component score, and ability to perform activities of daily living. Univariate and multivariable analyses were performed to assess the association of patient characteristics and treatment on cluster transitions over a 2-year interval. Results: The final cohort consisted of 2934 (age 65 or older) older patients with BC. We identified 3 distinct clusters: 67.9% in Cluster 1; 25.4% in Cluster 2; and 6.7% in Cluster 3. After 2 years, 11.7% patients showed improvement and 14% experienced a decline. Cancer/treatment characteristics were not associated with cluster transition. Younger age and being married were identified as factors associated with improvement. Older age, non–homeownership, and last-year fall were associated with transition to less healthy cluster. Conclusions: Over a 2-year interval after BC diagnosis, a quarter of patients will have substantial changes in QoL. No significant association was observed between cluster transition and BC characteristics. Age as well as marital, fall, and homeowner status do impact this transition.

Deciphering bone marrow engraftment after allogeneic stem cell transplantation in humans using single-cell analyses.

BACKGROUNDDonor cell engraftment is a prerequisite of successful allogeneic hematopoietic stem cell transplantation. Based on peripheral blood analyses, it is characterized by early myeloid recovery and T and B cell lymphopenia. However, cellular networks associated with bone marrow engraftment of allogeneic human cells have been poorly described.METHODSMass cytometry and CITE-Seq analyses were performed on bone marrow cells 3 months after transplantation in patients with acute myelogenous leukemia.RESULTSMass cytometric analyses in 26 patients and 20 healthy controls disclosed profound alterations in myeloid and B cell progenitors, with a shift toward terminal myeloid differentiation and decreased B cell progenitors. Unsupervised analysis separated recipients into 2 groups, one of them being driven by previous graft-versus-host disease (R2 patients). We then used single-cell CITE-Seq to decipher engraftment, which resolved 36 clusters, encompassing all bone marrow cellular components. Hematopoiesis in transplant recipients was sustained by committed myeloid and erythroid progenitors in a setting of monocyte-, NK cell-, and T cell-mediated inflammation. Gene expression revealed major pathways in transplant recipients, namely, TNF-α signaling via NF-κB and the IFN-γ response. The hallmark of allograft rejection was consistently found in clusters from transplant recipients, especially in R2 recipients.CONCLUSIONBone marrow cell engraftment of allogeneic donor cells is characterized by a state of emergency hematopoiesis in the setting of an allogeneic response driving inflammation.FUNDINGThis study was supported by the French National Cancer Institute (Institut National du Cancer; PLBIO19-239) and by an unrestricted research grant by Alexion Pharmaceuticals.

Cluster Analysis Identifies Clinical Phenotypes of Primary Hyperhidrosis

Introduction: Identifying subgroups of patients with primary hyperhidrosis (PHH) can improve the understanding of the disease pathophysiology. The study objective was to determine the naturally occurring subgroups of patients with PHH based on clinical characteristics. Methods: In this retrospective cohort study, data were collected from participants included in a clinical trial. The data were collected between January 2020 and June 2021 from outpatients with PHH attending a dermatologic department in Denmark. Overall, 84 patients with PHH were screened for inclusion in the clinical trial. Of these, 41 met the eligibility criteria. Four participants were excluded because of missing data. The main outcome was the identification of subgroups of patients with PHH using an unsupervised hierarchical cluster analysis. Results: Overall, 37 patients were included {28 (76.7%) females; median age at inclusion 28.0 (interquartile range [IQR] 24.0–38.3); median body mass index 24.9 (IQR 20.9–27.4); median age of onset 13.0 (IQR 9.5–18.5); and 26 (70.3%) had a familial disposition toward PHH}. Two clusters of 18 and 17 patients were identified. The first cluster had, when compared to the second, a younger age of onset (median age 11.0 [IQR 0–13.0] vs. 17.0 [IQR 15.0–21.0], p = 0.003) and higher sweat rates on gravimetry (median 175.0 [IQR 121.2–252.5] vs. 40.0 [IQR 20.0–60.0] milligrams of sweat/5 min, p < 0.001) and transepidermal water loss (median 93.7 [IQR 91.2–97.8] vs. 59.0 [IQR 44.4–73.2] g/m2/h, p < 0.001). No differences were observed for the other variables. Conclusions: This study identifies 2 subgroups of patients with PHH. The patients with an onset of PHH during childhood had a substantially higher sweat and evaporation rate in adulthood than those with an onset during adolescence. These findings may imply a changed understanding of the pathophysiology of PHH, by indicating that an early disease onset can lead to a worse disease course.

