Informative Markers Research Articles

Exploring the correlations between six serological inflammatory markers and different stages of type 2 diabetic retinopathy

To determine the correlations between six serological inflammatory markers, namely the systemic immune-inflammation index (SII), systemic inflammatory response index (SIRI), aggregate index of systemic inflammation (AISI), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and monocyte-to-lymphocyte ratio (MLR), and various stages of type 2 diabetic retinopathy (T2DR). Additionally, the diagnostic value of these markers in T2DR was evaluated. Clinical data were collected from a total of 397 patients with type 2 diabetes who visited the ophthalmology department at Mian Yang Central Hospital and the Affiliated Hospital of Southwest Medical University from January 2023 to December 2023. Based on the results of fundus photography, patients were categorized into a non-diabetic retinopathy group (NDR, n = 121), a non-proliferative diabetic retinopathy group (NPDR, n = 77), and a proliferative diabetic retinopathy group (PDR, n = 199). General patient information and systemic inflammatory markers, including the SII, SIRI, AIRI, NLR, PLR, and MLR, were compared among the groups, and their correlations with T2DR were analyzed. The SII values were found to be significantly higher in the PDR group compared to the NPDR group, which in turn were higher than those in the NDR group (P < 0.05). Similarly, the AISI values were significantly elevated in the PDR group compared to both the NPDR and NDR groups (P < 0.05). The SIRI and MLR values were significantly higher in the PDR group than in the NDR group (P < 0.05). Furthermore, the NLR and PLR values were significantly higher in the NPDR and PDR groups compared to the NDR group (P < 0.05). The Mantel‒Haenszel chi-square test revealed a significant linear trend between the SII and PLR and the incidence of PDR (P < 0.001), with the incidence of PDR increasing as the quartile levels of the SII and PLR increased. Multivariate logistic regression analysis indicated that, compared with NDR, a higher SII was found to be an independent risk factor for NPDR (ORSII = 1.002, p = 0.001) and PDR (ORSII = 1.002, P < 0.001). The ROC curve analysis suggested that the combined assessment of the six inflammatory indices had the highest accuracy in the evaluation of DR, with an area under the curve (AUC) of 0.69, a sensitivity of 54%, and a specificity of 75%. The results of this study indicate that the SII is an independent risk factor for T2DR. A close correlation was observed between the SII and PLR and the occurrence and progression of T2DR. The high accuracy of the combined diagnosis of T2DR via various serological inflammatory markers underscores their potential as early biological indicators for the diagnosis of T2DR.

A machine learning approach for estimating Eastern Asian origins from massive screening of Y chromosomal short tandem repeats polymorphisms.

Inferring the ancestral origin of DNA evidence recovered from crime scenes is crucial in forensic investigations, especially in the absence of a direct suspect match. Ancestry informative markers (AIMs) have been widely researched and commercially developed into panels targeting multiple continental regions. However, existing forensic ancestry inference panels typically group East Asian individuals into a homogenous category without further differentiation. In this study, we screened Y chromosomal short tandem repeat (Y-STR) haplotypes from 10,154 Asian individuals to explore their genetic structure and generate an ancestry inference tool through a machine learning (ML) approach. Our research identified distinct genetic separations between East Asians and their neighboring Southwest Asians, with tendencies of northern and southern differentiation observed within East Asian populations. All machine learning models developed in this study demonstrated high accuracy, with the Asian classification model achieving an optimal performance of 82.92% and the East Asian classification model reaching 84.98% accuracy. This work not only deepens the understanding of genetic substructures within Asian populations but also showcases the potential of ML in forensic ancestry inference using extensive Y-STR data. By employing computational methods to analyze intricate genetic datasets, we can enhance the resolution of ancestry in forensic contexts involving Asian populations.

The effects of socioeconomic position on endogenous pain modulation: A quasi-experimental approach.

