Set Of Markers Research Articles

SARS-CoV-2 infects epithelial cells of the blood-cerebrospinal fluid barrier rather than endothelial cells or pericytes of the blood-brain barrier

BackgroundAs a consequence of SARS-CoV-2 infection various neurocognitive and neuropsychiatric symptoms can appear, which may persist for several months post infection. However, cell type-specific routes of brain infection and underlying mechanisms resulting in neuroglial dysfunction are not well understood.MethodsHere, we investigated the susceptibility of cells constituting the blood-brain barrier (BBB) and the blood-cerebrospinal fluid barrier (BCSFB) of the choroid plexus (ChP) to SARS-CoV-2 infection using human induced pluripotent stem cell (hiPSC)-derived cellular models and a ChP papilloma-derived epithelial cell line as well as ChP tissue from COVID-19 patients, respectively.ResultsWe noted a differential infectibility of hiPSC-derived brain microvascular endothelial cells (BMECs) depending on the differentiation method. Extended endothelial culture method (EECM)-BMECs characterized by a complete set of endothelial markers, good barrier properties and a mature immune phenotype were refractory to SARS-CoV-2 infection and did not exhibit an activated phenotype after prolonged SARS-CoV-2 inoculation. In contrast, defined medium method (DMM)-BMECs, characterized by a mixed endothelial and epithelial phenotype and excellent barrier properties were productively infected by SARS-CoV-2 in an ACE2-dependent manner. hiPSC-derived brain pericyte-like cells (BPLCs) lacking ACE2 expression were not susceptible to SARS-CoV-2 infection. Furthermore, the human choroid plexus papilloma-derived epithelial cell line HIBCPP, modeling the BCSFB was productively infected by SARS-CoV-2 preferentially from the basolateral side, facing the blood compartment. Assessment of ChP tissue from COVID-19 patients by RNA in situ hybridization revealed SARS-CoV-2 transcripts in ChP epithelial and ChP stromal cells.ConclusionsOur study shows that the BCSFB of the ChP rather than the BBB is susceptible to direct SARS-CoV-2 infection. Thus, neuropsychiatric symptoms because of COVID-19 may rather be associated with dysfunction of the BCSFB than the BBB. Future studies should consider a role of the ChP in underlying neuropsychiatric symptoms following SARS-CoV-2 infection.

COVID-19 Recovery: Consistent Absence of Cerebrospinal Fluid Biomarker Abnormalities in Patients With Neurocognitive Post-COVID Complications.

To investigate evidence of residual viral infection, intrathecal immune activation, central nervous system (CNS) injury, and humoral responses in cerebrospinal fluid (CSF) and plasma in patients recovering from coronavirus disease 2019 (COVID-19), with or without neurocognitive post-COVID condition (PCC). Thirty-one participants (25 with neurocognitive PCC) underwent clinical examination, lumbar puncture, and venipuncture ≥3 months after COVID-19 symptom onset. Healthy volunteers were included. CSF and plasma severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) nucleocapsid and spike antigen (N-Ag, S-Ag), and CSF biomarkers of immune activation and neuronal injury were analyzed. SARS-CoV-2 N-Ag or S-Ag were undetectable in all samples and no participant had pleocytosis. We detected no significant differences in CSF and plasma cytokine concentrations, albumin ratio, IgG index, neopterin, β2M, or in CSF biomarkers of neuronal injury and astrocytic damage. Furthermore, principal component analysis (PCA1) analysis did not indicate any significant differences between the study groups in the marker sets cytokines, neuronal markers, or anti-cytokine autoantibodies. We found no evidence of ongoing viral replication, immune activation, or CNS injury in plasma or CSF in patients with neurocognitive PCC compared with COVID-19 controls or healthy volunteers, suggesting that neurocognitive PCC is a consequence of events suffered during acute COVID-19 rather than persistent viral CNS infection or residual CNS inflammation.

Marker Set Configuration and Rigid Body Attitude Determination.

