Variation In Expression Research Articles

ELLA: Modeling Subcellular Spatial Variation of Gene Expression within Cells in High-Resolution Spatial Transcriptomics.

Spatial transcriptomic technologies are becoming increasingly high-resolution, enabling precise measurement of gene expression at the subcellular level. Here, we introduce a computational method called subcellular expression localization analysis (ELLA), for modeling the subcellular localization of mRNAs and detecting genes that display spatial variation within cells in high-resolution spatial transcriptomics. ELLA creates a unified cellular coordinate system to anchor diverse cell shapes and morphologies, utilizes a nonhomogeneous Poisson process to model spatial count data, leverages an expression gradient function to characterize subcellular expression patterns, and produces effective control of type I error and high statistical power. We illustrate the benefits of ELLA through comprehensive simulations and applications to four spatial transcriptomics datasets from distinct technologies, where ELLA not only identifies genes with distinct subcellular localization patterns but also associates these patterns with unique mRNA characteristics. Specifically, ELLA shows that genes enriched in the nucleus exhibit an abundance of long noncoding RNAs or protein-coding mRNAs, often characterized by longer gene lengths. Conversely, genes containing signal recognition peptides, encoding ribosomal proteins, or involved in membrane related activities tend to enrich in the cytoplasm or near the cellular membrane. Furthermore, ELLA reveals dynamic subcellular localization patterns during the cell cycle, with certain genes showing decreased nuclear enrichment in the G1 phase while others maintain their enrichment patterns throughout the cell cycle. Overall, ELLA represents a calibrated, powerful, robust, scalable, and versatile tool for modeling subcellular spatial expression variation across diverse high-resolution spatial transcriptomic platforms.

Coordinated, multicellular patterns of transcriptional variation that stratify patient cohorts are revealed by tensor decomposition.

Tissue-level and organism-level biological processes often involve the coordinated action of multiple distinct cell types. The recent application of single-cell assays to many individuals should enable the study of how donor-level variation in one cell type is linked to that in other cell types. Here we introduce a computational approach called single-cell interpretable tensor decomposition (scITD) to identify common axes of interindividual variation by considering joint expression variation across multiple cell types. scITD combines expression matrices from each cell type into a higher-order matrix and factorizes the result using the Tucker tensor decomposition. Applying scITD to single-cell RNA-sequencing data on 115 persons with lupus and 83 persons with coronavirus disease 2019, we identify patterns of coordinated cellular activity linked to disease severity and specific phenotypes, such as lupus nephritis. scITD results also implicate specific signaling pathways likely mediating coordination between cell types. Overall, scITD offers a tool for understanding the covariation of cell states across individuals, which can yield insights into the complex processes that define and stratify disease.

Transposon-mediated genic rearrangements underlie variation in small RNA pathways

Transposable elements (TEs) can alter host gene structure and expression, whereas host organisms develop mechanisms to repress TE activities. In the nematode Caenorhabditis elegans , a small interfering RNA pathway dependent on the helicase ERI-6/7 primarily silences retrotransposons and recent genes of likely viral origin. By studying gene expression variation among wild C. elegans strains, we found that structural variants and transposon remnants likely underlie expression variation in eri-6/7 and the pathway targets. We further found that multiple insertions of the DNA transposons, Polintons, reshuffled the eri-6/7 locus and induced inversion of eri-6 in some wild strains. In the inverted configuration, gene function was previously shown to be repaired by unusual trans-splicing mediated by direct repeats. We identified that these direct repeats originated from terminal inverted repeats of Polintons . Our findings highlight the role of host-transposon interactions in driving rapid host genome diversification among natural populations and shed light on evolutionary novelty in genes and splicing mechanisms.

