Genomic Markers Research Articles

Plastome structure and phylogenetic relationships of genus Hydrocotyle (apiales): provide insights into the plastome evolution of Hydrocotyle

BackgroundThe genus Hydrocotyle Tourn. ex L. is a key group for further study on the evolution of Apiales, comprising around 170 species globally. Previous studies mainly focused on separate sections and provided much information about this genus, but its infrageneric relationships are still confusing. In addition, the genetic basis of its adaptive evolution remains poorly understood. To investigate the phylogeny and evolution of the genus, we selected ten representative species covering two of three diversity distribution centers and exhibiting rich morphology diversity. Comparative plastome analysis was conducted to clarify the structural character of Hydrocotyle plastomes. Positive selection analyses were implemented to assess the evolution of the genus. Phylogenetic inferences with protein-coding sequences (CDS) of Hydrocotyle and 17 related species were also performed.ResultsPlastomes within Hydrocotyle were generally conservative in structure, gene order, and size. A total of 14 regions (rps16-trnK, trnQ-rps16, atpI-atpH, trnC-petN-psbM, ycf3-trnS, accD-psaI-ycf4, petA-psbJ, rps12-rpl20, rpl16 intron, rps3-rpl16 intron, rps9-rpl22, ndhF-rpl32, ndhA intron, and ycf1a) were recognized as hotspot regions within the genus, which suggested to be promising DNA barcodes for global phylogenetic analysis of Hydrocotyle. The ycf15 gene was suggested to be a protein-coding gene for Hydrocotyle species, and it could be used as a DNA barcode to identify Hydrocotyle. In phylogenetic analysis, three monophyletic clades (Clade I, II, III) were identified with evidence of rapid radiation speciation within Clade I. The selective pressure analysis detected that six CDS genes (ycf1b, matK, atpF, accD, rps14, and psbB) of Hydrocotyle species were under positive selection. Within the genus, the last four genes were conservative, suggesting a relation to the unique evolution of the genus in Apiales. Seven genes (atpE, matK, psbH, ycf1a, ycf1b, rpoA, and ycf2) were detected to be under some degree of positive selection in different taxa within the genus Hydrocotyle, indicating their role in the adaptive evolution of species.ConclusionsOur study offers new insights into the phylogeny and adaptive evolution of Hydrocotyle. The plastome sequences could significantly enhance phylogenetic resolution and provide genomic resources and potential DNA markers useful for future studies of the genus.

Genome-wide identification of SSR markers for Curcuma alismatifolia Gagnep., and their potential for wider application in this genus

The genus Curcuma, containing over 120 species, have considerable ornamental, edible and medicinal value. Due to the persistent lack of efficient genomic SSR markers, the conservation and identification of Curcuma genetic resources have faced substantial challenges in practical applications. To date, there are few systematic researches on whole-genome mining of SSR locus in the genus Curcuma. Herein, we performed the first deep identification of genome-wide SSR markers based on the whole-genome data of C. alismatifolia. A total of 257,032 SSR loci were identified with an average density of 216.1–367.3 SSRs/Mb within each chromosome. Mononucleotide repeat loci were most abundant, accounting for 55.1 % of all SSRs, with dinucleotide and trinucleotide repeats accounting for 22.6 % and 20.3 %, respectively. Moreover, 38 polymorphic genomic SSRs (g-SSR) were screened from the synthesized 280 primer pairs, with an average allele number (Na) and polymorphic information content (PIC) of 15.342 and 0.775 per locus, respectively. These markers had excellent cross-species transferability with an overall efficiency of 97.5 % in 21 Curcuma species. According to the cluster and structure analyses, the 178 Curcuma accessions were devided into three major clades correspongding to their origins, hybrid affinities and use values. Finally, a total of 66 Curcuma core collections were preserved, with no significant difference in genetic diversity between the core and entire collections by the t-test. A combination of numbers and letters was employed to establish DNA barcodes for 66 core collections. This study provides valuable molecular markers for wild-collection and conservation, genetic diversity analysis and marker-assisted selection breeding of Curcuma.