Image-based motion-analysis systems typically place markers on the bodies of interest. The error in determining segment attitude from these markers is a function the marker position errors, the number of markers, and the spatial distribution of the markers. The spatial distribution includes two factors: the mean square distance of these markers to their geometric center, and the degree of anisotropy in the marker distribution. The purposes of this study were to: (1) present a metric which quantifies the marker spatial distribution (anisotropic to isotropic) and (2) examine the influence of marker distribution on the accuracy of rigid body attitude determination. To test the influence of the marker distribution on body attitude determination 1000 criterion attitudes were determined. These attitudes then had to be estimated for two marker sets for which the marker distribution metric, noise levels, and root-mean-square distance of the markers were systematically varied. Anisotropic marker distributions were shown to negatively affect the accuracy of attitude determination. The influence of anisotropic marker distributions on attitude accuracy could be blunted by increasing the number of markers, increasing the root-mean-square distance of markers from their geometric center, and reducing noise levels. These results have implications for the measurement of the attitudes of body segments. For example, the ability to have a large spatial distribution of markers and a large number of markers to maximize the measurement accuracy of segment attitude is different for a small segment such as the fifth metacarpal compared with the thigh.

The first two chromosome-scale genome assemblies of American hazelnut enable comparative genomic analysis of the genus Corylus.

The native, perennial shrub American hazelnut (Corylus americana) is cultivated in the Midwestern United States for its significant ecological benefits, as well as its high-value nut crop. Implementation of modern breeding methods and quantitative genetic analyses of C. americana requires high-quality reference genomes, a resource that is currently lacking. We therefore developed the first chromosome-scale assemblies for this species using the accessions 'Rush' and 'Winkler'. Genomes were assembled using HiFi PacBio reads and Arima Hi-C data, and Oxford Nanopore reads and a high-density genetic map were used to perform error correction. N50 scores are 31.9 Mb and 35.3 Mb, with 90.2% and 97.1% of the total genome assembled into the 11 pseudomolecules, for 'Rush' and 'Winkler', respectively. Gene prediction was performed using custom RNAseq libraries and protein homology data. 'Rush' has a BUSCO score of 99.0 for its assembly and 99.0 for its annotation, while 'Winkler' had corresponding scores of 96.9 and 96.5, indicating high-quality assemblies. These two independent assemblies enable unbiased assessment of structural variation within C. americana, as well as patterns of syntenic relationships across the Corylus genus. Furthermore, we identified high-density SNP marker sets from genotyping-by-sequencing data using 1343 C. americana, C. avellana and C. americana × C. avellana hybrids, in order to assess population structure in natural and breeding populations. Finally, the transcriptomes of these assemblies, as well as several other recently published Corylus genomes, were utilized to perform phylogenetic analysis of sporophytic self-incompatibility (SSI) in hazelnut, providing evidence of unique molecular pathways governing self-incompatibility in Corylus.

The Great Boundary Fault in the Indo-Gangetic foreland basin: A geomorphological perspective

Identifying active faults in alluvial plains with varying sedimentation rates, erosion, and extensive anthropogenic modifications is challenging. In tectonically active foreland basins, tectonics outweighs other controlling factors and produces a set of key fluvial geomorphic markers that are critical for precisely locating active faults. This study employs several key tectonic fluvial morphometric parameters, seismically induced soft-sediment deformation structures (SSDS) as seismites, and the displaced rocks of the Siwalik mountains to mark the surface positions of a buried basement fault system, namely the Great Boundary Fault (GBF) beneath the alluvial cover of the Indo-Gangetic foreland basin.The Great Boundary Fault (GBF) is a NE-SW trending, oblique-slip, branching reverse basement fault running from peninsular India to the Sub-Himalayas. About 400 km length of its NE segment is buried under thick sediments of the Indo-Gangetic foreland basin. This fault's surfaceward propagation deforms the overlying sediments via a network of subsidiary faults, resulting in 20 km wide broad fault zones. The extreme flatness of the Ganga plain conceals the primary faulting evidence but tectonic fluvial geomorphic signatures, the SSDS, and the laterally displaced Siwalik rocks, including south-verging thrusts at the frontal Himalayas, provide collective evidence of neotectonic activity along both fault branches. The primary objectives of this study are to characterise the GBF's buried segment in the Ganga plain through the geomorphic signatures and SSDS, to elucidate its extension up to the Main Boundary Thrust (MBT), and to understand the surfaceward propagation mechanism of its reverse faults through the alluvial-fills.Tectonic geomorphic parameters such as sinuosity spectra and knickpoints were analysed using ArcGIS and Matlab. Meander scar mapping was carried out using Google Earth Pro and Landsat imagery within ArcGIS to characterise the pattern of avulsions and adjustments of the floodplain linked to the tilting directions of the fault blocks.Fourteen prominent knickpoints of 4 to ≥6 m height were derived from the river profiles across the fault zone. Rivers on the up-thrown block possessed low sinuosity (as low as 1.1), whilst those on the down-thrown block had higher sinuosities (up to 3.1), indicating active uplift and rapid subsidence. The deflection of multiple river courses (Ganga, Ramganga, Burhiganga, Sot, Pangaili, and Deoha) and the presence of asymmetrical meander-belts demonstrate an eastward tilting of the tectonic block. The occurrence of liquefaction-induced SSDS close to the GBF corroborates its recent seismogenic nature. Field observation suggests these SSDS are likely related to the Usawan paleo-earthquake (estimated magnitude of 6.5–7.2), epicentered near the GBF. Based on the geometry and the venting characteristics, a new type of SSDS has been recognized, and the term “liquefaction vents” has been proposed for it. Their association with the other types of SSDS that are characteristically formed by >6.5 magnitude earthquakes suggests it is the minimum magnitude of earthquake required for the formation of liquefaction vents. The regional seismic record suggests a significant number of both large and moderate earthquakes around this fault.Finally, we synthesise the results to propose a growth model for the propagation of reverse faulting from the underlying basement bedrock through to the alluvial sediments and the surface of the Ganga plain.