Antimicrobial resistance profiling of bacteria isolated from wastewater and samples of pharmaceutical industries in South India

The study was aimed to determine the phenotypic and genotypic antimicrobial resistance in the isolated bacteria from the influent (25), effluent (15), surface and ground water samples (15) surrounding the pharmaceutical industries located in south India. From 55 samples, 48 isolates of 10 different bacteria were obtained. The identified bacterial isolates were viz. Klebsiella pneumoniae, Pseudomonas aeruginosa, Enterobacter aerogenes, Corynebacterium sp., Acinetobacter sp., Aeromonas punctata, Ralstonia picketti, Staphylococcus aureus, Stenotrophomonas maltophillia, and Citrobacter freundii. The phenotypic profile of resistance through antibiotic susceptibility test was carried out against sixteen different antibiotics. Standard PCR technique was used for the detection of 12 resistance genes encoding carbapenems, quinoline, aminoglycoside, β-lactam belonging blaOXA-58,blaOXA-22,qnrA, qnrB, aac(6)-Ib-cr, aac (3)-XI, mec A, qepA, aadB, blaVIM, blaOXA-48 and blaNDM. Pseudomonas aeruginosa (1: TN/I/2020) showed presence of 3 resistance genes. qnrB (489 bp) gene was present in maximum of 7 isolates while blaVIM (196 bp) gene was present in 6 isolates. The resistance genes blaNDM (621 bp) was present in three different isolates; aac (X):6)-lb-cr (482 bp), qepA (495 bp), aadB (500 bp), blaOXA-58 (843 bp) resistant genes were present in two different isolates each among the bacterial isolates obtained in this study. In phenotypic resistance profiling by AST method, out of 16 antibiotics tested, 14 showed resistance. Similarly, in genotypic resistance profiling, among 12 resistance genes tested, a maximum of three resistance genes were noticed in Pseudomonas aeruginosa. There were positive and negative correlations observed between phenotypic and genotypic resistance among different antibiotics and their resistance genes indicating the variations in the resistance gene expression.

Substrate-Mediated Regulation of Src Expression Drives Osteoclastogenesis Divergence.

Glass, bone, and dentin are commonly applied substrates for osteoclast cultures; however, the impact of these substrates on osteoclastogenesis remains underexplored. This study aimed to address a significant gap in understanding how different substrates influence the process of osteoclastogenesis. RAW 264.7 cells were cultured and induced with RANKL on glass, bone, and dentin slides. Histological and molecular techniques were used to identify patterns and differences in osteoclast behavior on each substrate. Osteoclasts cultured on glass slides possessed the greatest number of nuclei and the highest expression levels of ACP5 (TRAP) and CTSK, with osteoclasts on bone and dentin slides displaying progressively lower levels. Src expression was also most pronounced in osteoclasts on glass slides, with decreased levels observed on bone and dentin. This variation in Src expression likely contributed to differences in cytoskeletal remodeling and oxidative phosphorylation (OXPHOS), resulting in substrate-dependent divergences in osteoclastogenesis. Glass slides were the most favorable substrate for inducing osteoclastogenesis, while bone and dentin slides were less effective. The substrate-induced expression of Src played a fundamental role in shaping the phenotypic divergence of osteoclasts. These insights fill important knowledge gaps and have significant implications for the development and selection of in vitro models for bone-related diseases and drug screening platforms.

Identification of potential characteristic genes in chronic skin infections through RNA sequencing and immunohistochemical analysis.

The objective of the present study was to perform RNA sequencing and immunohistochemical analysis on skin specimens obtained from healthy individuals and individuals afflicted with prolonged skin infections. Bioinformatics methodologies were used to scrutinize the RNA sequencing data with the intention of pinpointing distinctive gene signatures associated with chronic skin infections. Skin tissue samples were collected from 11 individuals (4 subjects healthy and 7 patients with chronic skin infections) at the Affiliated Hospital of Southwest Medical University (Luzhou, China). The iDEP tool identified differentially expressed genes (DEGs) with log2 (fold change) ≥2 and q-value ≤0.01. Functional enrichment analysis using Gene Ontology and KEGG databases via the oebiotech online tool was then performed to determine the biological functions and pathways related to these DEGs. A protein-protein interaction network of DEGs identified HIF1A as a potential key gene. Subsequent immunohistochemistry analyses were performed on the samples to assess any variations in HIF1A expression. A total of 900 DEGs, 365 upregulated and 535 downregulated, were observed between the normal and chronic infection groups. The identified DEGs were found to serve a role in various biological processes, including 'hypoxia adaptation', 'angiogenesis', 'cell adhesion' and 'regulation of positive cell migration'. Additionally, these genes were revealed to be involved in the 'TGF-β', 'PI3K-Akt' and 'IL-17' signaling pathways. HIF1A and nine other genes were identified as central nodes in the PPI network. HIF1A expression was higher in chronically infected skin samples than in healthy samples, indicating its potential as a novel research target.

