Performance Of Principal Component Analysis Research Articles

Genetic variability of histidine-rich protein 2 repeat sequences: Misleading factor in true determination of Plasmodium falciparum in different population

PurposeGenetically diverse parasites enhances resistance against antimalarials, vaccines and host immune responses. The present study was designed to evaluate the role played by Plasmodium falciparum genetic diversity in predicting the real world malarial population. MethodsInitially, the incidence pattern of all four northern Indian malarial species was examined using 18S rRNA gene and performed principal component analysis (PCA) based on frequencies of Plasmodium species. Consequently, genetic variance of Plasmodium falciparum histidine-rich protein-2 (Pfhrp2) gene among different malarial populations were compared using phylogenetic analysis. Multi-dimensional scaling was performed to assess genetic similarities and distances among studied populations. ResultsOf total 2168 patients screened, 561 patients with fever of unknown origin were included. 18S rRNA and Pfhrp2 genes were amplified in 78 and 45 samples, respectively. Among them 13.9%(78/561) patients had Plasmodium infection. Infections by P. falciparum, P. vivax and mixed infections were diagnosed among 47(60.2%) and 28(35.9%) and 3(3.8%) patients, respectively. We found eight types of Pfhrp2 amino acid sequence repeats among northern Indian population. The PCA findings were in line with genetic diversity and phylogenetic data. Temporal analysis showed the proportion of total diversity present in total subpopulation (ΔS/ΔT) was maximum for P. falciparum. Conclusions: Higher incidence of Pfhrp2 sequence variation through genetic recombination among multiple strains during sexual reproduction is potentially correlated with high transmission activity. This sequence variation might alter RDT detection sensitivities for different parasites by modulating the structure and frequency of antigenic epitopes.

Cardiopulmonary exercise testing complements both spirometry and nuclear imaging for assessing sarcoidosis stage and for monitoring disease activity.

Pulmonary sarcoidosis is a systemic disease that can confound established follow-up tools. Pulmonary function tests (PFTs) are recommended in initial and follow-up patient evaluations yet are imperfect predictors of disease progression. The cardiopulmonary exercise test (CPET) is another potentially useful monitoring tool, although previous studies report conflicting findings regarding which variables are altered by the disease. Nuclear imaging tests are also employed to assess inflammatory activity and may be predictive of functional deterioration. We asked whether PFTs or CPET are more diagnostic of disease stage, which subsets of functional variables are impacted by the disease, and how these relate to nuclear imaging signs of active inflammation. We collected retrospective data (spirometry, CPET, Gallium-67 scintigraphy, 18F-FDG PET/CT) from 48 patients and 10 controls. Disease severity was assessed following Scadding classification. First, we correlated individual PFTs and CPET parameters to Scadding stage and nuclear imaging data. Next, we performed Principal Component Analysis (PCA) on PFTs and CPET parameters, separated into respiratory, cardiovascular and metabolic subsets. Finally, we constructed multiple regression models to determine which variable subsets were the best predictors of Scadding stage and disease activity. The majority of PFTs and CPET single parameters were significantly correlated with patient stage, while only few correlated with disease activity. Nevertheless, multiple regression models were able to significantly relate PFTs and CPET to both disease stage and activity. Additionally, these analyses highlighted CPET cardiovascular parameters as the best overall predictors of disease stage and activity. Our results display how CPET and spirometry data complement each other for sarcoidosis disease staging, and how these tests are able to detect disease activity. Our findings suggest that CPET, a repeatable and non-invasive functional test, should be more routinely performed and taken into account in sarcoidosis patient follow-up.

Abstract 6234: Single-cell analysis of state-transition from health to leukemia and the leukemic cell heterogeneities in inv(16) acute myeloid leukemia (AML)

Abstract AML is an aggressive hematological malignancy with heterogeneous genetic abnormalities. Using a conditional knock-in mouse model (Cbfb56M/+/Mx1-Cre) to mimic the somatic acquisition of fusion gene CBFB-MYH11 (CM), created in the recurrent chromosome translocation inv(16)(p13q22) of AML, we previously reported that the blood transcriptome undergoes state-transition from health to leukemia. In this study, we analyze the transcriptomic alterations and leukemic cell heterogeneities in a leukemia state-space using single-cell RNA sequencing (scRNA-seq). CM expression was induced in Cbfb56M/+/Mx1-Cre mice by poly (I:C) injection, and scRNA-seq analysis was performed on peripheral blood mononuclear cells (PBMC) and bone marrow (BM) cells from leukemic mice and age-matched wild-type (WT) controls. More than 105,000 cells were subjected to the following dimensionality reduction and cell type annotation. By performing principal component analysis (PCA), we observed a Y-shape cell distribution with emergence of a divergent branch occupied by leukemic cells which can be mapped to a leukemic state. The principal components 1 and 2 (PC1 and PC2) clearly distinguish the leukemic cells from normal cell types such as myeloid cells and lymphocytes. By accessing the eigenvalue of genes for PC1 and PC2, we quantified the crucial genes and pathways related to leukemia progression at a single-cell level. We characterized cKit+ leukemic cell clusters by transcriptome-based correlation and gene expression pattern and found that they resembled stem cells and megakaryocytic-erythroid progenitors (MEP) with dysregulated pathways, including upregulated "inflammatory response" and downregulated pathways related to Myc targets, oxidative phosphorylation, mitochondrial protein translation, and ribosomal biogenesis/assembly. Examining the 31 clusters (C0-C30) seen in cKit+ BM cells, we identified 9 leukemic clusters exhibiting distinct features, including high heme-metabolism (C0, C1, and C2), high proliferation (C1, C8), S phase (C4, C10), low oxidative phosphorylation (C2, C5, C17), high Cd9 (C10), high Egfl7 (C7, C8). In conclusion, our study unveils state-transition from health to leukemia at single-cell resolution, and uncovers diverse biological heterogeneities associated with the inv(16) AML state. Further refinement of transcriptome state-transition and single-cell analysis will provide an analytical framework for modeling the dynamic changes during disease evolution at the single-cell level. Citation Format: Yu-Hsuan Fu, Lianjun Zhang, Ying-Chieh Chen, David E. Frankhouser, Lisa Uechi, Denis O'Meally, Sergio Branciamore, Guido Marcucci, Russell Rockne, Ya-Huei Kuo. Single-cell analysis of state-transition from health to leukemia and the leukemic cell heterogeneities in inv(16) acute myeloid leukemia (AML) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6234.