Socioeconomic Position (SEP) is a multidimensional construct encompassing education, income, occupation, and neighborhood distress, influencing chronic pain severity, interference, and duration. However, its impact on placebo analgesia, where reduced pain perception occurs due to treatment belief, remains understudied. Using a quasi-experimental approach, we investigated SEP's influence on placebo analgesia in 401 participants with temporomandibular disorder (TMD) and 400 pain-free individuals. Using latent class analysis, we grouped participants into two SEP groups based on self-reported education, income, occupation, and neighborhood distress indices, including the area deprivation and distressed community indexes. Ancestry Informative Markers (AIMs) and self-reported race were included to account for genetic and demographic influences. Placebo analgesia was elicited using verbal suggestion and classical conditioning. Linear mixed models were employed to analyze SEP's impact, while multiple regression and ANCOVA assessed AIMs' and race's effects. Comparable placebo effects were observed between participants with TMD and pain-free individuals (F(1,4765.73) = 0.49, p = 0.48). A trend was noted in the main effect of SEP (F(1,4764.5) = 3.64, p = 0.056). Among TMD participants, those with distressed SEP exhibited lower placebo analgesia (F(1,4765.73) = 7.9, p = 0.005), while placebo response did not differ by SEP in pain-free participants (F(1,4765.73) = 0.27, p = 0.59). East Asian ancestry (β = 5.71, 95% CI [1.50, 9.92]) and self-reported Asian (mean = 24.20, sem = 1.52, p = 0.020) were associated with greater placebo analgesia. This study highlights the interplay of SEP, AIMs, and race in placebo analgesia and calls for tailored pain management interventions. PERSPECTIVE: SEP significantly contributes to pain disparities. This quasi-experimental study demonstrates analogous placebo analgesia between chronic pain and pain-free individuals but finds lower placebo analgesia only among individuals with chronic pain and distressed SEP. This highlights a link between chronic pain, SEP, and impaired placebo effects, suggesting new avenues for research.

HEART RATE VARIABILITY IN THE ASSESSMENT OF ADAPTIVE CAPACITY IN NEWBORNS WITH PERINATAL INJURY OF CENTRAL NERVOUS SYSTEM

Relevance. The neurohumoral mechanisms regulating heart rhythm in newborns are a focal point of research in perinatal cardiology. This emphasis arises from the fact that heart rhythm serves as a key indicator of the functional interplay within the cardiovascular system and the overall physiological state of a newborn, acting as a primary marker of autonomic nervous system activity. Aim: To examine the characteristics of autonomic heart rhythm regulation in newborns with perinatal injuries of central nervous system through daily electrocardiogram monitoring. Subjects and methods. The study included 40 full-term newborns divided into two groups: Group I involved conditionally healthy newborns (n = 23), and Group II included newborns with hypoxic-ischemic lesions of the central nervous system (n = 17). Based on temporal and spectral analysis of heart rate variability, it was demonstrated that changes in the early neonatal period correlate with clinical signs of impaired postnatal adaptation. By the 3rd–4th day of life, all newborns exhibited a decrease in parasympathetic influence on heart rhythm and increased activity of the sympathetic division of the autonomic nervous system during the day. Hypoxic-ischemic injury of the central nervous system was associated with more pronounced autonomic dysfunction, leading to impaired regulation of the circulatory system. An imbalance between sympathetic and parasympathetic influences on the heart in newborns, who experienced asphyxia was identified as a significant arrhythmogenic factor. Heart rate variability emerges as a critical, informative marker for identifying at-risk newborns prone to developing heart rhythm disorders, facilitating timely inclusion in risk groups and ensuring appropriate follow-up care.

The Frequency of DPYD c.557A&gt;G in the Dominican Population and Its Association with African Ancestry

Background/Objectives: Genetic polymorphism of the dihydropyrimidine dehydrogenase gene (DPYD) is responsible for the variability found in the metabolism of fluoropyrimidines such as 5-fluorouracil (5-FU), capecitabine, or tegafur. The DPYD genotype is linked to variability in enzyme activity, 5-FU elimination, and toxicity. Approximately 10–40% of patients treated with fluoropyrimidines develop severe toxicity. The interethnic variability of DPYD gene variants in Afro-Latin Americans is poorly studied, thereby establishing a barrier to the implementation of personalized medicine in these populations. Therefore, the present study aims to analyze the frequency of DPYD variants with clinical relevance in the Dominican population and their association with genomic ancestry components. Methods: For this study, 196 healthy volunteers from the Dominican Republic were genotyped for DPYD variants by qPCR, and individual genomic ancestry analysis was performed in 178 individuals using 90 informative ancestry markers. Data from the 1000 Genomes project were also retrieved for comparison and increased statistical power. Results and Conclusions: The c.557A&gt;G variant (decreased dihydropyrimidine dehydrogenase function) presented a frequency of 2.6% in the Dominican population. Moreover, the frequency of this variant is positively associated with African ancestry (r2 = 0.67, p = 1 × 10−7), which implies that individuals with high levels of African ancestry are more likely to present this variant. HapB3 is completely absent in Dominican, Mexican, Peruvian, Bangladeshi, and all East Asian and African populations, which probably makes its analysis dispensable in these populations. The implementation of pharmacogenetics in oncology, specifically DPYD, in populations of Afro-Latin American ancestry should include c.557A&gt;G, to be able to carry out the safe and effective treatment of patients treated with fluoropyrimidines.