Characterizing m6A modification factors and their interactions in colorectal cancer: implications for tumor subtypes and clinical outcomes

BackgroundThe study aims to comprehensively combine colorectal cancer data cohorts in order to analyze the effects of various DNA methylation-coding genes on colorectal cancer patients. The annual incidence and mortality of colorectal cancer are very high, and there are no effective treatments for advanced colorectal cancer. DNA methylation is a method widely used to regulate epigenetics in the molecular mechanism study of tumors.MethodThree single-cell cohorts GSE166555, GSE146771, and EMTAB8107, and five transcriptome cohorts GSE17536, GSE39582, GSE72970, and TCGA-CRC (TCGA-COAD and TCGA-READ) were applied in this study. 2 erasers (ALKBH5 and FTO), There are 7 writers (METTL3, METTL14, WTAP, VIRMA, RBM15, RBM15B, and ZC3H13) and 11 readers (YTHDC1, IGF2BP1, IGF2BP2, IGF2BP3, YTHDF1, YTHDF3, YTHDC2, and HNRNPA2B1, YTHDF2, HNRNPC and RBMX), a total of 20 M6A regulators, were used as the basis of the dataset in this study and were applied to the construction of molecular typing and prognostic models. Drugs that are differentially sensitive in methylation-regulated gene-related prognostic models were identified using the ConsensusClusterPlus package, which was also used to identify distinct methylation regulatory expression patterns in colorectal cancer and to model the relationship between tissue gene expression profiles and drug IC50 values. Finally, TISCH2 assessed which immune cells were significantly expressed with M6A scores. The immunosuppression of M6A methylation is spatially explained.ResultsThis study used data from 583 CRC patients in the TCGA-CRC cohort. Firstly, the mutation frequency and CNV variation frequency of 20 m6A modification-related factors were analyzed, and the corresponding histogram and heat map were drawn. The study next analyzed the expression variations between mutant and wild forms of the VIRMA gene and explored differences in the expression of these variables in tumor and normal tissues. In addition, the samples were divided into different subgroups by molecular clustering method based on m6A modification, and each subgroup’s expression and clinicopathological characteristics were analyzed. Finally, we compared prognostic differences, tumor microenvironment (TME) characteristics, immune cell infiltration, and gene function enrichment among different subpopulations. We also developed a colorectal cancer m6A-associated gene signature and validated its prognostic effects across multiple cohorts. Finally, using single-cell RNA sequencing data, we confirmed that tumor cells show elevated expression of m6A-related gene signatures.DiscussionThis study explored the mutation frequency, expression differences, interactions, molecular clustering, prognostic effect, and association with tumor characteristics of m6A modification-related factors in CRC and validated them at the single-cell level. These results clarify the association between m6A alteration and colorectal cancer (CRC) and offer important insights into the molecular recognition and management of cancer.

GsQTL: Associating genetic risk variants with gene sets by exploiting their shared variability.

To investigate the functional significance of genetic risk loci identified through genome-wide association studies (GWASs), genetic loci are linked to genes based on their capacity to account for variation in gene expression, resulting in expression quantitative trait loci (eQTL). Following this, gene set analyses are commonly used to gain insights into functionality. However, the efficacy of this approach is hampered by small effect sizes and the burden of multiple testing. We propose an alternative approach: instead of examining the cumulative associations of individual genes within a gene set, we consider the collective variation of the entire gene set. We introduce the concept of gene set QTL (gsQTL), and show it to be more adept at identifying links between genetic risk variants and specific gene sets. Notably, gsQTL experiences less susceptibility to inflation or deflation of significant enrichments compared with conventional methods. Furthermore, we demonstrate the broader applicability of shared variability within gene sets. This is evident in scenarios such as the coordinated regulation of genes by a transcription factor or coordinated differential expression.