Predictive analysis of breast cancer metastasis and identification of genetic markers using machine learning.

4 Background: The study devises a framework integrating machine learning (ML) paradigms with explainable artificial intelligence (XAI) to prognosticate the metastatic trajectory of breast cancer (BC) and delineate critical genomic indicators pertinent to metastasis. Methods: An examination was conducted on 98 initial BC specimens, which included 34 instances evolving into distant metastasis within a quintennial monitoring phase and 44 instances evincing no recurrence for a minimum pentad post-diagnosis. Genomic datasets underwent rigorous biostatistical scrutiny, followed by the implementation of an elastic net algorithm for feature discernment, thereby constraining the scope to a salient subset of genomic markers implicated in BC metastasis. An ensemble of advanced predictive models encompassing Light Gradient Boosting Machine (LightGBM), Categorical Boosting (CatBoost), Extreme Gradient Boosting (XGBoost), Gradient Boosting Trees (GBT), and Adaptive Boosting (AdaBoost) was deployed. Model efficacy was gauged through metrics such as accuracy, F1 score, precision, recall, the Area Under the Receiver Operating Characteristic Curve (AUC), and the Brier score. To elucidate the reasoning behind the ML predictions and to navigate the opacity inherent in such models, a SHapley Additive exPlanations (SHAP) approach was invoked. Results: The predictive acumen of the LightGBM model was superior, evidenced by a striking accuracy of 96% and an AUC of 99.3%. Parallel to biostatistical assessments, SHAP analysis leveraging XAI illuminated that augmented expression levels of specific genes, namely TSPYL5, ATP5E, CA9, NUP210, SLC37A1, ARIH1, PSMD7, UBQLN1, PRAME, and UBE2T (with statistical significance p ≤ 0.05), correlated with an escalated risk of BC metastasis. Conversely, diminished expression of CACTIN, TGFB3, SCUBE2, ARL4D, OR1F1, ALDH4A1, PHF1, and CROCC (p ≤ 0.05) was similarly associated with heightened metastatic susceptibility in BC. Conclusions: The insights garnered from this investigation may catalyze preventative strategies against BC progression and metastatic dissemination, thereby enhancing therapeutic efficacy through personalized intervention modalities for BC patients.

Gut resistome of NSCLC patients treated with immunotherapy.

The newest method of treatment for patients with NSCLC (non-small cell lung cancer) is immunotherapy directed at the immune checkpoints PD-1 (Programmed Cell Death 1) and PD-L1 (Programmed Cell Death Ligand 1). PD-L1 is the only validated predictor factor for immunotherapy efficacy, but it is imperfect. Some patients do not benefit from immunotherapy and may develop primary or secondary resistance. This study aimed to assess the intestinal resistome composition of non-small cell lung cancer (NSCLC) patients treated with immune checkpoint inhibitors in the context of clinical features and potentially new prediction factors for assessing immunotherapy efficacy. The study included 30 advanced NSCLC patients, 19 (57%) men and 11 (33%) women treated with first- or second-line immunotherapy (nivolumab, pembrolizumab or atezolizumab). We evaluated the patient's gut resistome composition using the high sensitivity of targeted metagenomics. Studies have shown that resistome richness is associated with clinical and demographic factors of NSCLC patients treated with immunotherapy. Smoking seems to be associated with an increased abundance of macrolides, lincosamides, streptogramins and vancomycin core resistome. The resistome of patients with progression disease appears to be more abundant and diverse, with significantly higher levels of genomic markers of resistance to lincosamides (lnuC). The resistance genes lnuC, msrD, ermG, aph(6), fosA were correlated with progression-free survival or/and overall survival, thus may be considered as factors potentially impacting the disease. The results indicate that the intestinal resistome of NSCLC patients with immune checkpoint inhibitors treatment differs depending on the response to immunotherapy, with several distinguished markers. Since it might impact treatment efficacy, it must be examined more deeply.