Changes in monocyte subsets in volunteers who received an oral wild-type Salmonella Typhi challenge and reached typhoid diagnosis criteria.

An oral Controlled Human Infection Model (CHIM) with wild-type S. Typhi was re-established allowing us to explore the development of immunity. In this model, ~55% of volunteers who received the challenge reached typhoid diagnosis criteria (TD), while ~45% did not (NoTD). Intestinal macrophages are one of the first lines of defense against enteric pathogens. Most organs have self-renewing macrophages derived from tissue-resident progenitor cells seeded during the embryonic stage; however, the gut lacks these progenitors, and all intestinal macrophages are derived from circulating monocytes. After infecting gut-associated lymphoid tissues underlying microfold (M) cells, S. Typhi causes a primary bacteremia seeding organs of the reticuloendothelial system. Following days of incubation, a second bacteremia and clinical disease ensue. S. Typhi likely interacts with circulating monocytes or their progenitors in the bone marrow. We assessed changes in circulating monocytes after CHIM. The timepoints studied included 0 hours (pre-challenge) and days 1, 2, 4, 7, 9, 14, 21 and 28 after challenge. TD participants provided extra samples at the time of typhoid diagnosis, and 48-96 hours later (referred as ToD). We report changes in Classical Monocytes -CM-, Intermediate Monocytes -IM- and Non-classical Monocytes -NCM-. Changes in monocyte activation markers were identified only in TD participants and during ToD. CM and IM upregulated molecules related to interaction with bacterial antigens (TLR4, TLR5, CD36 and CD206). Of importance, CM and IM showed enhanced binding of S. Typhi. Upregulation of inflammatory molecules like TNF-α were detected, but mechanisms involved in limiting inflammation were also activated (CD163 and CD354 downregulation). CM upregulated molecules to interact/modulate cells of the adaptive immunity, including T cells (HLA-DR, CD274 and CD86) and B cells (CD257). Both CM and IM showed potential to migrate to the gut as integrin α4β7 was upregulated. Unsupervised analysis revealed 7 dynamic cell clusters. Five of these belonged to CM showing that this is the main population activated during ToD. Overall, we provide new insights into the changes that diverse circulating monocyte subsets undergo after typhoid diagnosis, which might be important to control this disease since these cells will ultimately become intestinal macrophages once they reach the gut.

Disentangling the riddle of systemic lupus erythematosus with antiphospholipid syndrome: blood transcriptome analysis reveals a less-pronounced IFN-signature and distinct molecular profiles in venous versus arterial events

IntroductionSystemic lupus erythematosus with antiphospholipid syndrome (SLE-APS) represents a challenging SLE endotype whose molecular basis remains unknown.MethodsWe analysed whole-blood RNA-sequencing data from 299 patients with SLE (108 SLE-antiphospholipid antibodies (aPL)-positive,...

Exploring the transcriptomic and metabolomic profiles of adipose tissues: Insights and implications for fat grafting

BackgroundExpanding on our prior research on murine fat grafts' metabolic shift, this study delves deeper into the metabolic profiles of human adipose tissues, specifically the superficial subcutaneous adipose tissue (SSAT) and deep subcutaneous adipose tissue (DSAT). MethodsUtilizing RNA-sequencing, metabolome, and metabolic flux analyses, we examined SSAT and DSAT samples obtained during deep inferior epigastric perforator flap breast reconstructions. Transcript data underwent unsupervised hierarchical clustering and Gene set enrichment analysis. Metabolomics involved analyzing samples for cationic and anionic metabolites via Capillary Electrophoresis Time-of-Flight Mass Spectrometry (CE-TOFMS), followed by principal component analysis (PCA) and heat map generation. Additionally, primary adipocytes from SSAT and DSAT were assessed using the Seahorse® extracellular flux analyzer. ResultsPCA and heat map analyses highlighted distinct transcriptome and metabolome differences between SSAT and DSAT. SSAT predominantly upregulated genes linked to adipogenesis (false discovery rate [FDR] q < 0.0001), oxidative phosphorylation (FDR q < 0.0001), fatty acid metabolism (FDR q <0.0001), and glycolysis (FDR q = 0.001). In contrast, DSAT showed a significant upregulation in inflammatory response genes (FDR q <0.05). Metabolite analysis revealed an abundance of glycolytic metabolites in SSAT, whereas DSAT was rich in metabolites associated with fatty acid metabolism and oxidative phosphorylation. Cellular flux analysis further confirmed SSAT's elevated glycolysis and spare oxidative phosphorylation capacities. ConclusionOur results highlight the metabolic uniqueness of SSAT and DSAT in humans, with SSAT exhibiting superior metabolic flexibility. The implications of these metabolic differences, especially in fat grafting procedures, necessitate further research and exploration in future studies. Data availabilityThe ribonucleic acid sequencing data were deposited in the Gene Expression Omnibus database (GSE216803). https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE216803 (GEO token: qnsngiuqplqdpaf).