Dorsal root ganglion inflammation by oxaliplatin toxicity: DPEP1 as possible target for peripheral neuropathy prevention

BackgroundPeripheral neuropathy (PN) constitutes a dose-limiting side effect of oxaliplatin chemotherapy that often compromises the efficacy of antineoplastic treatments. Sensory neurons damage in dorsal root ganglia (DRG) are the cellular substrate of PN complex molecular origin. Dehydropeptidase-1 (DPEP1) inhibitors have shown to avoid platin-induced nephrotoxicity without compromising its anticancer efficiency. The objective of this study was to describe DPEP1 expression in rat DRG in health and in early stages of oxaliplatin toxicity. To this end, we produced and characterized anti-DPEP1 polyclonal antibodies and used them to define the expression, and cellular and subcellular localization of DPEP1 by immunohistochemical confocal microscopy studies in healthy controls and short term (six days) oxaliplatin treated rats.ResultsDPEP1 is expressed mostly in neurons and in glia, and to a lesser extent in endothelial cells. Rats undergoing oxaliplatin treatment developed allodynia. TNF-\U0001d6fc expression in DRG revealed a pattern of focal and at different intensity levels of neural cell inflammatory damage, accompanied by slight variations in DPEP1 expression in endothelial cells and in nuclei of neurons.ConclusionsDPEP1 is expressed in neurons, glia and endothelial cells of DRG. Oxaliplatin caused allodynia in rats and increased TNF-α expression in DRG neurons. The expression of DPEP1 in neurons and other cells of DRG suggest this protein as a novel strategic molecular target in the prevention of oxaliplatin-induced acute neurotoxicity.

Abstract PR-14: Meta-analysis of spatial transcriptomics data from pancreatic cancer precursors proposes a common molecular framework for PanIN and IPMN

Abstract INTRODUCTION Pancreatic ductal adenocarcinoma (PDAC) arises from either pancreatic intraepithelial neoplasia (PanIN) or intraductal papillary mucinous neoplasm (IPMN). Transcriptomic studies of PanIN and IPMN conducted in isolation both aim to improve early detection, risk stratification, and therapeutic target selection. Here, we curated existing and new whole transcriptome digital spatial profiles (DSP-RNA) from normal to malignant pancreatic epithelium to map the molecular landscape of PDAC progenitors. METHODS New NanoString GeoMx DSP-RNA datasets were generated from 1) surgical IPMN specimens resected at Duke University and 2) deceased donor pancreas specimens collected by the Gift of Life Michigan Donor Care Center that could not be used for transplantation and thus allocated to the University of Michigan for approved research. Published studies were downloaded from GEO (GSE229752, GSE226829, GSE199102) and the NanoString Spatial Organ Atlas. Epithelial areas of interest (AOIs) were processed and analyzed with stgeomx (https://github.com/mkiyer/stgeomx) and R/Bioconductor packages. Quality control (QC) procedures removed low quality AOIs. Batch effects were mitigated by defining highly variable genes within each dataset and merging them into a common set for downstream analysis. Molecular subtype gene signatures were obtained from the pdacR package and quantified using Gene Set Variation Analysis. RESULTS We assembled a pancreatic epithelial DSP-RNA cohort consisting of 768 AOIs from 62 patients. AOIs were annotated as normal duct (n=193), acini (n=53), acinar-ductal metaplasia (ADM) (n=57), PanIN (n=108), IPMN (n=92), and PDAC (n=265). Principal component analysis (PCA) revealed compelling associations between DSP-RNA profiles and the established classical, basal-like, and exocrine molecular subtypes of PDAC. The first principal component (PC1) stratified the exocrine and basal-like PDAC subtypes on opposite ends of its axis (PC1 vs exocrine Pearson r=0.929, p < 0.001, PC1 vs basal-like Pearson r=-0.775, padj < 0.001). PC2 further distinguished classical from basal-like AOIs, and PC3 separated normal (CRISP3+) from neoplastic (MUC5AC+) ductal epithelium. Unsupervised clustering of AOIs proposed four epithelial subtypes: “acinar/ADM” (cAcinar), “normal duct” (cDuct), “low-risk neoplasm” (cLR), and “high-risk/malignant” (cHR). PanIN and IPMN lesions exhibited remarkably similar global gene expression patterns, suggesting that they arise from a common lineage. CONCLUSIONS Meta-analysis of pancreatic epithelial DSP-RNA datasets provided a unifying molecular context for PanIN, IPMN, and PDAC. The established classical, basal-like, and exocrine PDAC subtypes explained global patterns of gene expression variation. Intriguingly, PanIN and IPMN transcriptomes appeared similar, suggesting that early detection and therapeutic selection strategies may be broadly applicable to PDAC precursors. Citation Format: Matthew K Iyer, Ashley Fletcher, Elishama Kanu, Chanjuan Shi, Rosina Carr, Ahmed M. Elhossiny, Daniel P. Nussbaum, Eileen S. Carpenter, Marina Pasca di Magliano, Arul M. Chinnaiyan, Peter J. Allen, Timothy L. Frankel. Meta-analysis of spatial transcriptomics data from pancreatic cancer precursors proposes a common molecular framework for PanIN and IPMN [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research; 2024 Sep 15-18; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl_2):Abstract nr PR-14.