Tau-mediated coupling between Pol III synthesis and DnaB helicase unwinding helps maintain genomic stability

The τ-subunit of the clamp loader complex (CLC) physically interacts with both the DnaB helicase and the polymerase III (Pol III) core α-subunit through Domains IV and V, respectively. This interaction is proposed to help maintain rapid and efficient DNA synthesis rates with high genomic fidelity and plasticity, facilitating enzymatic coupling within the replisome. To test this hypothesis, CRISPR-Cas9 editing was used to create site-directed genomic mutations within the dnaX gene at the C-terminus of τ predicted to interact with the α-subunit of Pol III. Perturbation of the α-τ binding interaction in vivo resulted in cellular and genomic stress markers that included reduced growth rates, fitness, and viabilities. Specifically, dnaX:mut strains showed increased cell filamentation, mutagenesis frequencies, and activated SOS. In situ fluorescence flow cytometry and microscopy quantified large increases in the amount of single-stranded DNA (ssDNA) gaps present. Removal of the C-terminus of τ (I618X) still maintained its interactions with DnaB and stimulated unwinding but lost its interaction with Pol III, resulting in significantly reduced rolling circle DNA synthesis. Intriguingly, dnaX:L635P/D636G had the largest induction of SOS, high mutagenesis, and the most prominent ssDNA gaps, which can be explained by an impaired ability to regulate the unwinding speed of DnaB resulting in a faster rate of in vitro rolling circle DNA replication, inducing replisome decoupling. Therefore, τ stimulated DnaB unwinding and physical coupling with Pol III acts to enforce replisome plasticity to maintain an efficient rate of synthesis and prevent genomic instability.

Using Copy Number Variation Data and Neural Networks to Predict Cancer Metastasis Origin Achieves High Area under the Curve Value with a Trade-Off in Precision.

The accurate identification of the primary tumor origin in metastatic cancer cases is crucial for guiding treatment decisions and improving patient outcomes. Copy number alterations (CNAs) and copy number variation (CNV) have emerged as valuable genomic markers for predicting the origin of metastases. However, current models that predict cancer type based on CNV or CNA suffer from low AUC values. To address this challenge, we employed a cutting-edge neural network approach utilizing a dataset comprising CNA profiles from twenty different cancer types. We developed two workflows: the first evaluated the performance of two deep neural networks-one ReLU-based and the other a 2D convolutional network. In the second workflow, we stratified cancer types based on anatomical and physiological classifications, constructing shallow neural networks to differentiate between cancer types within the same cluster. Both approaches demonstrated high AUC values, with deep neural networks achieving a precision of 60%, suggesting a mathematical relationship between CNV type, location, and cancer type. Our findings highlight the potential of using CNA/CNV to aid pathologists in accurately identifying cancer origins with accessible clinical tests.

Chronic Lymphocytic Leukemia: Prognostic Factors in the Era of Novel Drugs.

Novel drugs have profoundly changed the outcomes in chronic lymphocytic leukemia (CLL) patients, and the traditional prognostic factors that were identified in the era of chemoimmunotherapy need to be validated in the context of these new targeted therapies. Currently, the most important prognostic genetic biomarkers are the immunoglobulin heavy chain variable (IGHV) mutational status, genetic aberrations including del(17p)/TP53 abnormalities, and the complex karyotype. In this review, we discuss the prognostic role of these genomic markers in relation to novel treatments. Moreover, we present and discuss new scoring systems that were elaborated and validated in the era of new drugs. In routine clinical practice, the application of an extensive genomic work-up with validated prognostic markers could improve the identification of "very high-risk" CLL patients who could benefit from novel, more effective targeted treatments.

To be or not to be tetraploid-the impact of marker ploidy on genomic prediction and GWAS of potato.