The Application of Imaging and Molecular Markers in Cancer Diagnosis and Treatment

Cancer is one of the leading causes of death worldwide, and its diagnosis and treatment face many challenges. In recent years, the combination of imaging techniques and molecular markers has made remarkable progress in cancer diagnosis and treatment. Imaging techniques such as CT, MRI and PET-CT can provide anatomical and functional information of tumors, while molecular markers such as EGFR and HER2 play a key role in tumor diagnosis, prognosis and treatment. However, how to effectively combine these technologies to achieve more accurate diagnosis and treatment is still a gap in research. This paper reviews the combined application of imaging technology and molecular markers in cancer diagnosis, therapeutic response monitoring and individualized therapy, analyzes the synergistic effect of multimodal imaging technology and molecular markers, and finds that it has significant advantages in early detection and individualized treatment planning. Combining imaging and molecular marker information can improve the accuracy of cancer diagnosis and guide targeted therapies. The combination of such technologies provides a new reference for precision cancer medicine in the future, but further research is still needed in the aspects of marker sensitivity and image analysis automation. In the future, the combination of new technologies such as artificial intelligence is expected to promote the development of more accurate and efficient cancer diagnosis and treatment.

Polymorphism of Genes Potentially Affecting Growth and Body Size Suggests Genetic Divergence in Wild and Domestic Reindeer (Rangifer tarandus) Populations.

Background/Objectives: A combination of increased human presence in the Arctic zone alongside climate change has led to a decrease in the number of wild reindeer (Rangifer tarandus). Studying the genetic potential of this species will aid in conservation efforts, while simultaneously promoting improved meat productivity in domestic reindeer. Alongside reducing feed costs, increasing disease resistance, etc., acquiring genetic variation information is a crucial task for domestic reindeer husbandry. This study thus identified highly informative molecular genetic markers usable for assessing genetic diversity and breeding purposes in reindeer. Methods: We analyzed gene polymorphism that may potentially affect animal growth and development in populations of wild (Taimyr Peninsula) and domestic reindeer, including Nenets and Evenk breeds. We screened these populations for polymorphisms by sequencing the GH, GHR, LCORL and BMP2 genes. Results: Following generation of gene sequences, we compared the alleles frequency in the surveyed populations and their genetic divergence. Some loci lacked polymorphism in wild reindeer, unlike domestic breeds. This could suggest a selection-driven microevolutionary divergence in domestic reindeer populations. An isolated domestic population from Kolguyev Island appeared to be genetically remote from continental reindeer. Conclusions: Molecular genetic markers associated with economically important traits in reindeer can be further developed using the data obtained. Monitoring wild reindeer populations and better utilizing the genetic potential of domestic animals will depend on a panel of these marker genes. By using this marker panel, the amount of time spent on selection efforts will be greatly reduced to enhance meat performance during reindeer breeding.

DINOV2-FCS: a model for fruit leaf disease classification and severity prediction.

The assessment of the severity of fruit disease is crucial for the optimization of fruit production. By quantifying the percentage of leaf disease, an effective approach to determining the severity of the disease is available. However, the current prediction of disease degree by machine learning methods still faces challenges, including suboptimal accuracy and limited generalizability. In light of the growing application of large model technology across a range of fields, this study draws upon the DINOV2 visual large vision model backbone network to construct the DINOV2-Fruit Leaf Classification and Segmentation Model (DINOV2-FCS), a model designed for the classification and severity prediction of diverse fruit leaf diseases. DINOV2-FCS employs the DINOv2-B (distilled) backbone feature extraction network to enhance the extraction of features from fruit disease leaf images. In fruit leaf disease classification, for the problem that leaf spots of different diseases have great similarity, we have proposed Class-Patch Feature Fusion Module (C-PFFM), which integrates the local detailed feature information of the spots and the global feature information of the class markers. For the problem that the model ignores the fine spots in the segmentation process, we propose Explicit Feature Fusion Architecture (EFFA) and Alterable Kernel Atrous Spatial Pyramid Pooling (AKASPP), which improve the segmentation effect of the model. To verify the accuracy and generalizability of the model, two sets of experiments were conducted. First, the labeled leaf disease dataset of five fruits was randomly divided. The trained model exhibited an accuracy of 99.67% in disease classification, an mIoU of 90.29%, and an accuracy of 95.68% in disease severity classification. In the generalizability experiment, four disease data sets were used for training and one for testing. The mIoU of the trained model reached 83.95%, and the accuracy of disease severity grading was 95.24%. The results demonstrate that the model exhibits superior performance compared to other state-of-the-art models and that the model has strong generalization capabilities. This study provides a new method for leaf disease classification and leaf disease severity prediction for a variety of fruits. Code is available at https://github.com/BaiChunhui2001/DINOV2-FCS.