199 Awardee Talk: Insights into translational regulation through tRNA sequencing, RNA sequencing, and ribosome profiling

Abstract Translation acts as an additional layer of regulation that has an important role in gene expression and function. Due to a phenomenon called codon usage bias, highly expressed genes are thought to be codon-biased to support efficient translation. As more than one codon can code for the same amino acid (synonymous codons), organisms may exhibit preferences for specific codons that facilitate increased expression of important genes due to variations in the availability of corresponding tRNAs. Advances in sequencing technologies have recently permitted the study of the translatome, which refers to the entire population of mRNA associated with ribosomes for protein synthesis and can be investigated through ribosome profiling. This cutting-edge technique allows the identification of actively translated regions, but can also reveal translational pausing events that stem from the presence of SNPs. Our previous research has demonstrated variation in tRNA expression across tissues and different states of health, leading us to consider the connection between tRNA abundance and translational stalling due to SNPs. By coupling ribosome profiling with tRNA sequencing and RNA sequencing, we have investigated all elements of translational machinery to predict translational efficiency, estimate proteome composition, and evaluate tRNA abundance as a source of genetic variation. In this work, we utilized ribosome profiling, tRNAseq, and RNAseq and performed an integrative analysis in bovine tissues (kidney, liver and muscle) as well as murine myoblast cell lines (C2C12). By applying these methods to different bovine tissues, we were able to explore the interplay between tRNA availability and translational stalling events. Moreover, we have identified translationally regulated genes underlying tissue-specific biological processes and found that many upregulated and downregulated genes coincided with high and low translational efficiency respectively. We have also successfully defined stalling sites that depict the regulatory information encoded within the coding sequence of transcripts, which could control translation rate and facilitate proper protein folding. Through the implementation of these next generation sequencing (NGS) methods to cell culture, we were able to evaluate the translatome at various time points (0-min, 30-min, 60-min, and 4-h) post-induction of differentiation. Where most studies have focused on molecular changes between differentiating myoblasts and multinucleated myotubes that are present 7 d after differentiation, we focus on the earliest stages of muscle differentiation that initiate muscle development and therefore kickstart the process of meat production. This work offers an atlas of distinctive stalling sites across bovine tissues and various stages of muscle differentiation, which provides an opportunity to predict codon optimality and understand tissue-specific or time-specific mechanisms of regulating protein synthesis.