Cultivated potato, Solanum tuberosum L., is considered an autotetraploid with 12 chromosomes with four homologous phases. However, recent evidence found that, due to frequent large phase deletions in the genome, gene ploidy is not constant across the genome. The elite cultivar "Otava" was found to have an average gene copy number of 3.2 across all loci. Breeding programs for elite potato cultivars rely increasingly on genomic prediction tools for selection breeding and elucidation of quantitative trait loci underpinning trait genetic variance. These are typically based on anonymous single nucleotide polymorphism (SNP) markers, which are usually called from, for example, SNP array or sequencing data using a tetraploid model. In this study, we analyzed the impact of using whole genome markers genotyped as either tetraploid or observed allele frequencies from genotype-by-sequencing data on single-trait additive genomic best linear unbiased prediction (GBLUP) genomic prediction (GP) models and single-marker regression genome-wide association studies of potato to evaluate the implications of capturing varying ploidy on the statistical models employed in genomic breeding. A panel of 762 offspring of a diallel cross of 18 parents of elite breeding material was used for modeling. These were genotyped by sequencing and phenotyped for five key performance traits: chipping quality, length/width ratio, senescence, dry matter content, and yield. We also estimated the read coverage required to confidently discriminate between a heterozygous triploid and tetraploid state from simulated data. It was found that using a tetraploid model neither impaired nor improved genomic predictions compared to using the observed allele frequencies that account for true marker ploidy. In genome-wide associations studies (GWAS), very minor variations of both signal amplitude and number of SNPs supporting both minor and major quantitative trait loci (QTLs) were observed between the two data sets. However, all major QTLs were reproducible using both data sets.

Genome-based identification of phosphate-solubilizing capacities of soil bacterial isolates

Identifying genomic markers for phosphate-solubilizing bacteria (PSB) is vital for advancing agricultural sustainability. This study utilizes whole-genome sequencing and comprehensive bioinformatics analysis, examining the genomes of 76 PSB strains with the aid of specialized genomic databases and analytical tools. We have identified the pqq gene cluster, particularly the pqqC gene, as a key marker for (P) solubilization capabilities. The pqqC gene encodes an enzyme that catalyzes the conversion of precursors to 2-keto-d-gluconic acid, which significantly enhances P solubilization in soil. This gene’s importance lies not only in its biochemical function but also in its prevalence and effectiveness across various PSB strains, distinguishing it from other potential markers. Our study focuses on Burkholderia cepacia 51-Y1415, known for its potent solubilization activity, and demonstrates a direct correlation between the abundance of the pqqC gene, the quantitative release of P, and the production of 2-keto-d-gluconic acid over a standard 144-h cultivation period under standardized conditions. This research not only underscores the role of the pqqC gene as a universal marker for the rapid screening and functional annotation of PSB strains but also highlights its implications for enhancing soil fertility and crop yields, thereby contributing to more sustainable agricultural practices. Our findings provide a foundation for future research aimed at developing targeted strategies to optimize phosphate solubilization, suggesting areas for further investigation such as the integration of these genomic insights into practical agricultural applications to maximize the effectiveness of PSB strains in real-world soil environments.

Sparse testing designs for optimizing predictive ability in sugarcane populations.