Identifying Genetic Signatures from Single-Cell RNA Sequencing Data by Matrix Imputation and Reduced Set Gene Clustering

In this current era, the identification of both known and novel cell types, the representation of cells, predicting cell fates, classifying various tumor types, and studying heterogeneity in various cells are the key areas of interest in the analysis of single-cell RNA sequencing (scRNA-seq) data. Due to the nature of the data, cluster identification in single-cell sequencing data with high dimensions presents several difficulties. In this paper, we introduce a new framework that combines various strategies such as imputed matrix, minimum redundancy maximum relevance (MRMR) feature selection, and shrinkage clustering to discover gene signatures from scRNA-seq data. Firstly, we conducted the pre-filtering of the “drop-out” value in the data focusing solely on imputing the identified “drop-out” values. Next, we applied the MRMR feature selection method to the imputed data and obtained the top 100 features based on the MRMR feature selection optimization scores for further downstream analysis. Thereafter, we employed shrinkage clustering on the selected feature matrix to identify the cell clusters using a global optimization approach. Finally, we applied the Limma-Voom R tool employing voom normalization and an empirical Bayes test to detect differentially expressed features with a false discovery rate (FDR) < 0.001. In addition, we performed the KEGG pathway and gene ontology enrichment analysis of the identified biomarkers using David 6.8 software. Furthermore, we conducted miRNA target detection for the top gene markers and performed miRNA target gene interaction network analysis using the Cytoscape online tool. Subsequently, we compared our detected 100 markers with our previously detected top 100 cluster-specified markers ranked by FDR of the latest published article and discovered three common markers; namely, Cyp2b10, Mt1, Alpi, along with 97 novel markers. In addition, the Gene Set Enrichment Analysis (GSEA) of both marker sets also yields similar outcomes. Apart from this, we performed another comparative study with another published method, demonstrating that our model detects more significant markers than that model. To assess the efficiency of our framework, we apply it to another dataset and identify 20 strongly significant up-regulated markers. Additionally, we perform a comparative study of different imputation methods and include an ablation study to prove that every key phase of our framework is essential and strongly recommended. In summary, our proposed integrated framework efficiently discovers differentially expressed stronger gene signatures as well as up-regulated markers in single-cell RNA sequencing data.

Single-cell RNA sequencing distinctly characterizes the wide heterogeneity in pediatric mixed phenotype acute leukemia