Shining new light on naupliar eyes: A novel molecular phylogeny for Pleuromamma (Family: Metridinidae) and the characterization of luciferase and opsin expression

Pleuromamma (Giesbrecht, 1898) is a cosmopolitan genus of metridinid copepods, with species that perform remarkable diel vertical migrations (DVM) and emit a bioluminescent secretion when disturbed that varies both spectrally and kinetically. Copepod bioluminescence is autogenic and uses luciferase enzymes that catalyze a luciferin, coelenterazine, to produce light. Pleuromamma possess naupliar eyes, relatively simple photosensitive structures used for many visually-guided behaviors. Yet the fundamental molecular unit for vision, the opsin protein, has not been previously described for the family. The light producers and detectors are important to study because DVM is a behavior that mediates significant active elemental fluxes between the upper ocean and midwaters across vast stretches of oceanic habitat, and DVM is guided by visual behaviors, with animals tracking an isolume. Here we provide the first fully resolved molecular phylogeny for Pleuromamma (Family: Metridinidae) and describe the luciferase and opsin gene diversity and expression using de novo assembled transcriptomes. We successfully sequenced and assembled transcriptomes for 10 of 11 described species of Pleuromamma as well as two other metridinid species: Metridia longa and Gaussia princeps. In all species, we obtained coding sequences of one putative rhabdomeric middle wavelength sensitive visual opsin gene, as well as several non-visual opsins – a c-type pteropsin and a tetra-opsin type peropsin. Furthermore, Pleuromamma express luciferases from each of two main evolutionary clades (Luc1 and Luc2), and a single paralog (Luc2a) dominates expression throughout the group. The variation in luciferase number, sequence, and expression among species could lead to different spectral and kinetic properties of bioluminescence and aid in congener recognition.

SEMbap: Bow-free covariance search and data de-correlation.

Large-scale studies of gene expression are commonly influenced by biological and technical sources of expression variation, including batch effects, sample characteristics, and environmental impacts. Learning the causal relationships between observable variables may be challenging in the presence of unobserved confounders. Furthermore, many high-dimensional regression techniques may perform worse. In fact, controlling for unobserved confounding variables is essential, and many deconfounding methods have been suggested for application in a variety of situations. The main contribution of this article is the development of a two-stage deconfounding procedure based on Bow-free Acyclic Paths (BAP) search developed into the framework of Structural Equation Models (SEM), called SEMbap(). In the first stage, an exhaustive search of missing edges with significant covariance is performed via Shipley d-separation tests; then, in the second stage, a Constrained Gaussian Graphical Model (CGGM) is fitted or a low dimensional representation of bow-free edges structure is obtained via Graph Laplacian Principal Component Analysis (gLPCA). We compare four popular deconfounding methods to BAP search approach with applications on simulated and observed expression data. In the former, different structures of the hidden covariance matrix have been replicated. Compared to existing methods, BAP search algorithm is able to correctly identify hidden confounding whilst controlling false positive rate and achieving good fitting and perturbation metrics.

Genome-Wide Identification and Expression Analysis of the Cys2His2 Zinc Finger Protein Gene Family in Flammulina filiformis.

Zinc finger proteins (ZFPs) are essential transcription factors in eukaryotes, particularly the extensively studied C2H2 family, which is known for its involvement in various biological processes. This research provides a thorough examination and analysis of the C2H2-ZFP gene family in Flammulina filiformis. Using bioinformatics tools, 58 FfC2H2-ZFP genes spread across 11 chromosomes were identified and scrutinized in detail for their gene structures, protein characteristics, and phylogenetic relationships. The study of phylogenetics and synteny sheds light on the evolutionary relationships among C2H2-ZFPs in F. filiformis and other fungi, revealing a complex evolutionary past. The identification of conserved cis-regulatory elements in the gene promoter regions suggests intricate functionalities, particularly in the developmental and stress response pathways. By utilizing RNA-seq and qRT-PCR techniques, the expression patterns of these genes were explored across different developmental stages and tissues of F. filiformis, unveiling distinct expression profiles. Notably, significant expression variations were observed in the stipe elongation region and pilei of various sizes, indicating potential roles in fruiting body morphogenesis. This study enhances our knowledge of the C2H2-ZFP gene family in F. filiformis and lays the groundwork for future investigations into their regulatory mechanisms and applications in fungal biology and biotechnology.