Navigating diversity: primer impact on arthropod recovery in molecular environmental monitoring in tropical Amazon

IntroductionThe urgent need for effective environmental monitoring amid the escalating biodiversity crisis has prompted the adoption of molecular techniques like DNA metabarcoding. Through sequencing of taxonomically informative mitochondrial markers in bulk arthropod samples, metabarcoding allows assessment of arthropod diversity, which is crucial for ecosystem health evaluations, especially in threatened regions like the Amazon. However, challenges such as primer biases and reference database limitations persist. MethodsHere, we assess the performance of four metabarcoding primer sets, two COI markers (ZBJ-ArtF1c/ZBJ-ArtR2c [Zeale]: 157bp, targeting arthropods, and mlCOIintF/jgHCO2198 [Leray]: 313bp, targeting metazoans) and two 16S markers (Ins16S_1shortF/Ins16S_1shortR [Ins16S]: 150bp, targeting insects, and Coleop_16Sc/Coleop_16Sd [EPP]: 105bp, targeting arthropods, mainly Coleoptera) in amplifying the taxonomic constituents of bulk arthropod samples, collected across different natural and anthropogenic habitats from the Brazilian Amazon biome. To evaluate primer performance, we used the indicators (i) amplification efficiency, (ii) primer specificity, i.e., the amount of non-target sequences, (iii) detected OTU richness, (iv) group coverage and (v) taxonomic resolution. Finally, we (vi) estimated the refinement in taxa recovery by additional amplifications. ResultsDespite lower specificity and contrasting results regarding OTU richness, the primer pairs returning larger fragments showed higher taxonomic resolution (Ins16S) and broader taxonomic coverage (Leray) than Zeale and EPP did. Furthermore, results demonstrated the complementarity of the Leray and the Ins16S primer sets from taxon-dependent studies. ConclusionsDespite limitations, combining these primers could enhance biodiversity monitoring in the region. Considering incomplete reference gene banks, primers maximizing OTU richness (EPP and Leray) may be the best choice for taxon-independent surveys. These findings underscore the importance of primer selection and highlight the ongoing efforts to refine DNA metabarcoding for robust environmental assessments.

Genome-wide association analysis of body conformation traits in Chinese Holstein Cattle

BackgroundThe body conformation traits of dairy cattle are closely related to their production performance and health. The present study aimed to identify gene variants associated with body conformation traits in Chinese Holstein cattle and provide marker loci for genomic selection in dairy cattle breeding. These findings could offer robust theoretical support for optimizing the health of dairy cattle and enhancing their production performance.ResultsThis study involved 586 Chinese Holstein cattle and used the predicted transmitting abilities (PTAs) of 17 body conformation traits evaluated by the Council on Dairy Cattle Breeding in the USA as phenotypic values. These traits were categorized into body size traits, rump traits, feet/legs traits, udder traits, and dairy characteristic traits. On the basis of the genomic profiling results from the Genomic Profiler Bovine 100 K SNP chip, genotype data were quality controlled via PLINK software, and 586 individuals and 80,713 SNPs were retained for further analysis. Genome-wide association studies (GWASs) were conducted via GEMMA software, which employs both univariate linear mixed models (LMMs) and multivariate linear mixed models (mvLMMs). The Bonferroni method was used to determine the significance threshold, identifying gene variants significantly associated with body conformation traits in Chinese Holstein cattle. The single-trait GWAS identified 24 SNPs significantly associated with body conformation traits (P < 0.01), with annotation leading to the identification of 21 candidate genes. The multi-trait GWAS identified 54 SNPs, which were annotated to 57 candidate genes, including 39 new SNPs not identified in the single-trait GWAS. Additionally, 14 SNPs in the 86.84–87.41 Mb region of chromosome 6 were significantly associated with multiple traits, such as body size, udder, and dairy characteristics. Four genes—SLC4A4, GC, NPFFR2, and ADAMTS3—were annotated in this region.ConclusionsA total of 63 SNPs were identified as significantly associated with 17 body conformation traits in Chinese Holstein cattle through both single-trait and multi-trait GWAS analyses. Sixty-six candidate genes were annotated, with 12 genes identified by both methods, such as SLC4A4, GC, NPFFR2, and ADAMTS3, which are involved in pathways such as growth hormone synthesis and secretion, sphingolipid signaling, and dopaminergic synapse pathways. These findings provide potential genetic marker information related to body conformation traits for the breeding of Chinese Holstein cattle.