Modeling COVID-19 Disease with Deterministic and Data-Driven Models Using Daily Empirical Data in the United Kingdom

The COVID-19 pandemic has had a significant impact on countries worldwide, including the United Kingdom (UK). The UK has faced numerous challenges, but its response, including the rapid vaccination campaign, has been noteworthy. While progress has been made, the study of the pandemic is important to enable us to properly prepare for future epidemics. Collaboration, vigilance, and continued adherence to public health measures will be crucial in navigating the path to recovery and building resilience for the future. In this article, we propose an overview of the COVID-19 situation in the UK using both mathematical (a nonlinear differential equation model) and statistical (time series modeling on a moving window) models on the transmission dynamics of the COVID-19 virus from the beginning of the pandemic up until July 2022. This is achieved by integrating a hybrid model and daily empirical case and death data from the UK. We partition this dataset into before and after vaccination started in the UK to understand the influence of vaccination on disease dynamics. We used the mathematical model to present some mathematical analyses and the calculation of the basic reproduction number (R0). Following the sensitivity analysis index, we deduce that an increase in the rate of vaccination will decrease R0. Also, the model was fitted to the data from the UK to validate the mathematical model with real data, and we used the data to calculate time-varying R0. The homotopy perturbation method (HPM) was used for the numerical simulation to demonstrate the dynamics of the disease with varying parameters and the importance of vaccination. Furthermore, we used statistical modeling to validate our model by performing principal component analysis (PCA) to predict the evolution of the spread of the COVID-19 outbreak in the UK on some statistical predictor indicators from time series modeling on a 14-day moving window for detecting which of these indicators capture the dynamics of the disease spread across the epidemic curve. The results of the PCA, the index of dispersion, the fitted mathematical model, and the mathematical model simulation are all in agreement with the dynamics of the disease in the UK before and after vaccination started. Conclusively, our approach has been able to capture the dynamics of the pandemic at different phases of the disease outbreak, and the result presented will be useful to understand the evolution of the disease in the UK and future and emerging epidemics.

Identification of two hidden clinical subgroups among men with idiopathic cryptozoospermia.