Metabolites profiling, in-vitro and molecular docking studies of five legume seeds for Alzheimer’s disease

Even though legumes are valuable medicinal plants with edible seeds that are extensively consumed worldwide, there is little information available on the metabolic variations between different dietary beans and their influence as potential anti-cholinesterase agents. High-resolution liquid chromatography coupled with mass spectrometry in positive and negative ionization modes combined with multivariate analysis were used to explore differences in the metabolic profiles of five commonly edible seeds, fava bean, black-eyed pea, kidney bean, red lentil, and chickpea. A total of 139 metabolites from various classes were identified including saponins, alkaloids, phenolic acids, iridoids, and terpenes. Chickpea showed the highest antioxidant and anti-cholinesterase effects, followed by kidney beans. Supervised and unsupervised chemometric analysis determined that species could be distinguished by their different discriminatory metabolites. The major metabolic pathways in legumes were also studied. Glycerophospholipid metabolism was the most significantly enriched KEGG pathway. Pearson’s correlation analysis pinpointed 18 metabolites that were positively correlated with the anti-cholinesterase activity. Molecular docking of the biomarkers to the active sites of acetyl- and butyryl-cholinesterase enzymes revealed promising binding scores, validating the correlation results. The present study will add to the metabolomic analysis of legumes and their nutritional value and advocate their inclusion in anti-Alzheimer’s formulations.

Traditional Processing Can Enhance the Medicinal Effects of Polygonatum cyrtonema by Inducing Significant Chemical Changes in the Functional Components in Its Rhizomes.

The aims of this study were to explore the significant chemical changes in functional components induced by the traditional processing method and evaluate whether this method based on nine cycles of steaming and drying can effectively enhance the medicinal effects of Polygonatum cyrtonema rhizome. A global analysis on dynamic changes in secondary metabolites during nine processing cycles was performed, and the significantly differentially accumulated secondary metabolites were initially identified based on the secondary metabolome. Unsupervised principal component analysis (PCA), hierarchical clustering analysis (HCA), and orthogonal partial least squares discriminant analysis (OPLA-DA) on secondary metabolites clearly showed that processing significantly increased the global accumulation of secondary metabolites in processed P. cyrtonema rhizomes compared to unprocessed crude rhizomes. The first six processing cycles induced drastic changes in the accumulation of functional components, while the last three did not induce further changes. The accumulations of most functional components were significantly enhanced after the first three cycles and stabilized after six cycles; meanwhile, the first three cycles also led to numerous new components. However, the enhancing effects were unavoidably reversed or weakened under continued processing lasting 6-9 cycles. Furthermore, continued processing also reduced the contents of a small number of original components to undetectable levels. Processing induced some significantly enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, among which the first three processing cycles enhanced the synthesis of various secondary metabolites and significantly affected the metabolisms of amino acids. In conclusion, this study not only reveals that processing can effectively enhance the medicinal effects, by two main mechanisms including enhancing chemical synthesis and inducing structural transformation of functional components, but also provides theoretical guidance for the optimization of the traditional processing method based on nine cycles of steaming and drying for achieving optimal effects on enhancing the medicinal effects of P. cyrtonema rhizome.

Encoding of female mating dynamics by a hypothalamic line attractor

Females exhibit complex, dynamic behaviours during mating with variable sexual receptivity depending on hormonal status1–4. However, how their brains encode the dynamics of mating and receptivity remains largely unknown. The ventromedial hypothalamus, ventrolateral subdivision contains oestrogen receptor type 1-positive neurons that control mating receptivity in female mice5,6. Here, unsupervised dynamical system analysis of calcium imaging data from these neurons during mating uncovered a dimension with slow ramping activity, generating a line attractor in neural state space. Neural perturbations in behaving females demonstrated relaxation of population activity back into the attractor. During mating, population activity integrated male cues to ramp up along this attractor, peaking just before ejaculation. Activity in the attractor dimension was positively correlated with the degree of receptivity. Longitudinal imaging revealed that attractor dynamics appear and disappear across the oestrus cycle and are hormone dependent. These observations suggest that a hypothalamic line attractor encodes a persistent, escalating state of female sexual arousal or drive during mating. They also demonstrate that attractors can be reversibly modulated by hormonal status, on a timescale of days.