Hierarchical marker genes selection in scRNA-seq analysis.

When analyzing scRNA-seq data containing heterogeneous cell populations, an important task is to select informative marker genes to distinguish various cell clusters and annotate the clusters with biologically meaningful cell types. In existing analysis methods and pipelines, marker genes are typically identified using a one-vs-all strategy, examining differential expression between one cell cluster versus the combination of all other cell clusters. However, this strategy applied to cell clusters belonging to closely related cell types often generates overlapping marker genes, which capture the common signature of closely related cell clusters but provide limited information for distinguishing them. To address the limitations of the one-vs-all strategy, we propose a hierarchical marker gene selection strategy that groups similar cell clusters and selects marker genes in a hierarchical manner. This strategy is able to improve the accuracy and interpretability of cell type identification in single-cell RNA-seq data.

Association of the polymorphism of the genes coding the renin-angiotensin system and the LZTFL1 gene with the severity and outcomes of SARS-CoV-2 infection

The aim of the study is to establish the association of variants of a number of genes involved in the reninangiotensin system with the severity of SARS-CoV-2 infection.The study included 206 hospitalized patients with COVID-19: Group 1 – 99 patients with severe COVID-19; Group 2 ‒ 107 with moderate and mild forms of the disease.Polymorphic variants of the ACE rs4646994, ACE2 rs2074192 rs2285666 and rs413031713 and LZTFL1 rs10490770 genes were analyzed using quantitative PCR with TaqMan probes (Primetech, Belarus) on a CFX96 thermocycler (Bio-Rad, USA). AGT rs699 and AGTR1 rs5186 gene variants were determined using a Rotor-Gene Q thermocycler (QIAGEN, Germany), in accordance to the protocols of the manufacturer (Litekh, Russia).We revealed that the genetic markers of the severe COVID-19 infection in the Belarusian patients are the heterozygous T/C genotype of the LZTFL1 rs10490770 variant, as well as the A-A haplotype of the ACE2 rs2074192 and rs2285666 loci, and the A-A-C allelic combination of the ACE2 rs2074192, rs2285666 and AGT rs699 genes. Genetic markers associated with mild/moderate severity of COVID-19 were also identified: heterozygous A/C genotype of the AGTR1 rs5186 variant, G-G-T allelic combination at the ACE2 rs2074192 and rs2285666, AGT rs699 loci (among carriers of these gene variants, the risk of severe disease is reduced).The loci of the genes encoding the renin-angiotensin system – ACE2 rs2074192 and rs2285666, AGTR1 rs5186, and the LZTFL1 rs10490770 genes, as well as allelic combinations between the ACE2 rs2074192, rs2285666 and AGT rs699 loci are informative markers for predicting the severity of COVID-19.

Effects of psychoplastogens on blood levels of brain-derived neurotrophic factor (BDNF) in humans: a systematic review and meta-analysis.