Are there subgroups among patients with cryptozoospermia pointing to distinct etiologies? We reveal two distinct subgroups of cryptozoospermic (Crypto) patients based on testicular tissue composition, testicular volume, and FSH levels. Cryptozoospermic patients present with a sperm concentration below 0.1 million/ml. While the etiology of the severely impaired spermatogenesis remains largely unknown, alterations of the spermatogonial compartment have been reported including a reduction of the reserve stem cells in these patients. To assess whether there are distinct subgroups among cryptozoospermic patients, we applied the statistical method of cluster analysis. For this, we retrospectively selected 132 cryptozoospermic patients from a clinical database who underwent a testicular biopsy in the frame of fertility treatment at a university hospital. As controls (Control), we selected 160 patients with obstructive azoospermia and full spermatogenesis. All 292 patients underwent routine evaluation for endocrine, semen, and histological parameters (i.e. the percentage of tubules with elongated spermatids). Moreover, outcome of medically assisted reproduction (MAR) was assessed for cryptozoospermic (n = 73) and Control patients (n = 87), respectively. For in-depth immunohistochemical and histomorphometrical analyses, representative tissue samples from cryptozoospermic (n = 27) and Control patients (n = 12) were selected based on cluster analysis results and histological parameters. This study included two parts: firstly using clinical parameters of the entire cohort of 292 patients, we performed principal component analysis (PCA) followed by hierarchical clustering on principal components (i.e. considering hormonal values, ejaculate parameters, and histological information). Secondly, for histological analyses seminiferous tubules were categorized according to the most advanced germ cell type present in sections stained with Periodic acid Schif. On the selected cohort of 39 patients (12 Control, 27 cryptozoospermic), we performed immunohistochemistry for spermatogonial markers melanoma-associated antigen 4 (MAGEA4) and piwi like RNA-mediated gene silencing 4 (PIWIL4) followed by quantitative analyses. Moreover, the morphologically defined Adark spermatogonia, which are considered to be the reserve stem cells, were quantified. The PCA and hierarchical clustering revealed three different clusters, one of them containing all Control samples. The main factors driving the sorting of patients to the clusters were the percentage of tubules with elongated spermatids (Cluster 1, all Control patients and two cryptozoospermic patients), the percentage of tubules with spermatocytes (Cluster 2, cryptozoospermic patients), and tubules showing a Sertoli cells only phenotype (Cluster 3, cryptozoospermic patients). Importantly, the percentage of tubules containing elongated spermatids was comparable between Clusters 2 and 3. Additional differences were higher FSH levels (P < 0.001) and lower testicular volumes (P < 0.001) in Cluster 3 compared to Cluster 2. In the spermatogonial compartment of both cryptozoospermic Clusters, we found lower numbers of MAGEA4+ and Adark spermatogonia but higher proportions of PIWIL4+ spermatogonia, which were significantly correlated with a lower percentage of tubules containing elongated spermatids. In line with this common alteration, the outcome of MAR was comparable between Controls as well as both cryptozoospermic Clusters. While we have uncovered the existence of subgroups within the cohort of cryptozoospermic patients, comprehensive genetic analyses remain to be performed to unravel potentially distinct etiologies. The novel insight that cryptozoospermic patients can be divided into two subgroups will facilitate the strategic search for underlying genetic etiologies. Moreover, the shared alterations of the spermatogonial stem cell compartment between the two cryptozoospermic subgroups could represent a general response mechanism to the reduced output of sperm, which may be associated with a progressive phenotype. This study therefore offers novel approaches towards the understanding of the etiology underlying the reduced sperm formation in cryptozoospermic patients. German research foundation CRU 326 (grants to: SDP, NN). Moreover, we thank the Faculty of Medicine of the University of Münster for the financial support of Lena Charlotte Schülke through the MedK-program. We acknowledge support from the Open Access Publication Fund of the University of Münster. The authors have no potential conflicts of interest. N/A.

Unlocking therapeutic potential: computational insights into TREM2 protein targeting with FDA-approved drugs for neurodegeneration

Neurodegenerative diseases such as Alzheimer’s disease (AD) pose a significant global health challenge that requires the exploration of innovative therapeutic strategies. Triggering receptor expressed on myeloid cells-2 (TREM2) is one of the critical proteins involved in immune regulation and neuroinflammation. It has emerged as a promising therapeutic target to develop treatments for neurodegenerative disorders like AD. Here, we employed a comprehensive virtual screening approach to identify potential small molecule inhibitors among FDA-approved drugs for TREM2. The docking study reveals significant binding affinity, ranging from −7.8 kcal/mol to −8.5 kcal/mol, for the elucidated hits against TREM2, accompanied by several crucial interactions. Among the repurposed drugs identified in the initial screening, Carpipramine, Clocapramine, and Pimozide stood out due to their notable binding potential and favorable drug profiling. Further, we conducted molecular dynamics (MD) simulations on the selected molecules that probed their structural dynamics and stability within the TREM2 binding pocket. The structural parameters and hydrogen bond dynamics remained remarkably stable throughout the simulated trajectories. Furthermore, we performed principal component analysis (PCA) and constructed free energy landscapes (FELs) to gain deeper insights into ligand binding and conformational flexibility of TREM2. The findings revealed that the elucidated molecules, Carpipramine, Clocapramine, and Pimozide, exhibited an exceptional fit within the binding pocket of TREM2 with remarkable stability and interaction patterns throughout the 500 ns simulation window. Interestingly, these molecules possessed a spectrum of anti-neurodegenerative properties and favorable drug profiles, which suggest their potential as promising drug candidates for repurposing in the treatment of AD. Communicated by Ramaswamy H. Sarma

Evaluation of commercial nanofiltration and reverse osmosis membrane filtration to remove per-and polyfluoroalkyl substances (PFAS): Effects of transmembrane pressures and water matrices.

Perfluoroalkyl and polyfluoroalkyl substances (PFASs) are now widely found in aquatic ecosystems, including sources of drinking water and portable water, due to their increasing prevalence. Among different PFAS treatment or separation technologies, nanofiltration (NF) and reverse osmosis (RO) both yield high rejection efficiencies (>95%) of diverse PFAS in water; however, both technologies are affected by many intrinsic and extrinsic factors. This study evaluated the rejection of PFAS of different carbon chain length (e.g., PFOA and PFBA) by two commercial RO and NF membranes under different operational conditions (e.g., applied pressure and initial PFAS concentration) and feed solution matrixes, such as pH (4-10), salinity (0- to 1000-mM NaCl), and organic matters (0-10mM). We further performed principal component analysis (PCA) to demonstrate the interrelationships of molecular weight (213-499 g·mol-1 ), membrane characteristics (RO or NF), feed water matrices, and operational conditions on PFAS rejection. Our results confirmed that size exclusion is a primary mechanism of PFAS rejection by RO and NF, as well as the fact that electrostatic interactions are important when PFAS molecules have sizes less than the NF membrane pores. PRACTITIONER POINTS: Two commercial RO and NF membranes were both evaluated to remove 10 different PFAS. High transmembrane pressures facilitated permeate recovery and PFAS rejection by RO. Electrostatic repulsion and pore size exclusion are dominant rejection mechanisms for PFAS removal. pH, ionic strength, and organic matters affected PFAS rejection. Mechanisms of PFAS rejection with RO/NF membranes were explained by PCA analysis.