Dense SNP-based analyses complement forensic anthropology biogeographical ancestry assessments

Identification of unidentified human remains (UHRs) is crucial yet challenging, especially with traditional forensic techniques. Forensic anthropological examinations can yield ancestry estimations; however, the utility of these estimates is limited by the data points that can be collected from partial remains, complexities of admixture, and variation of phenotypic expression due to environmental effects. While it is generally known that anthropological estimates can be imprecise, the performance of these methods has not been studied at scale. Genome-wide SNP testing is an orthogonal approach for estimating ancestry and offers a unique opportunity to measure the magnitude of anthropological ancestry misattribution. Genomic ancestry inference leverages principal component analysis (PCA) and model-based clustering approaches. This study compares anthropologically determined ancestry with those estimated using genome-wide SNP markers. A dataset of 611 UHR samples with publicly available ancestry assessments from National Missing and Unidentified Persons System (NamUs) was analyzed. The genetic ancestry approach, validated against reference population samples, offers robust ancestry calculations for major population groups. Inconsistency between anthropological and genomic ancestry assignments were observed, particularly for admixed populations. Although forensic anthropological examinations remain valuable, their limitations emphasize the need for refinement and enhancement through the augmentation of SNP-based analyses. Further validation studies are crucial to define the uncertainty associated with both anthropological and genome-based ancestry estimates to resolve cases and aid law enforcement investigations. Additionally, current policy and practices for reporting ancestry for UHRs should be revisited to reduce potential misinformation.

Integrating large-scale single-cell RNA sequencing in central nervous system disease using self-supervised contrastive learning

The central nervous system (CNS) comprises a diverse range of brain cell types with distinct functions and gene expression profiles. Although single-cell RNA sequencing (scRNA-seq) provides new insights into the brain cell atlases, integrating large-scale CNS scRNA-seq data still encounters challenges due to the complexity and heterogeneity among CNS cell types/subtypes. In this study, we introduce a self-supervised contrastive learning method, called scCM, for integrating large-scale CNS scRNA-seq data. scCM brings functionally related cells close together while simultaneously pushing apart dissimilar cells by comparing the variations of gene expression, effectively revealing the heterogeneous relationships within the CNS cell types/subtypes. The effectiveness of scCM is evaluated on 20 CNS datasets covering 4 species and 10 CNS diseases. Leveraging these strengths, we successfully integrate the collected human CNS datasets into a large-scale reference to annotate cell types and subtypes in neural tissues. Results demonstrate that scCM provides an accurate annotation, along with rich spatial information of cell state. In summary, scCM is a robust and promising method for integrating large-scale CNS scRNA-seq data, enabling researchers to gain insights into the cellular and molecular mechanisms underlying CNS functions and diseases.

Where the "ruber" Meets the Road: Using the Genome of the Red Diamond Rattlesnake to Unravel the Evolutionary Processes Driving Venom Evolution.

Understanding the proximate and ultimate causes of phenotypic variation is fundamental in evolutionary research, as such variation provides the substrate for selection to act upon. Although trait variation can arise due to selection, the importance of neutral processes is sometimes understudied. We presented the first reference-quality genome of the Red Diamond Rattlesnake (Crotalus ruber) and used range-wide 'omic data to estimate the degree to which neutral and adaptive evolutionary processes shaped venom evolution. We characterized population structure and found substantial genetic differentiation across two populations, each with distinct demographic histories. We identified significant differentiation in venom expression across age classes with substantially reduced but discernible differentiation across populations. We then used conditional redundancy analysis to test whether venom expression variation was best predicted by neutral divergence patterns or geographically variable (a)biotic factors. Snake size was the most significant predictor of venom variation, with environment, prey availability, and neutral sequence variation also identified as significant factors, though to a lesser degree. By directly including neutrality in the model, our results confidently highlight the predominant, yet not singular, role of life history in shaping venom evolution.