Peripheral levels of brain-derived neurotrophic factor (BDNF) are often used as a biomarker for the rapid plasticity-promoting effects of ketamine, psychedelics, and other psychoplastogens in humans. However, studies analyzing peripheral BDNF after psychoplastogen exposure show mixed results. In this meta-analysis, we aimed to test whether the rapid upregulation of neuroplasticity seen in preclinical studies is detectable using peripheral BDNF in humans. This analysis was pre-registered (PROSPERO ID: CRD42022333096) and funded by the University of Fribourg. We systematically searched PubMed, Web of Science, and PsycINFO to meta-analyze the effects of all available psychoplastogens on peripheral BDNF levels in humans, including ketamine, esketamine, LSD, psilocybin, ayahuasca, DMT, MDMA, scopolamine, and rapastinel. Risk of bias was assessed using Cochrane Risk of Bias Tools. Using meta-regressions and mixed effects models, we additionally analyzed the impact of several potential moderators. We included 29 studies and found no evidence that psychoplastogens elevate peripheral BDNF levels in humans (SMD = 0.024, p = 0.64). This result was not affected by drug, dose, blood fraction, participant age, or psychiatric diagnoses. In general, studies with better-controlled designs and fewer missing values reported smaller effect sizes. Later measurement timepoints showed minimally larger effects on BDNF. These data suggest that peripheral BDNF levels do not change after psychoplastogen administration in humans. It is possible that peripheral BDNF is not an informative marker of rapid changes in neuroplasticity, or that preclinical findings on psychoplastogens and neuroplasticity may not translate to human subjects. Limitations of this analysis include the reliability and validity of BDNF measurement and low variation in some potential moderators. More precise methods of measuring rapid changes in neuroplasticity, including neuroimaging and stimulation-based methods, are recommended for future studies attempting to translate preclinical findings to humans.

A review of deep learning models for the prediction of chromatin interactions with DNA and epigenomic profiles.

Advances in three-dimensional (3D) genomics have revealed the spatial characteristics of chromatin interactions in gene expression regulation, which is crucial for understanding molecular mechanisms in biological processes. High-throughput technologies like ChIA-PET, Hi-C, and their derivatives methods have greatly enhanced our knowledge of 3D chromatin architecture. However, the chromatin interaction mechanisms remain largely unexplored. Deep learning, with its powerful feature extraction and pattern recognition capabilities, offers a promising approach for integrating multi-omics data, to build accurate predictive models of chromatin interaction matrices. This review systematically summarizes recent advances in chromatin interaction matrix prediction models. By integrating DNA sequences and epigenetic signals, we investigate the latest developments in these methods. This article details various models, focusing on how one-dimensional (1D) information transforms into the 3D structure chromatin interactions, and how the integration of different deep learning modules specifically affects model accuracy. Additionally, we discuss the critical role of DNA sequence information and epigenetic markers in shaping 3D genome interaction patterns. Finally, this review addresses the challenges in predicting chromatin interaction matrices, in order to improve the precise mapping of chromatin interaction matrices and DNA sequence, and supporting the transformation and theoretical development of 3D genomics across biological systems.

Development of SNP Panels from Low‐Coverage Whole Genome Sequencing (lcWGS) to Support Indigenous Fisheries for Three Salmonid Species in Northern Canada

ABSTRACTSingle nucleotide polymorphism (SNP) panels are powerful tools for assessing the genetic population structure and dispersal of fishes and can enhance management practices for commercial, recreational and subsistence mixed‐stock fisheries. Arctic Char (Salvelinus alpinus), Brook Trout (Salvelinus fontinalis) and Lake Whitefish (Coregonus clupeaformis) are among the most harvested and consumed fish species in Northern Indigenous communities in Canada, contributing significantly to food security, culture, tradition and economy. However, genetic resources supporting Indigenous fisheries have not been widely accessible to northern communities (e.g. Inuit, Cree, Dene). Here, we developed Genotyping‐in‐Thousands by sequencing (GT‐seq) panels for population assignment and mixed‐stock analyses of three salmonids, to support fisheries stewardship or co‐management in Northern Canada. Using low‐coverage Whole Genome Sequencing data from 418 individuals across source populations in Cambridge Bay (Nunavut), Great Slave Lake (Northwest Territories), James Bay (Québec) and Mistassini Lake (Québec), we developed a bioinformatic SNP filtering workflow to select informative SNP markers from genotype likelihoods. These markers were then used to design GT‐seq panels, thus enabling high‐throughput genotyping for these species. The three GT‐seq panels yielded an average of 413 autosomal loci and were validated using 525 individuals with an average assignment accuracy of 83%. Thus, these GT‐seq panels are powerful tools for assessing population structure and quantifying the relative contributions of populations/stocks in mixed‐stock fisheries across multiple regions. Interweaving genomic data derived from these tools with Traditional Ecological Knowledge will ensure the sustainable harvest of three culturally important salmonids in Indigenous communities, contributing to food security programmes and the economy in Northern Canada.

Testing the informativeness of Y-STR and mitochondrial DNA control region markers in an attempt to predict ancestry of World War II victims from Slovenian mass grave.