Application of terahertz Time-Domain spectroscopy and chemometrics-based whale optimization algorithm in PDE5 inhibitor detection

Combating the illicit use of PDE5 inhibitor drugs is a focal point in forensic science research. In order to achieve rapid identification of such drugs, this study applies terahertz time-domain spectroscopy combined with chemometrics to establish a fast and accurate detection method for PDE5 inhibitors. The optimal detection method is determined by comparing the spectral performance of three optical parameters, namely absorption coefficient, refractive index, and dielectric constant. Linear discriminant models based on different spectral parameters, whale optimization algorithm optimized extreme learning machine models, and whale optimization algorithm optimized random forest models are established. The effectiveness and performance of principal component analysis and competitive adaptive reweighted sampling algorithm for spectral feature data selection are also investigated. The PDE5 inhibitor identification model based on the competitive adaptive reweighted sampling - whale optimization algorithm - random forest (CARS-WOA-RF) model achieves an accuracy of 98.61%, and the identification model for two concentrations of Sildenafil achieves 100% accuracy. The results demonstrate that terahertz time-domain spectroscopy combined with chemometrics can effectively detect various common types of PDE5 inhibitor drugs and different concentrations.

PM2.5 constituents associated with childhood obesity and larger BMI growth trajectory: A 14-year longitudinal study

BackgroundThe association of specific PM2.5 chemical constituents with childhood overweight or obesity (OWOB) remain unclear. Furthermore, the long-term impacts of PM2.5 exposure on the trajectory of children's body mass index (BMI) have not been explored. MethodsWe conducted a longitudinal study among 1,450,830 Chinese children aged 6–19 years from Beijing and Zhongshan in China during 2005–2018 to examine the associations of PM2.5 and its chemical constituents with incident OWOB risk. We extracted PM2.5 mass and five main component exposure from Tracking Air Pollution in China (TAP) dataset. Cox proportional hazards models were applied to quantify exposure–response associations. We further performed principal component analysis (PCA) to handle the multi-collinearity and used quantile g-computation (QGC) approach to analyze the impacts of exposure mixtures. Additionally, we selected 125,863 children with at least 8 physical examination measurements and combined group-based trajectory models (GBTM) with multinomial logistic regression models to explore the impacts of exposure to PM2.5 mass and five constituents on BMI and BMI Z-score trajectories during 6–19 years. ResultsWe observed each interquartile range increment in PM2.5 exposure was significantly associated with a 5.1 % increase in the risk of incident OWOB (95 % confidence Interval [CI]: 1.036–1.066). We also found black carbon, sulfate, organic matter, often linked to fossil combustion, had comparable or larger estimates of the effect (HR = 1.139–1.153) than PM2.5. Furthermore, Exposure to PM2.5 mass, sulfate, nitrate, ammonium, organic matter and black carbon was significantly associated with an increased odds of being in a larger BMI trajectory and being assigned to persistent OWOB trajectory. ConclusionsOur findings provide evidence that the constituents mainly from fossil fuel combustion may have a perceptible influence on increased OWOB risk associated with PM2.5 exposure in China. Moreover, long-term exposure to PM2.5 contributes to an increased odds of being in a lager BMI and a persistent OWOB trajectories.

Surface water mass dynamics at IODP Site U1313 through Principal Component Analysis: Evidence from coccolith assemblages in the ∼25–7 kyr interval

Coccolith assemblages from Integrated Ocean Drilling Program Site U1313 (North Atlantic; 41°0′N, 32°57.4′W) are analysed to assess temporal changes in surface water conditions from the Last Glacial Maximum (LGM) to the Middle Holocene (∼25–7 kyr). We perform Principal Component Analysis (PCA), involving both calcareous nannofossil assemblage data and other paleoenvironmental proxies. We reconstruct sea-surface dynamics using calcareous nannofossil assemblage variations and comparison with other previously published proxy data from Site U1313 including C37:4 alkenone, dolomite/calcite and quartz/calcite ratios, Uk’37 SST, as well as the sedimentation rate.We use variations in paleoproductivity proxies, cold and warm water group abundance, and the PCA results to infer North Atlantic Current (NAC) and North Atlantic Transitional Waters (NATW) migration through the studied interval. These proxies are also used to highlight variations in the Subtropical Gyre dynamics. Warming episodes are recorded during the LGM and Heinrich Stadial (HS) 1 as a result of northward expansion of the Subtropical Gyre. High coccolithophore productivity episodes characterize HS 1 and 2, and are concomitant with increasing terrigenous input. Both processes are considered a consequence of iceberg influence, which transport nutrients and ice-rafted debris to the area. A southward shift of the Subpolar Front and NAC favour the expansion of cold water from 25.3 ka to the end of the Younger Dryas, whereas the early Holocene is characterized by NATW influence resulting from northward migration of the NAC and Azores Current.