BackgroundMixed phenotype acute leukemia (MPAL), a rare subgroup of leukemia characterized by blast cells with myeloid and lymphoid lineage features, is difficult to diagnose and treat. A better characterization of MPAL is essential to understand the subtype heterogeneity and how it compares with acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL). Therefore, we performed single-cell RNA sequencing (scRNAseq) on pediatric MPAL bone marrow (BM) samples to develop a granular map of the MPAL blasts and microenvironment landscape.MethodsWe analyzed over 40,000 cells from nine pediatric MPAL BM samples to generate a single-cell transcriptomic landscape of B/myeloid (B/My) and T/myeloid (T/My) MPAL. Cells were clustered using unsupervised single-cell methods, and malignant blast and immune clusters were annotated. Differential expression analysis was performed to identify B/My and T/My MPAL blast-specific signatures by comparing transcriptome profiles of MPAL with normal BM, AML, and ALL. Gene set enrichment analysis (GSEA) was performed, and significantly enriched pathways were compared in MPAL subtypes.ResultsB/My and T/My MPAL blasts displayed distinct blast signatures. Transcriptomic analysis revealed that B/My MPAL profile overlaps with B-ALL and AML samples. Similarly, T/My MPAL exhibited overlap with T-ALL and AML samples. Genes overexpressed in both MPAL subtypes’ blast cells compared to AML, ALL, and healthy BM included MAP2K2 and CD81. Subtype-specific genes included HBEGF for B/My and PTEN for T/My. These marker sets segregated bulk RNA-seq AML, ALL, and MPAL samples based on expression profiles. Analysis comparing T/My MPAL to ETP, near-ETP, and non-ETP T-ALL, showed that T/My MPAL had greater overlap with ETP-ALL cases. Comparisons among MPAL subtypes between adult and pediatric samples showed analogous transcriptomic landscapes of corresponding subtypes. Transcriptomic differences were observed in the MPAL samples based on response to induction chemotherapy, including selective upregulation of the IL-16 pathway in relapsed samples.ConclusionsWe have for the first time described the single-cell transcriptomic landscape of pediatric MPAL and demonstrated that B/My and T/My MPAL have distinct scRNAseq profiles from each other, AML, and ALL. Differences in transcriptomic profiles were seen based on response to therapy, but larger studies will be needed to validate these findings.

QTL mapping of oleic acid content in modern VNIIMK sunflower (Helianthus annuus L.) lines by using GBS-based SNP map.

Oleic acid is a monounsaturated fatty acid increasing oil oxidative stability. High content of oleic acid is thus a valuable trait in oilseed crops. Sunflower (Helianthus annuus L.) normally accumulates linoleic acid as a major fatty acid, but a mutant expressing a high oleic phenotype form was previously obtained by chemical mutagenesis and mapped on the sunflower genome. Several studies suggest the presence of additional genes involved in the control of the high content of oleic acid, with their expression possibly depending on the genetic background. To test this hypothesis, we performed a QTL mapping of the high oleic acid trait within two independent F2 crosses involving lines with contrasting oleic acid content from the Pustovoit All-Russia Research Institute of Oil Crops (VNIIMK) collection. We applied genotyping-by-sequencing (GBS) to construct single nucleotide polymorphism-based genetic maps and performed QTL mapping using quantitative and qualitative encoding for oleic acid content. Our results support the finding that the oleic acid content in the assessed crosses is controlled by one major effect locus. However, different dominant/recessive effects of the major locus were reported for both crosses. Additionally, a possible translocation between chromosome 7 and 14 was reported in one assessed cross. We defined a set of single nucleotide polymorphism markers for each cross which could be used for marker-assisted selection.

Unraveling Triplet Formation Mechanisms in Acenothiophene Chromophores.

The evolution of molecular platforms for singlet fission (SF) chromophores has fueled the quest for new compounds capable of generating triplets quantitatively at fast time scales. As the exploration of molecular motifs for SF has diversified, a key challenge has emerged in identifying when the criteria for SF have been satisfied. Here, we show how covalently bound molecular dimers uniquely provide a set of characteristic optical markers that can be used to distinguish triplet pair formation from processes that generate an individual triplet. These markers are contained within (i) triplet charge-transfer excited state absorption features, (ii) kinetic signatures of triplet-triplet annihilation processes, and (iii) the modulation of triplet formation rates using bridging moieties between chromophores. Our assignments are verified by time-resolved electron paramagnetic resonance (EPR) measurements, which directly identify triplet pairs by their electron spin and polarization patterns. We apply these diagnostic criteria to dimers of acenothiophene derivatives in solution that were recently reported to undergo efficient intermolecular SF in condensed media. While the electronic structure of these heteroatom-containing chromophores can be broadly tuned, the effect of their enhanced spin-orbit coupling and low-energy nonbonding orbitals on their SF dynamics has not been fully determined. We find that SF is fast and efficient in tetracenothiophene but that anthradithiophene exhibits fast intersystem crossing due to modifications of the singlet and triplet excited state energies upon functionalization of the heterocycle. We conclude that it is not sufficient to assign SF based on comparisons of the triplet formation kinetics between monomer and multichromophore systems.

Multivariate piecewise linear regression model to predict radiosensitivity using the association with the genome-wide copy number variation.