Sugarcane is a crucial crop for sugar and bioenergy production. Saccharose content and total weight are the two main key commercial traits that compose sugarcane's yield. These traits are under complex genetic control and their response patterns are influenced by the genotype-by-environment (G×E) interaction. An efficient breeding of sugarcane demands an accurate assessment of the genotype stability through multi-environment trials (METs), where genotypes are tested/evaluated across different environments. However, phenotyping all genotype-in-environment combinations is often impractical due to cost and limited availability of propagation-materials. This study introduces the sparse testing designs as a viable alternative, leveraging genomic information to predict unobserved combinations through genomic prediction models. This approach was applied to a dataset comprising 186 genotypes across six environments (6×186=1,116 phenotypes). Our study employed three predictive models, including environment, genotype, and genomic markers as main effects, as well as the G×E to predict saccharose accumulation (SA) and tons of cane per hectare (TCH). Calibration sets sizes varying between 72 (6.5%) to 186 (16.7%) of the total number of phenotypes were composed to predict the remaining 930 (83.3%). Additionally, we explored the optimal number of common genotypes across environments for G×E pattern prediction. Results demonstrate that maximum accuracy for SA ( ) and for TCH ( ) was achieved using in training sets few (3) to no common (0) genotype across environments maximizing the number of different genotypes that were tested only once. Significantly, we show that reducing phenotypic records for model calibration has minimal impact on predictive ability, with sets of 12 non-overlapped genotypes per environment (72=12×6) being the most convenient cost-benefit combination.

Using the Strategy of Genome-Wide Association Studies to Identify Genetic Markers of Suicidal Behavior: A Narrative Review.

Several studies involving various suicidal phenotypes based on the strategy of the search of genome-wide associations with single nucleotide polymorphisms have been performed recently. These studies need to be generalized. To systematize the findings of a number of genome-wide association studies (GWAS) for suicidal phenotypes, annotate the identified markers, analyze their functionality, and possibly substantiate the hypothesis holding that these phenotypes reflect a nonspecific set of gene variants that are relevant as relates to stress-vulnerability as a key endophenotype of suicidal behavior (SB). A search on the PubMed and related resources using the combinations "suicide AND GWAS" and "suicidal behavior AND GWAS" was performed. It yielded a total of 34 independent studies and meta-analyses. For the 10 years since such studies emerged, they have undergone significant progress. Estimates of the SNP heritability of SB in some cases are comparable with estimates of heritability based on the twin method. Many studies show a high genetic correlation with the genomic markers of the most common mental disorders (depression, bipolar disorder, schizophrenia, post-traumatic stress disorder). At the same time, a genomic architecture specific to SB is also encountered. Studies utilizing the GWAS strategy have not revealed any associations of SB with candidate genes that had been previously studied in detail (different neurotransmitters, stress response system, polyamines, etc.). Frequently reported findings from various studies belong in three main groups: 1) genes involved in cell interactions, neurogenesis, the development of brain structures, inflammation, and the immune responses; 2) genes encoding receptors for neurotrophins and various components of the intracellular signaling systems involved in synaptic plasticity, embryonic development, and carcinogenesis; and 3) genes encoding various neuro-specific proteins and regulators. In general, GWAS in the field of suicidology mainly serve the purpose of a deeper understanding of the pathophysiology of suicidal behavior. However, they also demonstrate growing capability in terms of predicting and preventing suicide, especially when calculating the polygenic risk score among certain populations (psychiatric patients) and in combination with tests of different modalities. From our point of view, there exists a set of markers revealed by the GWAS strategy that seems to point to a leading role played by stress vulnerability, an endophenotype that is formed during early development and which subsequently comes to play the role of key pathogenetic mechanism in SB.

Spatio-temporal clustering of African swine fever virus (Asfarviridae: Asfivirus) circulating in the Kaliningrad region based on three genome markers.

The rapid spread of African swine fever in the Kaliningrad region makes it necessary to use the methods of molecular epidemiology to determine the dynamics and direction of ASF spread in this region of Russia. The aim of the study was to determine single nucleotide polymorphisms within molecular markers K145R, O174L and MGF 505-5R of ASFVs isolated in Kaliningrad region and to study the circulating of the pathogen in European countries by subgenotyping and spatio-temporal clustering analysis. Blood samples from living domestic pigs and organs from dead domestic pigs and wild boars, collected in the Kaliningrad region between 2017 and 2022 were used. Virus isolation was carried out in porcine bone-marrow primary cell culture. Amplicons of genome markers were amplified by PCR with electrophoretic detection and subsequent extraction of fragments from agarose gel. Sequencing was performed using the Sanger method. The circulation of two genetic clusters of ASFV isolates on the territory of the Kaliningrad has been established: epidemic (K145R-III, MGF 505-5R-II, O174L-I - 94.3% of the studied isolates) and sporadic (K145R-II, MGF 505-5R-II, O174L-I - 5.7%). The broaden molecular genetic surveillance of ASFV isolates based on sequencing of genome markers is necessary in the countries of the Eurasian continent to perform a more detailed analysis of ASF spread between countries and within regions.