Abstract C041: Transcriptomic analysis of prostate cancer patients living with HIV

Abstract Background: Higher incidence and mortality rates of liver, lung, anal, and head and neck cancers are typically seen in patients living with human immunodeficiency virus (HIV). However, some previous studies have a decreased risk of prostate cancer in HIV+ patients, particularly in men from sexual minorities, with no clear biological or epidemiological explanation. Methods: To identify the specific transcriptomic changes and genetic pathways associated with HIV infection and prostate cancer emergence, we performed bulk RNA-Seq analysis on 18 prostate cancer samples (9 HIV+ and 9 HIV-). We first used kallisto to measure gene-level expression. We then performed principal component analysis (PCA) to determine if HIV status clearly clustered patients based on their gene expression levels. We subsequently used DESeq2 to perform differentially expressed transcriptomic analysis to identify genes that are associated with HIV and prostate cancer phenotypes. Samples were clustered based on expression levels of the top differentially expressed genes across the HIV+ and HIV- phenotypes. Gene set enrichment analysis was performed on the gene signatures from the two phenotypes. Using an existing single cell RNA-Seq prostate cancer dataset, we deconvoluted the 18 samples with CIBERSORT to approximate the cell type composition. Results: PCA highlighted no major separation between samples based on HIV status. We identified 450 significantly upregulated genes in HIV+ patients, including YAP1, MAT2A, KLF4, and ERF. We identified 409 upregulated genes in HIV- patients, including MICA, AQP2, and KRT6C. Upregulated genes found in HIV+ patients were significantly enriched in multiple immune response pathways, which is consistent with previous sequencing studies focused on HIV status. After cell type composition estimation, we found the samples to be similar in their cell type distributions - stromal and tumor cells were the largest populations, while immune cells were not well represented. Conclusions: We identified transcriptomic differences that need future studies that directly interrogate the biological pathways that affect the incidence of prostate cancer in patients living with HIV. Citation Format: Hanbing Song, Michelle Li, Jon Akutagawa, Franklin Huang. Transcriptomic analysis of prostate cancer patients living with HIV [abstract]. In: Proceedings of the 16th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2023 Sep 29-Oct 2;Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2023;32(12 Suppl):Abstract nr C041.

Genetic ancestry of human induced pluripotent stem cells banked for neurodegeneration research

AbstractBackgroundThe National Centralized Repository for Alzheimer’s Disease and Related Dementias (NCRAD) has established a centralized repository for human induced pluripotent stem cells (iPSCs) and fibroblasts in partnership with the National Institute on Aging (NIA). This is a critical resource for the research community, which can facilitate disease modeling, functional genetic studies, and drug development. However, the utility of this resource is still limited in terms of racial and ethnic diversity, which has the potential to bias neurodegenerative research and drug development unless steps are taken to create and share a more diverse array of cell lines.MethodAt NCRAD, banked iPSCs are subjected to quality control including short‐read whole genome sequencing (WGS). 78 samples have been submitted for sequencing; of these, data for 51 have been returned and analyzed, including multiple isogenic lines from three donors. WGS data was aligned, and quality control performed using Sentieon software, following GATK best practices. Data was combined with sequencing data from 2,548 1000 Genomes participants and pruned in PLINK. R package SNPRelate was used to generate a kinship matrix and perform principal component analysis (PCA). Clustering analysis was performed utilizing K‐means clustering to group samples into 5 clusters. PCs 1‐3 were graphed, color‐coded by cluster, and final ancestry calls reviewed for clustering with reference groups.ResultThe NCRAD 51 iPSC lines with analyzed WGS data were sourced from 28 neurodegenerative cases and 12 controls. In this data set, 83% of samples (89% of cases, 67% of controls) have European ancestry. The cohort also includes one case and one control line of East Asian ancestry, as well as 2 cases and 3 controls showing admixture of European/American ancestry. 17 individuals did not have reported race/ethnicity, while 2 controls showed genetic admixture though reported as White non‐Hispanic, and the remaining individuals’ genetic ancestry matched reported race/ethnicity.ConclusionCentralized banking of iPSCs from individuals with neurodegenerative diseases is creating a key resource to advance research; however, to date the diversity of these samples is limited. We urge researchers to develop and share more diverse cell lines, to promote diversity and equity in neurodegenerative research.