The search for biomarkers to predict radiosensitivity is important not only to individualize radiotherapy of cancer patients but also to forecast radiation exposure risks. The aim of this study was to devise a machine-learning method to stratify radiosensitivity and to investigate its association with genome-wide copy number variations (CNVs) as markers of sensitivity to ionizing radiation. We used the Affymetrix CytoScan HD microarrays to survey common CNVs in 129 fibroblast cell strains. Radiosensitivity was measured by the surviving fraction at 2 Gy (SF2). We applied a dynamic programming (DP) algorithm to create a piecewise (segmented) multivariate linear regression model predicting SF2 and to identify SF2 segment-related distinctive CNVs. SF2 ranged between 0.1384 and 0.4860 (mean=0.3273 The DP algorithm provided optimal segmentation by defining batches of radio-sensitive (RS), normally-sensitive (NS), and radio-resistant (RR) responders. The weighted mean relative errors (MRE) decreased with increasing the segments' number. The borders of the utmost segments have stabilized after partitioning SF2 into 5 subranges. The 5-segment model associated C-3SFBP marker with the most-RS and C-7IUVU marker with the most-RR cell strains. Both markers were mapped to gene regions (MCC and SLC1A6, respectively). In addition, C-3SFBP marker is also located in enhancer and multiple binding motifs. Moreover, for most CNVs significantly correlated with SF2, the radiosensitivity increased with the copy-number decrease.In conclusion, the DP-based piecewise multivariate linear regression method helps narrow the set of CNV markers from the whole radiosensitivity range to the smaller intervals of interest. Notably, SF2 partitioning not only improves the SF2 estimation but also provides distinctive markers. Ultimately, segment-related markers can be used, potentially with tissues' specific factors or other clinical data, to identify radiotherapy patients who are most RS and require reduced doses to avoid complications and the most RR eligible for dose escalation to improve outcomes.

Development of polymorphic microsatellite markers for Fagus pashanica (Fagaceae) using next-generation sequencing.

Fagus pashanica is an endangered and endemic tree species in China. To understand its genetic diversity and structure for effective conservation, we used next-generation sequencing data to develop a set of microsatellite markers. Twenty-three of the 68 designed loci were successfully amplified. Fifteen polymorphic loci with clear peaks were selected for further analyses in three F. pashanica populations sampled from Nanjiang, Wangcang and Pingwu counties in Sichuan Province, China. The number of alleles per locus ranged from two to 11. The levels of observed and expected heterozygosity ranged from 0.033-0.852 and 0.033-0.787, respectively. All 23 loci were also successfully amplified in F. longipetiolata and F. lucida, and 19 were successfully amplified in F. engleriana. These microsatellite markers will be useful for population genetic studies of F. pashanica and other Fagus species.

A large-scale targeted proteomics of serum and tissue shows the utility of classifying high grade and low grade meningioma tumors

BackgroundMeningiomas are the most prevalent primary brain tumors. Due to their increasing burden on healthcare, meningiomas have become a pivot of translational research globally. Despite many studies in the field of discovery proteomics, the identification of grade-specific markers for meningioma is still a paradox and requires thorough investigation. The potential of the reported markers in different studies needs further verification in large and independent sample cohorts to identify the best set of markers with a better clinical perspective.MethodsA total of 53 fresh frozen tumor tissue and 51 serum samples were acquired from meningioma patients respectively along with healthy controls, to validate the prospect of reported differentially expressed proteins and claimed markers of Meningioma mined from numerous manuscripts and knowledgebases. A small subset of Glioma/Glioblastoma samples were also included to investigate inter-tumor segregation. Furthermore, a simple Machine Learning (ML) based analysis was performed to evaluate the classification accuracy of the list of proteins.ResultsA list of 15 proteins from tissue and 12 proteins from serum were found to be the best segregator using a feature selection-based machine learning strategy with an accuracy of around 80% in predicting low grade (WHO grade I) and high grade (WHO grade II and WHO grade III) meningiomas. In addition, the discriminant analysis could also unveil the complexity of meningioma grading from a segregation pattern, which leads to the understanding of transition phases between the grades.ConclusionsThe identified list of validated markers could play an instrumental role in the classification of meningioma as well as provide novel clinical perspectives in regard to prognosis and therapeutic targets.