Radiogenomics of Intrahepatic Cholangiocarcinoma: Correlation of Imaging Features With BAP1 and FGFR Molecular Subtypes.

Clinical research has shown unique tumor behavioral characteristics of BRCA-associated protein-1- (BAP1-) and fibroblast growth factor receptor (FGFR)-mutated intrahepatic cholangiocarcinomas (CCAs), with BAP1-mutated tumors demonstrating more aggressive forms of disease and FGFR-altered CCAs showing more indolent behavior. We performed a retrospective case-control study to evaluate for unique imaging features associated with BAP1 and FGFR genomic markers in intrahepatic CCA (iCCA). Multiple imaging features of iCCA at first staging were analyzed by 2 abdominal radiologists blinded to genomic data. Growth and development of metastases at available follow-up imaging were also recorded, as were basic clinical cohort data. Types of iCCA analyzed included those with BAP1, FGFR, or both alterations, as well as cases with low mutational burden or mutations with low clinical impact, which served as a control or "wild-type" group. There were 18 cases in the FGFR group, 10 with BAP1 mutations, and 31 wild types (controls). Cases with BAP1 mutations showed significantly larger growth at first year of follow-up (P = 0.03) and more frequent tumor-associated biliary ductal dilatation (P = 0.04) compared with controls. FGFR-altered cases showed more infiltrative margins compared with controls (P = 0.047) and demonstrated less enhancement between arterial to portal venous phases (P = 0.02). BAP1 and FGFR groups had more cases with stage IV disease at presentation than controls (P = 0.025, P = 0.006). Compared with wild-type iCCAs, FGFR-mutated tumors often demonstrate infiltrative margins, and BAP1 tumors show increased biliary ductal dilatation at presentation. BAP1-mutated cases had significantly larger growth at first-year restaging.

Agarose-Resolvable SSR Markers Based on ddRADSeq in Chickpea

Exploitation of genetic diversity is essential for sustainable crop production. Molecular markers have potential to reach these goals in more rapid and efficient manner. Simple sequence repeats (SSRs) are one of the most popular molecular markers in breeding studies due to their co-dominant inheritance, abundance in the genome, multi-allelic and reproducible nature. Here, we developed genomic SSR markers from chickpea with the use of ddRADSeq data. 1,396 SSR regions with an average of 530 SSR/Mb in the whole genome were successfully identified. Considering different types of repeats, dinucleotides were the most frequent type accounting for 62.03% of the total SSR regions identified, followed by trinucleotides (25.50%) and tetranucleotides (4.58%). The AT/TA motif was greatly characterized among dinucleotide repeats, and it was also the most common type in the chickpea genome accounting for 36.5% of the total SSR regions identified, followed by AG/GA (139) and TC/CT (135) among dinucleotide motifs. Considering their genomic distribution and simple visualization on agarose gels, we examined SSR regions of 10 bp and longer for identification of SSR markers. A total of 10 SSR markers were successfully designed and resulted in successful polymorphic bands among chickpea genotypes. Consequently, the results show that ddRADSeq is effective for marker development and these markers might be valuable for biodiversity studies, marker-assisted selection (MAS) and linkage map construction in chickpea.