The Peripheral Blood Stem Cell Immunome Demonstrates Abnormal Immune Effector Cells Early in the Disease Course of Multiple Myeloma and Contributes to Inferior Outcomes and Secondary Myeloid Neoplasms

Background: Upfront autologous stem cell transplant (ASCT) is the standard-of-care for newly diagnosed multiple myeloma (MM) patients. However, relapse and the development of therapy-related myeloid neoplasms (t-MN) post ASCT results in considerable morbidity and mortality. We hypothesized that enrichment of abnormal myeloid progenitors and immune effector cells (IEC) in peripheral blood stem cells (PBSCs) of patients with MM is associated with a higher risk of relapse and development of t-MN. Methods: After institutional review board approval, we screened patients with active MM that were evaluated at Mayo Clinic, Rochester, MN between 01/01/2003 and 12/31/2020. Patients that underwent an ASCT and had cryopreserved PBSC product available for research were included. Using mass cytometry, we performed a comprehensive immunophenotyping of mobilized cryopreserved PBSCs from 54 MM patients undergoing 1 st ASCT, using validated antibody panel of 37 lymphoid- and myeloid-based markers each. Flow cytometry standard files were normalized and concatenated using the Fluidigm acquisition software. Flow cytometry standard files were uploaded to the OMIQ software from Dotmatics (www.omiq.ai, www.dotmatics.com), where transformation and cleaning (doublets, debris) were performed. To correct for between-sample technical variations (“batch effects”), we used fdaNorm within the omiq.ai platform. Clustering and visualization of normalized and concatenaded flowcytometry files were performed within the omiq.ai platform using PhenoGraph and UMAP, respectively. We performed principal component analysis (PCA) to identify major immunomic drivers of variability across patients. We explored the associations between immune subsets and the dichotomized clinical outcomes of interest: progression-free (PFS), myeloid neoplasm-free (MNFS) and overall survival (OS) from ASCT. A 2-sided false discovery rate adjusted P-value of &amp;lt;0.05 was considered significant when multiple comparisons were performed; otherwise, a P-value of &amp;lt;0.05 was considered significant. Results: The most abundant cell populations were T-cells (49% of the CD45 +) and myeloid cells (43% of the CD45 + cells). Clustering on the CD45 + cells revealed 30 unique lymphoid clusters and 27 unique myeloid clusters. Subclustering on NK-cells identified an additional 16 NK-cell clusters. On PCA, principal components-1 (PC-1) and 2 (PC-2) explained 15.2% and 12.5% of the variability in the data, respectively. A high PC-2 score was associated with inferior median PFS (20 vs. 52 months; P=0.005, Figure 1A), MNFS (52 vs. 85 months, P=0.03, Figure 1B) and OS (64 vs. 106 months, P=0.01) from ASCT. Immune subsets enriched in the high PC-2 cohort included the ‘NKT-like’ (CD56 +) T-cell subset which expressed the inhibitory CD159a receptor and TIGIT; a terminally differentiated (CD27/CD28 -, CD57/KLRG1 +) and exhausted (TIGIT/PD-1 +) T cell subset, 2 exhausted (TIGIT/PD-1 +) T cell subsets; an immunosenescent (CD27/CD28 -, CD57/KLRG1 +) T cell subset as well as a terminally exhausted (C27/C28 -, KLRG1/PD-1/TIGIT +) TCRgamma-delta subset. Immune subsets with high loadings within PC-1 (associated with improved clinical outcomes) consisted exclusively of CD4 + cells with early memory phenotypes (CD27/CD28/CD127 +). Abnormal expression of CD7 and HLA-DR expression on myeloid progenitor cell subsets was associated with an inferior PFS, MNFS, and OS. A NK-cell subcluster, characterized by an immature (CD56 high), inhibitory (CD159a +, NKG2A) phenotype, was enriched in patients developing t-MN (0.33% vs. 0.17%, P=0.006). Conclusion: We identify abnormal myeloid and immune effector cells in the PBSCs of patients with MM, even prior to ASCT. Patients with a PBSC product that is enriched in immunosuppressive T and myeloid cell subsets, and with a lower proportion of antigen presenting cells, progenitor populations, and anti-tumor macrophages have a shorter PFS and MNFS. This suggests that the predisposition to t-MN and inferior outcomes for MM are possibly determined early in the disease course. These findings need external validation and have the potential to guide sequencing of future treatment modalities.

Whole mitochondrial genome analysis in highland Tibetans: further matrilineal genetic structure exploration.

Introduction: The Qinghai-Tibet Plateau is one of the last terrestrial environments conquered by modern humans. Tibetans are among the few high-altitude settlers in the world, and understanding the genetic profile of Tibetans plays a pivotal role in studies of anthropology, genetics, and archaeology. Methods: In this study, we investigated the maternal genetic landscape of Tibetans based on the whole mitochondrial genome collected from 145 unrelated native Lhasa Tibetans. Molecular diversity indices, haplotype diversity (HD), Tajima's D and Fu's Fs were calculated and the Bayesian Skyline Plot was obtained to determining the genetic profile and population fluctuation of Lhasa Tibetans. To further explore the genetic structure of Lhasa Tibetans, we collected 107 East Asian reference populations to perform principal component analysis (PCA), multidimensional scaling (MDS), calculated Fst values and constructed phylogenetic tree. Results: The maternal genetic landscape of Tibetans showed obvious East Asian characteristics, M9a (28.28%), R (11.03%), F1 (12.41%), D4 (9.66%), N (6.21%), and M62 (4.14%) were the dominant haplogroups. The results of PCA, MDS, Fst and phylogenetic tree were consistent: Lhasa Tibetans clustered with other highland Tibeto-Burman speakers, there was obvious genetic homogeneity of Tibetans in Xizang, and genetic similarity between Tibetans and northern Han people and geographically adjacent populations was found. In addition, specific maternal lineages of Tibetans also be determined in this study. Discussion: In general, this study further shed light on long-time matrilineal continuity on the Tibetan Plateau and the genetic connection between Tibetans and millet famers in the Yellow River Basin, and further revealed that multiple waves of population interaction and admixture during different historical periods between lowland and highland populations shaped the maternal genetic profile of Tibetans.