Machine learning prediction models for different stages of non-small cell lung cancer based on tongue and tumor marker: a pilot study

ObjectiveTo analyze the tongue feature of NSCLC at different stages, as well as the correlation between tongue feature and tumor marker, and investigate the feasibility of establishing prediction models for NSCLC at different stages based on tongue feature and tumor marker.MethodsTongue images were collected from non-advanced NSCLC patients (n = 109) and advanced NSCLC patients (n = 110), analyzed the tongue images to obtain tongue feature, and analyzed the correlation between tongue feature and tumor marker in different stages of NSCLC. On this basis, six classifiers, decision tree, logistic regression, SVM, random forest, naive bayes, and neural network, were used to establish prediction models for different stages of NSCLC based on tongue feature and tumor marker.ResultsThere were statistically significant differences in tongue feature between the non-advanced and advanced NSCLC groups. In the advanced NSCLC group, the number of indexes with statistically significant correlations between tongue feature and tumor marker was significantly higher than in the non-advanced NSCLC group, and the correlations were stronger. Support Vector Machine (SVM), decision tree, and logistic regression among the machine learning methods performed poorly in models with different stages of NSCLC. Neural network, random forest and naive bayes had better classification efficiency for the data set of tongue feature and tumor marker and baseline. The models’ classification accuracies were 0.767 ± 0.081, 0.718 ± 0.062, and 0.688 ± 0.070, respectively, and the AUCs were 0.793 ± 0.086, 0.779 ± 0.075, and 0.771 ± 0.072, respectively.ConclusionsThere were statistically significant differences in tongue feature between different stages of NSCLC, with advanced NSCLC tongue feature being more closely correlated with tumor marker. Due to the limited information, single data sources including baseline, tongue feature, and tumor marker cannot be used to identify the different stages of NSCLC in this pilot study. In addition to the logistic regression method, other machine learning methods, based on tumor marker and baseline data sets, can effectively improve the differential diagnosis efficiency of different stages of NSCLC by adding tongue image data, which requires further verification based on large sample studies in the future.

Dynamic segmental kinematics of the lumbar spine during diagnostic movements.

Background: In vivo measurements of segmental-level kinematics are a promising avenue for better understanding the relationship between pain and its underlying, multi-factorial basis. To date, the bulk of the reported segmental-level motion has been restricted to single plane motions. Methods: The present work implemented a novel marker set used with an optical motion capture system to non-invasively measure dynamic, 3D in vivo segmental kinematics of the lower spine in a laboratory setting. Lumbar spinal kinematics were measured for 28 subjects during 17 diagnostic movements. Results: Overall regional range of motion data and lumbar angular velocity measurement were consistent with previously published studies. Key findings from the work included measurement of differences in ascending versus descending segmental velocities during functional movements and observations of motion coupling paradigms in the lumbar spinal segments. Conclusion: The work contributes to the task of establishing a baseline of segmental lumbar movement patterns in an asymptomatic cohort, which serves as a necessary pre-requisite for identifying pathological and symptomatic deviations from the baseline.

Easykin: a flexible and user-friendly online tool for forensic kinship testing and missing person identification.

In forensic kinship testing and missing person identification, it is a fundamental question to choose the most informative reference relatives, select appropriate genotyping systems, and evaluate the weight of evidence comprehensively. Despite that several useful tools have been developed, they have not addressed these questions satisfactorily. In this paper, we develop a flexible and user-friendly online tool, Easykin, to address the aforementioned issues. It has some promising features: (i) Pedigrees can be constructed easily and presented intuitively with just a few mouse clicks. (ii) System power can be estimated before testing based on certain set of markers and reference relatives. (iii) The pruning function of EasyKin enables users to choose appropriate subsets of available references. (iv) Parameters at a specific LR for a single case may ease evidence interpretation. (v) The user interface (UI) is an HTML-based dashboard, which is friendly to both professional and non-professional users and can be used anytime and anywhere. Here, we presented three common cases as examples to demonstrate how kinship testing and missing person identification can be improved with EasyKin. In conclusion, this tool provides a one-stop solution for forensic use, that is, instructing users to choose appropriate kits and reference relatives before testing, calculating LR in the testing, and providing parameters for data interpretation after testing. EasyKin is freely available at https://forensicsysu.shinyapps.io/EasyKin/ .