Identification of human remains is a challenge in forensic genetics without relatives or personal items available. In Slovenia, a Konfin II mass grave from the Second World War (WWII) was found, containing skeletal remains of 65 victims. The archival documents detailing victims' information describe 45 persons of which 33 could be considered Germanic and 12 Slavic. This study aims to check for concordance between the victim list and actual victims found by using uniparental markers to differentiate between Slavic and non-Slavic origin by attempting to infer ancestry by analyzing the control region (CR) of mitochondrial DNA (mtDNA) and Y-chromosomal STRs. Diaphyses of femurs were used as a DNA source. Next Generation Sequencing (NGS) technology was used for mtDNA- namely HID Ion Chef™ Instrument, Precision ID mtDNA Control Region Panel, and Ion GeneStudio™ S5 System. For the Y-chromosome, PowerPlex® Y23 System (Promega) kit and SeqStudio™ for human identification (HID) were used. European DNA Profiling mtDNA Population Database (EMPOP) and Y-Chromosome STR Haplotype Reference Database (YHRD) were searched for haplotype matches. Closest haplogroups were predicted using EMPOP, Y-DNA Haplogroup Predictor- NevGen, and Whit Athey's Haplogroup Predictor. Despite mitotypes being more diverse than Y-haplotypes, the Y-haplotypes had more database matches and more unequivocal differentiation between populations. 16 victims could be considered Slavic, 15 non-Slavic, and the remaining 34 had a rather scarce informativeness- either unclear or not providing any match. To address ancestry inference more comprehensively, analysis of autosomal ancestry informative markers as well as expansion on haploid markers will be conducted in future research.

Development of a robust SNP marker set for genotyping diverse gene bank collections of polyploid roses

BackgroundDue to genetic depletion in nature, gene banks play a critical role in the long-term conservation of plant genetic resources and the provision of a wide range of plant genetic diversity for research and breeding programs. Genetic information on accessions facilitates gene bank management and can help to conserve limited resources and to identify taxonomic misclassifications or mislabelling. Here, we developed SNP markers for genotyping 4,187 mostly polyploid rose accessions from large rose collections, including the German Genebank for Roses.ResultsWe filtered SNP marker information from the RhWag68k Axiom SNP array using call rates, uniformity of the four allelic dosage groups and chromosomal position to improve genotyping efficiency. After conversion to individual PACE® markers and further filtering, we selected markers with high discriminatory power. These markers were used to analyse 4,187 accessions with a mean call rate of 91.4%. By combining two evaluation methods, the mean call rate was increased to 95.2%. Additionally, the robustness against the genotypic groups used for calling was evaluated, resulting in a final set of 18 markers. Analyses of 94 pairs of assumed duplicate accessions included as controls revealed unexpected differences for eight pairs, which were confirmed using SSR markers. After removing the duplicates and filtering for accessions that were robustly called with all 18 markers, 141 out of the 1,957 accessions showed unexpected identical marker profiles with at least one other accession in our PACE® and SSR analysis. Given the attractiveness of NGS technologies, 13 SNPs from the marker set were also analysed using amplicon sequencing, with 76% agreement observed between PACE® and amplicon markers.ConclusionsAlthough sampling error cannot be completely excluded, this is an indication that mislabelling occurs in rose collections and that molecular markers may be able to detect these cases. In future applications, our marker set could be used to develop a core reference set of representative accessions, and thus optimise the selection of gene bank accessions.

CYSTATIN C AS A MARKER OF EARLY KIDNEY DAMAGE UNDER ARTERIAL HYPERTENSION (LITERATURE REVIEW)

The purpose of this study is to determine kidney functional activity in relation to cystatin C levels, as reported in recent literature. Materials and Methods. A bibliographic-semantic method was employed to assess the current state of research on this topic, analyzing findings from previous scientific studies using both literature sources and electronic resources. Results. Chronic kidney disease (CKD) is as prevalent as hypertension. It is well-known that arterial hypertension exacerbates kidney function, making the assessment of renal functional activity critical. Glomerular filtration rate (GFR) is commonly used to evaluate kidney function, relying on established indicators as well as newer, more objective markers. Among these, cystatin C has emerged as one of the most accurate and sensitive indicators for assessing kidney function. Its concentration in serum negatively correlates with GFR and is particularly valuable for detecting renal pathology even when creatinine levels remain unchanged. This insight has led to the development of an estimated GFR (eGFR) formula that incorporates cystatin C levels. Research suggests that cystatin C levels increase with hypertension, although studies on the specific relationship between cystatin C and hypertension remain limited. Some findings propose that serum cystatin C could serve as a predictor of disease severity, particularly in elderly hypertensive patients with coronary heart disease. The literature supports the utility of cystatin C as a reliable marker for assessing GFR, which facilitates early detection of CKD even when albumin excretion is normal, thus identifying kidney damage at an early stage. Ppotential mechanisms by which elevated cystatin C may contribute to cardiovascular damage are under investigation. As a highly informative endogenous marker of GFR, serum cystatin C not only aids in staging CKD but is especially valuable in identifying early renal dysfunction. Conclusion. The measurement of cystatin C enables a highly accurate assessment of kidney function and aids in evaluating cardiovascular risk, especially when hypertension coexists with chronic kidney disease. Further research is needed to improve predictions of chronic kidney progression at various stages and in the presence of comorbidities. Such insights will support more effective preventive strategies to slow the progression of this pathology.