Computational approaches for clinical, genomic and proteomic markers of response to glucagon-like peptide-1 therapy in type-2 diabetes mellitus: An exploratory analysis with machine learning algorithms

IntroductionIn 2021, the International Diabetes Federation reported that 537 million people worldwide are living with diabetes. While glucagon-like peptide-1 agonists provide significant benefits in diabetes management, approximately 40% of patients do not respond well to this therapy. This study aims to enhance treatment outcomes by using machine learning to predict individual response status to glucagon-like peptide-1 therapy. MethodsWe analysed a type-2 diabetes mellitus dataset from the Diastrat cohort, recruited at the Northern Ireland Centre for Stratified Medicine. The dataset included individuals prescribed glucagon-like peptide-1 therapy, with response status determined by glycated haemoglobin levels of ≤53 mmol/mol. We identified genomic and proteomic markers and developed machine learning models to predict therapy response. ResultsThe study found 5 genomic variants and 45 proteomic markers that help differentiate glucagon-like peptide-1 therapy responders from non-responders, achieving 95 % prediction accuracy with a machine learning model. ConclusionThis study demonstrates the potential of machine learning in predicting the response to glucagon-like peptide-1 therapy in individuals with type-2 diabetes mellitus. These findings suggest that integrating genomic and proteomic data can significantly enhance personalized treatment approaches, potentially improving outcomes for patients who might otherwise not respond well to glucagon-like peptide-1 therapy. Further research and validation in larger cohorts are necessary to confirm these results and translate them into clinical practice.

Classification accuracy of machine learning algorithms for Chinese local cattle breeds using genomic markers.

Accurate breed classification is required for the conservation and utilization of farm animal genetic resources. Traditional classification methods mainly rely on phenotypic characterization. However, it is difficult to distinguish between the highly similar breeds due to the challenges in qualifying the phenotypic character. Machine learning algorithms show unique advantages in breed classification using genomic information. To evaluate the classification methods for Chinese cattle breeds, this study utilized genomic SNP data from 213 individuals across seven Chinese local breeds and compared the classification accuracies of three feature selection methods (FST value sorting and screening, mRMR, and Relief-F) and three machine learning algorithms (Random Forest, Support Vector Machine, and Naive Bayes). Results showed that: 1) using the FST method to screen more than 1500 SNPs, or using the mRMR algorithm to screen more than 1000 SNPs, the SVM classification algorithm can achieve more than 99.47% classification accuracy; 2) the most effective algorithm was SVM, followed by NB, while the best SNP selection method was FST and mRMR, followed by Relief-F; 3) species misclassification often occurs between breeds with high similarity. This study demonstrates that machine learning classification models combined with genomic data are effective methods for the classification of local cattle breeds, providing a technical basis for the rapid and accurate classification of cattle breeds in China.

Longitudinal risk prediction for pediatric glioma with temporal deep learning.

Pediatric glioma recurrence can cause morbidity and mortality; however, recurrence pattern and severity are heterogeneous and challenging to predict with established clinical and genomic markers. Resultingly, almost all children undergo frequent, long-term, magnetic resonance (MR) brain surveillance regardless of individual recurrence risk. Deep learning analysis of longitudinal MR may be an effective approach for improving individualized recurrence prediction in gliomas and other cancers but has thus far been infeasible with current frameworks. Here, we propose a self-supervised, deep learning approach to longitudinal medical imaging analysis, temporal learning, that models the spatiotemporal information from a patient's current and prior brain MRs to predict future recurrence. We apply temporal learning to pediatric glioma surveillance imaging for 715 patients (3,994 scans) from four distinct clinical settings. We find that longitudinal imaging analysis with temporal learning improves recurrence prediction performance by up to 41% compared to traditional approaches, with improvements in performance in both low- and high-grade glioma. We find that recurrence prediction accuracy increases incrementally with the number of historical scans available per patient. Temporal deep learning may enable point-of-care decision-support for pediatric brain tumors and be adaptable more broadly to patients with other cancers and chronic diseases undergoing surveillance imaging.

Predictors of response to immunotherapy in colorectal cancer.