Analysis of the Differences in Volatile Organic Compounds in Different Rice Varieties Based on GC-IMS Technology Combined with Multivariate Statistical Modelling.

In order to investigate the flavour characteristics of aromatic, glutinous, and nonaromatic rice, gas chromatography-ion mobility spectrometry (GC-IMS) was used to analyse the differences in volatile organic compounds (VOCs) amongst different rice varieties. The results showed that 103 signal peaks were detected in these rice varieties, and 91 volatile flavour substances were identified. Amongst them, 28 aldehydes (28.89~31.17%), 24 alcohols (34.85~40.52%), 14 ketones (12.26~14.74%), 12 esters (2.30~4.15%), 5 acids (7.80~10.85%), 3 furans (0.30~0.68%), 3 terpenes (0.34~0.64%), and 2 species of ethers (0.80~1.78%) were detected. SIMCA14.1 was used to perform principal component analysis (PCA) and orthogonal partial least squares discriminant analysis, and some potential character markers (VIP > 1) were further screened out of the 91 flavour substances identified based on the variable important projections, including ethanol, 1-hexanol, hexanal, heptanal, nonanal, (E)-2-heptenal, octanal, trans-2-octenal, pentanal, acetone, 6-methyl-5-hepten-2-one, ethyl acetate, propyl acetate, acetic acid, and dimethyl sulphide. Based on the established fingerprint information, combined with principal component analysis and orthogonal partial least squares discriminant analysis, different rice varieties were also effectively classified, and the results of this study provide data references for the improvement in aromatic rice varieties.

A proof-of-principle study: The potential application of MiniHap biomarkers in ancestry inference based on the QNome nanopore sequencing

Haplotyped SNPs convey forensic-related information, and microhaplotypes (MHs), as the most representative of this kind of marker, have proved the potential value for human forensics. In recent years, nanopore sequencing technology has developed rapidly, with its outstanding ability to sequence long continuous DNA fragments and obtain phase information, making the detection of longer haplotype marker possible. In this proof-of-principle study, we proposed a new type of forensic marker, MiniHap, based on five or more SNPs within a molecular distance less than 800 bp, and investigated the haplotype data of 56 selected MiniHaps in five Chinese populations using the QNome nanopore sequencing. The sequencing performance, allele (haplotype) frequencies, forensic parameters, effective number of alleles (Ae), and informativeness (In) were subsequently calculated. In addition, we performed principal component analysis (PCA), phylogenetic tree, and structure analysis to investigate the population genetic relationships and ancestry components among the five investigated populations and 26 worldwide populations. MiniHap-04 exhibited remarkable forensic efficacy, with 148 haplotypes reported and the Ae was 66.9268. In addition, the power of discrimination (PD) was 0.9934, the probability of exclusion (PE) was 0.9898, and the In value was 0.7893. Of the 56 loci, 85.71% had PD values above 0.85, 66.07% had PE values above 0.54, 67.86% had Ae values over 7.0%, and 55.36% were with In values above 0.2 across all samples, indicating that most of the MiniHaps are suitable for individual identification, paternity testing, mixture deconvolution, and ancestry inference. Moreover, the results of PCA, phylogenetic tree and structure analysis demonstrated that this MiniHap panel had the competency in continental population ancestry inference, but the differentiation within intracontinental/linguistically restricted subpopulations was not ideal. Such findings suggested that the QNome device for MiniHap detection was feasible and this novel marker has the potential in ancestry inference. Yet, the establishment of a more comprehensive database with sufficient reference population data remains necessary to screen more suitable MiniHaps.

YARARA V2: Reaching sub-m s−1 precision over a decade using PCA on line-by-line radial velocities

Context. The detection of Earth-like planets with the radial velocity (RV) method is extremely challenging today due to the presence of non-Doppler signatures such as stellar activity and instrumental signals that mimic and hide the signals of exoplanets. In a previous paper, we presented the YARARA pipeline, which implements corrections for telluric absorption, stellar activity, and instrumental systematics at the spectral level, and then it extracts line-by-line (LBL) RVs with a significantly better precision than standard pipelines. Aims. In this paper, we demonstrate that further gains in RV precision can be achieved by performing principal component analysis (PCA) decomposition on the LBL RVs. Methods. The mean-insensitive nature of PCA means that it is unaffected by true Doppler shifts, and thus can be used to isolate and correct nuisance signals other than planets. Results. We analysed the data of 20 intensively observed HARPS targets by applying our PCA approach on the LBL RVs obtained by YARARA. The first principal components show similarities across most of the stars and correspond to newly identified instrumental systematics for which we can now correct. For several targets, this results in an unprecedented RV root-mean-square of around 90 cm s−1 over the full lifetime of HARPS. We used the corrected RVs to confirm a previously published 120-day signal around 61 Vir, and to detect a super-Earth candidate (K ~ 60 ± 6 cm s−1, m sin i = 6.6 ± 0.7 M⊕) around the G6V star HD 20794, which spends part of its 600-day orbit within the habitable zone of the host star. Conclusions. This study highlights the potential of LBL PCA to identify and correct hitherto unknown, long-term instrumental effects and thereby extend the sensitivity of existing and future instruments towards the Earth analogue regime.