Study on Sex Determination and Variation in Gender Expression of Individual Trees in a Population of Cedrusdeodara (Roxb.) G. Don. From Dense Temperate Forests of Northern India

Conifers are reported to exhibit predominantly monoecious behaviour however, numerous species and some genera show uncertainties regarding their gender expression. The factors influencing the sexual differentiation of strobili in monoecious or dioecious conifers remain poorly understood. To investigate this unpredictable phenomenon in conifers, we selected three populations representing pure stands of Cedrusdeodara in dense temperate forests of Northern India, specifically one in Uttarakhand and two in Himachal Pradesh. Each site was surveyed, and total 900 trees were marked as male, female, monoecious and neutral trees based on their reproductive behaviour and sexual representation. Selection criteria were based on the reproductive age of Cedrusdeodara, as it attains maturity when it reaches a height of 19 to 20 meters. Our findings revealed that Cedrusdeodara exhibits subdioecious behaviour, characterized by the occurrence of four basic sex forms such as male trees, female trees, monoecious trees, and neutral trees. Yearly observations from 2014 to 2016 unveiled that Cedrusdeodara does not exhibit consistent reproductive behaviour. Instead, the species displays a fascinating pattern of alternation between dioecy and monoecy. Additionally, it was also found that individual trees demonstrated change in their expression of sex during each reproductive cycle. These findings underscore the complexity of sex determination and reproductive plasticity in Cedrusdeodara. The study has revealed that the monoecious behaviour was more dominant than the dioecious behaviour, and the individual tree changes its sexual representation depending upon the rate of seed production the previous year. This research pave the way for future investigations into the factors influencing sex expression and reproductive behaviour in conifers and will contribute to our broader knowledge of plant sexuality and plant evolution.

Distinct Variations in Gene Expression and Cell Composition across Lichen Planus Subtypes.

Lichen planus (LP) is a highly prevalent inflammatory skin disease. While various clinical subtypes have been defined, detailed comparisons of these variants are lacking. This study aimed to elucidate differences in gene expression and cellular composition across LP subtypes. Lesional skin biopsies from 28 LP patients (classical, oral, genital, and lichen planopilaris) and seven non-diseased skin controls (NDC) were analyzed. Gene expression profiling of 730 inflammation-related genes was conducted using NanoString. Immune cell compositions were assessed by multiplex immunohistochemistry. Gene expression profiles revealed unique inflammatory signatures for each LP subtype. Lichen planopilaris exhibited the most divergence, with downregulated gene expression and upregulation of complement pathway genes (C5-7), along with elevated M2 macrophages. Oral and genital LP demonstrated similar profiles with strong upregulation of TNF-related and Toll-like receptor-associated genes. Oral LP showed the highest upregulation of cytotoxicity-associated genes, as well as high numbers of CD8+ IL-17A+ (Tc17) cells (8.02%). Interferon gene signatures were strongly upregulated in oral and classical LP. The study highlights distinct differences in inflammatory gene expression and cell composition across LP subtypes, emphasizing the need for tailored therapeutic approaches.

Seasonal Variation in TLR4 Expression in The Testis and Epididymis of Anatolian Ground Squirrels (Spermophilus xanthoprymnus): Insights from Non-Breeding Period of Pre-Hibernation and Hibernation

This study investigates the modulation of Toll-like receptor 4 (TLR4) expression within the testis and epididymis of Anatolian ground squirrels (Spermophilus xanthoprymnus) during the non-breeding period of pre-hibernation and hibernation. Immunohistochemical investigation showed that TLR4 was not detected in germ cells, Leydig cells, or Sertoli cells in the testicular tissue during the pre-hibernation period. Nevertheless, there was a presence of TLR4 in the vessels walls and certain interstitial cells within the intertubular regions. Epithelial cells in the caput, corpus, and cauda regions of the epididymis showed no TLR4 expression. However, it was observed in the vessel walls, smooth muscle layers, and some interstitial cells. TLR4 expression was seen in spermatogonia and primary spermatocytes during hibernation, with strong labeling observed in the vessel walls of the intertubular area. In contrast, TLR4 was detected in the epididymal epithelium, as well as in the smooth muscle layers and vessel walls throughout all segments. A notable upregulation in the expression of TLR4 in the testis was identified through quantitative image analysis during hibernation as compared to pre-hibernation. During pre-hibernation, the cauda segment of the epididymis exhibited the highest expression of TLR4, whereas during hibernation, the corpus segment demonstrated the highest expression. These findings suggest a dynamic modulation of TLR4 in response to hibernation, highlighting its potential role in reproductive function and immune adaptation.