DNA methylation-based depiction of the immune microenvironment and immune-associated long non-coding RNAs in oral cavity squamous cell carcinomas

Oral cavity squamous cell carcinoma (OSCC) is a complex and dynamic disease characterized by clinicopathological and molecular heterogeneity. Spatial and temporal heterogeneity of cell subpopulations has been associated with cancer progression and implicated in the prognosis and therapy response. Emerging evidence indicates that aberrant epigenetic profiles in OSCC may foster an immunosuppressive tumor microenvironment by modulating the expression of immune-related long non-coding RNAs (lncRNAs). DNA methylation analysis was performed in 46 matched OSCC and normal adjacent tissue samples using a genome-wide platform (Infinium HumanMethylation450 BeadChip). Reference-based computational deconvolution (MethylCIBERSORT) was applied to infer the immune cell composition of the bulk samples. The expression levels of genes encoding immune markers and differentially methylated lncRNAs were investigated using The Cancer Genome Atlas dataset. OSCC specimens presented distinct immune cell composition, including the enrichment of monocyte lineage cells, natural killer cells, cytotoxic T-lymphocytes, regulatory T-lymphocytes, and neutrophils. In contrast, B-lymphocytes, effector T-lymphocytes, and fibroblasts were diminished in tumor samples. The hypomethylation of three immune-associated lncRNAs (MEG3, MIR155HG, and WFDC21P) at individual CpG sites was confirmed by bisulfite-pyrosequencing. Also, the upregulation of a set of immune markers (FOXP3, GZMB, IL10, IL2RA, TGFB, IFNG, TDO2, IDO1, and HIF1A) was detected. The immune cell composition, immune markers alteration, and dysregulation of immune-associated lncRNAs reinforce the impact of the immune microenvironment in OSCC. These concurrent factors contribute to tumor heterogeneity, suggesting that epi-immunotherapy could be an efficient alternative to treat OSCC.

FAT4 expression in peripheral blood mononuclear cells is associated with prognosis and immune cell infiltration in hepatocellular carcinoma

Peripheral blood mononuclear cell (PBMC) genes reflect the host immune status and could be suitable for evaluating the prognosis of patients with hepatocellular carcinoma (HCC), for which a reliable biomarker is unavailable and the host immune responses to cancer cells. This study aimed to investigate prognostically relevant genes in HCC PBMCs and assessed whether their expression represents tumor immune infiltration. Gene expression in PBMCs from patients with advanced or terminal HCC who had survived or died was examined. Correlations among FAT atypical cadherin 4 (FAT4) expression, cancer immune characteristics, and infiltrated immune cell gene marker sets were analyzed. FAT4 expression was lower in the PBMCs of patients with advanced or terminal HCC who had died than that in patients who survived. Kaplan–Meier analysis indicated that FAT4 downregulation was associated with a relatively poor prognosis while overexpression was positively correlated with immune cell infiltration, several immune cell markers, and immune checkpoint expression. Hsa-miR-93-5p represented the most probable upstream microRNA of FAT4. Thus, upregulated FAT4 in PBMCs and HCC tissues might indicate a favorable prognosis and increased immune cell infiltration, while miRNA-93-5p could be a modulator of FAT4 expression. Collectively, these findings suggest novel immunotherapy targets for HCC.

Comprehensive analysis of cell-extracellular matrix protein Ras suppressor-1 in function and prognosis of gastrointestinal cancers.

Ras suppressor 1 (RSU1), a highly conserved protein, plays an important role in actin cytoskeleton remodeling and cell-extracellular matrix adhesion. Aberration of RSU1 activity can cause changes in cell adhesion and migration, thereby enhancing tumor proliferation and metastasis. However, the correlation between RSU1 and gastrointestinal cancers (GICs), as well as its prognostic role related to tumor-infiltrating immune cells (TIICs) remains unclear. To shows RSU1 plays a potential promoting role in facilitating tumor immune escape in GIC. Differential expression of RSU1 in different tumors and their corresponding normal tissues was evaluated by exploring the Gene Expression Profiling Interactive Analysis (GEPIA) dataset. The correlation between RSU1 expression and prognosis of GIC cancer patients was evaluated by Kaplan-Meier plotter. Then, RSU1-correlated genes were screened and functionally characterized via enrichment analysis. The correlation between RSU1 and TIICs was further characterized using the Tumor Immune Estimation Resource (TIMER). In addition, the correlation between RSU1 and immune cell surface molecules was also analyzed by TIMER. High RSU1 expression was associated with poor overall survival of gastric cancer patients, exhibiting a hazard ratio (HR) = 1.36, first progression HR = 1.53, and post progression survival HR = 1.6. Specifically, high RSU1 Levels were associated with prognosis of gastric cancer in females, T4 and N3 stages, and Her-2-negative subtypes. Regarding immune-infiltrating cells, RSU1 expression level was positively correlated with infiltration of CD4+ T cells, macrophages, neutrophils, and dendritic cells (DCs) in colorectal adenocarcinoma and stomach adenocarcinoma. RSU1 expression was also predicted to be strongly correlated with immune marker sets in M2 macrophage, DCs and T cell exhaustion in GICs. In gastrointestinal cancers, RSU1 is increased in tumor tissues, and predicts poor survival of patients. Increased RSU1 may be involved in promoting macrophage polarization, DC infiltration, and T cell exhaustion, inducing tumor immune escape and the development of tumors in GICs. We suggest that RSU1 is a promising prognostic biomarker reflecting immune infiltration level of GICs, as well as a potential therapeutic target for precision treatment through improving the immune response.