The Development of Green Ports in Emerging Nations: A Case Study of Vietnam

The development of green ports is still limited, especially in developing nations, despite the fact that they are viewed as a significant answer to the problems of environmental pollution and climate change. The purpose of this study is to investigate the factors that promote and hinder the development of green ports in developing countries. Using 248 managers from 12 Vietnamese container ports as a sample, this study is one of the pioneers in using a quantitative methodology to investigate the factors affecting the development of green ports. We used SPSS 22.0 and AMOS 22.0 to perform principal component analysis (PCA), confirmatory factor analysis (CFA), and structural equation modeling (SEM). Our results indicated that cooperation of involved parties and foreign capital has the most important role in green port development in developing countries, followed by environmental regulation. Our findings also showed that lack of initial capital and lack of technological advancement negatively affects the development of green ports in these countries. The results and implications of the study will be discussed in more detail.

Identification and assessment of differentially expressed necroptosis long non-coding RNAs associated with periodontitis in human

BackgroundPeriodontitis is the most common oral disease and is closely related to immune infiltration in the periodontal microenvironment and its poor prognosis is related to the complex immune response. The progression of periodontitis is closely related to necroptosis, but there is still no systematic study of long non-coding RNA (lncRNA) associated with necroptosis for diagnosis and treatment of periodontitis.Material and methodsTranscriptome data and clinical data of periodontitis and healthy populations were obtained from the Gene Expression Omnibus (GEO) database, and necroptosis-related genes were obtained from previously published literature. FactoMineR package in R was used to perform principal component analysis (PCA) for obtaining the necroptosis-related lncRNAs. The core necroptosis-related lncRNAs were screened by the Linear Models for Microarray Data (limma) package in R, PCA principal component analysis and lasso algorithm. These lncRNAs were then used to construct a classifier for periodontitis with logistic regression. The receiver operating characteristic (ROC) curve was used to evaluate the sensitivity and specificity of the model. The CIBERSORT method and ssGSEA algorithm were used to estimate the immune infiltration and immune pathway activation of periodontitis. Spearman’s correlation analysis was used to further verify the correlation between core genes and periodontitis immune microenvironment. The expression level of core genes in human periodontal ligament cells (hPDLCs) was detected by RT-qPCR.ResultsA total of 10 core necroptosis-related lncRNAs (10-lncRNAs) were identified, including EPB41L4A-AS1, FAM30A, LINC01004, MALAT1, MIAT, OSER1-DT, PCOLCE-AS1, RNF144A-AS1, CARMN, and LINC00582. The classifier for periodontitis was successfully constructed. The Area Under the Curve (AUC) was 0.952, which suggested that the model had good predictive performance. The correlation analysis of 10-lncRNAs and periodontitis immune microenvironment showed that 10-lncRNAs had an impact on the immune infiltration of periodontitis. Notably, the RT-qPCR results showed that the expression level of the 10-lncRNAs obtained was consistent with the chip analysis results.ConclusionsThe 10-lncRNAs identified from the GEO dataset had a significant impact on the immune infiltration of periodontitis and the classifier based on 10-lncRNAs had good detection efficiency for periodontitis, which provided a new target for diagnosis and treatment of periodontitis.

Identification of Four Similar Cell Culture Media According to their Glucose, Glutamine, and Pyruvate Content by Handheld Raman Spectroscopy.

This study describes the first efforts to build a spectral library to identify four cell culture media in powder form with spectra obtained with a handheld Raman spectrometer. These complex mixtures contain over 30 components and are among the most widely used cell culture media. A total of 32 spectra were collected for the four Dulbecco's Modified Eagle Medium cell culture media and pure materials (glucose and L-glutamine) in powder form. The spectra were preprocessed using standard normal variate with second derivative, and the barcode method before performing principal component analysis (PCA). The PCA model differentiated the pure glucose and the cell culture media according to the glucose concentration along the first principal component. The second principal component differentiated the three cell culture media with high glucose content according to the pyruvate concentration. The correlation coefficient showed that powdered cell culture media with high glucose concentration have a higher correlation with pure glucose, when compared with the cell culture media with low glucose. The Raman spectra made it possible to differentiate the four DMEM in the cell culture media from the majority of the external samples used in the method evaluation. However, sample heterogeneity affected the predictions. Additional studies are needed to improve the method's ability to differentiate the DMEM with high glucose.