Comparative analysis of chloroplast genomes and phylogenetic relationships in the endemic Chinese bamboo Gelidocalamus (Bambusoideae).

Gelidocalamus Wen is a small yet taxonomically challenging genus within the Arundinarieae tribe. Recent molecular studies have suggested it may not be monophyletic. However, limited species sampling and insufficient molecular marker information have resulted in poorly resolved phylogenetic relationships within this genus. The complete chloroplast genomes covering all 16 species and one variant of Gelidocalamus were sequenced, and comparative analyses were conducted. Phylogenetic analyses were performed using different molecular markers, including chloroplast data, the nuclear ribosomal DNA (nrDNA) repeats region, and 29 mitochondrial protein-coding genes. Additionally, the divergence times of Gelidocalamus were estimated to reveal their evolutionary history. The plastomes of Gelidocalamus ranged in size from 139,500 bp to 139,801 bp, with a total of 137 identified genes, including 90 protein-coding genes, 39 tRNA genes, and 8 rRNA genes. The size of the nrDNA repeats ranged from 5,802 bp to 5,804 bp. Phylogenetic analysis based on chloroplast data revealed that Gelidocalamus is polyphyletic, with different subclades distributed within the IV and V clades. However, phylogenetic analysis based on nrDNA and mitochondrial genes did not effectively resolve the relationships within the genus. Comparative analysis of chloroplast genomes indicated that Gelidocalamus shares a high degree of similarity with closely related genera in terms of chloroplast genome collinearity, codon usage bias, and repetitive sequences. Divergence time estimation suggests that it is a relatively young group, with all members appearing successively over the past four million years. The complex phylogenetic patterns may arise from the rapid radiation of Arundinarieae. This study provides a preliminary foundation for further in-depth research on the phylogeny, genomic structural features, and divergence times of this genus.

Choice between DNA primer sets (A or B) of the ForenSeq kit: forensic evaluation in a Mexican admixed population sample.

Massively parallel sequencing (MPS) overcomes many PCR-CE limitations to analyze STRs and allow simultaneous inclusion of SNPs in forensic cases. By MPS, the ForenSeq™ DNA Signature Prep kit analyzes 27 aSTRs, 7 X-STRs, 24Y-STRs, and 94 identity-informative SNPs (iiSNPs) with the DNA Primer Set-A (DPS-A). Optionally, the DNA Primer Set-B (DPS-B) adds to the analysis 56 ancestry-informative SNPs (aiSNPs) and 24 phenotype-informative SNPs (piSNPs), but diminishes from 96 to 32 the number of samples per sequencing run. We assessed the forensic informativity provided by the loci analyzed by these two DPS in admixed individuals from Mexico City (Center, Mexico). For STRs, we report length-based (LB) and sequence-based (SB) allele frequencies and forensic parameters of the 152 identity informative markers (DPS-A). For aSTRs, the combined PD of SB genotypes (PD ~ 100%) was ~ 2949 times larger than that from LB. Conversely, the observed phenotype distribution offered low PD levels (PD = 6.6% and 10.4%), whereas piSNPs predicted accurately only the modal brown eye and dark hair colors, respectively. Similarly, aiSNPs detected a large prevalence of admixed individuals (97.3%; PD = 5.4%). Although few individuals were inferred as Europeans and Native Americans (1.37% each), they were self-declared as admixed, which result confusing for HID purposes. In brief, SB genotypes increased significantly the informativity of STRs to solve complex cases (DPS-A), whereas aiSNPs and piSNPs added mostly irrelevant information (DPS-B). We provide useful cost-benefit criteria in one Latin American population to choose DPS-A (96 samples) instead of DPS-B (32 samples) of the Forenseq kit.