Colorectal cancer (CRC) is a major cause of cancer-related deaths globally. While treatment advancements have improved survival rates, primarily through targeted therapies based on KRAS, NRAS, and BRAF mutations, personalized treatment strategies for CRC remain limited. Immunotherapy, mainly immune checkpoint blockade, has shown efficacy in various cancers but is effective in only a small subset of patients with CRC with deficient mismatch repair (dMMR) proteins or high microsatellite instability (MSI). Recent research has challenged the notion that CRC is immunologically inert, revealing subsets with high immunogenicity and diverse lymphocytic infiltration. Identifying precise biomarkers beyond dMMR and MSI is crucial to expanding immunotherapy benefits. Hence, exploration has extended to various biomarker sources, such as the tumor microenvironment, genomic markers, and gut microbiota. Recent studies have introduced a novel classification system, consensus molecular subtypes, that aids in identifying patients with CRC with an immunogenic profile. These findings underscore the necessity of moving beyond single biomarkers and toward a comprehensive understanding of the immunological landscape in CRC, facilitating the development of more effective, personalized therapies.

Long-term climatic stability drives accumulation and maintenance of divergent freshwater fish lineages in a temperate biodiversity hotspot

Anthropogenic climate change is forecast to drive regional climate disruption and instability across the globe. These impacts are likely to be exacerbated within biodiversity hotspots, both due to the greater potential for species loss but also to the possibility that endemic lineages might not have experienced significant climatic variation in the past, limiting their evolutionary potential to respond to rapid climate change. We assessed the role of climatic stability on the accumulation and persistence of lineages in an obligate freshwater fish group endemic to the southwest Western Australia (SWWA) biodiversity hotspot. Using 19,426 genomic (ddRAD-seq) markers and species distribution modelling, we explored the phylogeographic history of western (Nannoperca vittata) and little (Nannoperca pygmaea) pygmy perches, assessing population divergence and phylogenetic relationships, delimiting species and estimating changes in species distributions from the Pliocene to 2100. We identified two deep phylogroups comprising three divergent clusters, which showed no historical connectivity since the Pliocene. We conservatively suggest these represent three isolated species with additional intraspecific structure within one widespread species. All lineages showed long-term patterns of isolation and persistence owing to climatic stability but with significant range contractions likely under future climate change. Our results highlighted the role of climatic stability in allowing the persistence of isolated lineages in the SWWA. This biodiversity hotspot is under compounding threat from ongoing climate change and habitat modification, which may further threaten previously undetected cryptic diversity across the region.

Biochemical Markers for Neuroendocrine Tumors: Traditional Circulating Markers and Recent Development—A Comprehensive Review

Neuroendocrine neoplasms (NENs) are a heterogeneous group of neoplasms presenting unique challenges in diagnosis and management. Traditional markers such as chromogranin A (CgA), pancreatic polypeptide (PP), and neuron-specific enolase (NSE) have limitations in terms of specificity and sensitivity. Specific circulating markers such as serotonin and its metabolite 5-hydroxyindoleacetic acid (5-HIAA) and various gastrointestinal hormones such as gastrin, glucagon, somatostatin, and vasoactive intestinal peptide (VIP) have a role in identifying functional NENs. Recent advances in molecular and biochemical markers, also accounting for novel genomic and proteomic markers, have significantly improved the landscape for the diagnosis and monitoring of NENs. This review discusses these developments, focusing on both traditional markers such as CgA and NSE, as well as specific hormones like gastrin, insulin, somatostatin, glucagon, and VIP. Additionally, it covers emerging genomic and proteomic markers that are shaping current research. The clinical applicability of these markers is highlighted, and their role in improving diagnostic accuracy, predicting surgical outcomes, and monitoring response to treatment is demonstrated. The review also highlights the need for further research, including validation of these markers in larger studies, development of standardized assays, and integration with imaging techniques. The evolving field of biochemical markers holds promise for improving patient outcomes in the treatment of NENs, although challenges in standardization and validation remain.