Development of an SNP marker set for marker-assisted backcrossing using genotyping-by-sequencing in tetraploid perilla.

High-quality molecular markers are essential for marker-assisted selection to accelerate breeding progress. Compared with diploid species, recently diverged polyploid crop species tend to have highly similar homeologous subgenomes, which is expected to limit the development of broadly applicable locus-specific single-nucleotide polymorphism (SNP) assays. Furthermore, it is particularly challenging to make genome-wide marker sets for species that lack a reference genome. Here, we report the development of a genome-wide set of kompetitive allele specific PCR (KASP) markers for marker-assisted recurrent selection (MARS) in the tetraploid minor crop perilla. To find locus-specific SNP markers across the perilla genome, we used genotyping-by-sequencing (GBS) to construct linkage maps of two F2 populations. The two resulting high-resolution linkage maps comprised 2326 and 2454 SNP markers that spanned a total genetic distance of 2133cM across 16 linkage groups and 2169cM across 21 linkage groups, respectively. We then obtained a final genetic map consisting of 22 linkage groups with 1123 common markers from the two genetic maps. We selected 96 genome-wide markers for MARS and confirmed the accuracy of markers in the two F2 populations using a high-throughput Fluidigm system. We confirmed that 91.8% of the SNP genotyping results from the Fluidigm assay were the same as the results obtained through GBS. These results provide a foundation for marker-assisted backcrossing and the development of new varieties of perilla.

Discovering useful genetic variation in the seed parent gene pool for sorghum improvement.

Multi-parent populations contain valuable genetic material for dissecting complex, quantitative traits and provide a unique opportunity to capture multi-allelic variation compared to the biparental populations. A multi-parent advanced generation inter-cross (MAGIC) B-line (MBL) population composed of 708 F6 recombinant inbred lines (RILs), was recently developed from four diverse founders. These selected founders strategically represented the four most prevalent botanical races (kafir, guinea, durra, and caudatum) to capture a significant source of genetic variation to study the quantitative traits in grain sorghum [Sorghum bicolor (L.) Moench]. MBL was phenotyped at two field locations for seven yield-influencing traits: panicle type (PT), days to anthesis (DTA), plant height (PH), grain yield (GY), 1000-grain weight (TGW), tiller number per meter (TN) and yield per panicle (YPP). High phenotypic variation was observed for all the quantitative traits, with broad-sense heritabilities ranging from 0.34 (TN) to 0.84 (PH). The entire population was genotyped using Diversity Arrays Technology (DArTseq), and 8,800 single nucleotide polymorphisms (SNPs) were generated. A set of polymorphic, quality-filtered markers (3,751 SNPs) and phenotypic data were used for genome-wide association studies (GWAS). We identified 52 marker-trait associations (MTAs) for the seven traits using BLUPs generated from replicated plots in two locations. We also identified desirable allelic combinations based on the plant height loci (Dw1, Dw2, and Dw3), which influences yield related traits. Additionally, two novel MTAs were identified each on Chr1 and Chr7 for yield traits independent of dwarfing genes. We further performed a multi-variate adaptive shrinkage analysis and 15 MTAs with pleiotropic effect were identified. The five best performing MBL progenies were selected carrying desirable allelic combinations. Since the MBL population was designed to capture significant diversity for maintainer line (B-line) accessions, these progenies can serve as valuable resources to develop superior sorghum hybrids after validation of their general combining abilities via crossing with elite pollinators. Further, newly identified desirable allelic combinations can be used to enrich the maintainer germplasm lines through marker-assisted backcross breeding.