Sequence Variation Research Articles

Genome-wide association study and candidate gene identification for the cold tolerance at the seedling stage of rapeseed (Brassica napus L.)

Rapeseed (Brassica napus L.) is an important overwintering oilseed crop and suffers from severe cold stress during the seedling stage due to the increasingly delayed sowing in the Yangtze River Basin. However, the genetic basis underlying cold tolerance in rapeseed seedlings is not well understood. In this study, we observed the cold tolerance of 217 rapeseed accessions in the field and found a significant negative correlation between cold tolerance grades and malondialdehyde (MDA) contents. A genome-wide association study (GWAS) of cold tolerance grades Identified four significant loci in the genomic region of one MYB transcription factor BnaA8.MYB60. Furthermore, field accessions with BnaA8.MYB60Hap1 exhibited significantly higher cold tolerance and lower expression of BnaA8.MYB60 compared to the majority of accessions with BnaA8.MYB60Hap2. These results suggested that variations in the genomic sequences of BnaA8.MYB60 caused the divergence of gene expression levels and functions on cold tolerance in rapeseed seedlings. This study could provide the theoretical guidance for the breeding of cold-tolerant rapeseed varieties.

Study of metal sequenced spray impregnation method towards Co-Mo/γ-Al2O3 catalytic performance in hydrotreating of used coconut oil to liquid biohydrocarbon

A series of bifunctional catalyst CoMo on γ-Al2O3 have been successfully prepared using the spray impregnation (dry impregnation), with the variation in the metal sequence (Co-Mo and Mo-Co) and metal impregnation time gap (1-h, 24-h, and simultaneous). XRF indicated that the developed method causes a minimal amount of metal loss during the impregnation process. XRD shows that 24-h treatment reduces crystallinity compared to the 1-h and simultaneous catalysts. Co and Mo existed as CoAl2O4, CoO, and MoO3 as reported by XANES and XPS. The preparation technique signified the catalyst's acidity and surface area. The Co-Mo/γ-Al2O3 24H has a high surface area (118.32 m2 g−1), with a pore volume of 0.22 cm3 g−1, and a pore diameter of 7.48 nm. It has a high proportion of weak acid sites (41.32 %) which is crucial to perform HDO reactions. Co-Mo/γ-Al2O3 24H showed high catalytic activity (40.32 wt% liquid product) and selectivity (85.61 % of biogasoline and 9.75 % of diesel fractions) from the hydrotreatment. Kimi@UGM2010.

Genotypic Analysis of ABCA4 Coding Sequence in Thai Patients with Stargardt Disease

Objective: To study the mutational spectrum of the ABCA4 gene in Thai patients with Stargardt disease. Materials and Methods: DNA sequencing of all 50 exons of the ABCA4 gene was performed in nine Thai patients with clinically diagnosed Stargardt disease. Results: Amino acid sequence variation in the ABCA4 gene was found in five patients. Six missense mutations, c.71G>A, c.635G>A, c.1268A>G, c.3626T>C, c.4283C>T, and c.5761G>A, previously associated with Stargardt disease, were identified in our cohort. The variant c.1268A>G was the most prevalent in our study. Conclusion: In this cohort, only 56% of Thai Stargardt patients had missense mutations in the ABCA4 gene. Mutations in the non-coding regions of the ABCA4 or mutations in other genes may be responsible for Stargardt phenotypes in the remaining patients. Our findings are the first to reveal the mutational spectrum of ABCA4 leading to Stargardt disease in the Thai population and demonstrate a potential for ABCA4 screening as well as the importance of genetic variability in Thai patients with clinically suspected Stargardt disease.

Shotgun metagenomic analysis reveals the diversity of PHA producer bacterial community and PHA synthase gene in Addis Ababa municipal solid waste disposal area ‘Qoshe’

BackgroundPolyhydroxyalkanoates (PHAs) are naturally produced biopolymers with significant scientific and biotechnological potential. This study aimed to investigate the diversity of the PHA-producing bacterial community and PhaC genes in soil samples collected from a municipal solid waste disposal site known as “Qoshe” in Addis Ababa, Ethiopia, using a shotgun metagenomics approach. The SqueezeMeta pipeline was used to analyze the microbial community in the waste samples. A CD search against the TIGRFAM protein family database was performed to identify the complete-length multidomain sequences of PhaC genes and classify them into their respective classes. Statistical analysis and data visualization were performed using RStudio with R version 4.2.3.ResultsThe findings of this study suggest that known and unknown taxa likely contribute to the phaC genes of municipal solid waste. Taxonomic profiling of the metagenomic data revealed that the majority of the PHA-producing taxa belonged to the phylum Proteobacteria (80%), followed by Actinomycetota (16.5%). Furthermore, this study identified Thiomonas and unclassified Mycobacterium as the main contributors to class I PhaC genes. Class II PhaC genes are predominantly associated with the Pseudomonadaceae family, followed by unclassified Hyphomicrobials and Acidimicrobiales. Class III PhaC genes are abundantly related to the Methylococcaceae family, specifically the Methylocaldum genus. The analysis of PhaC gene sequences revealed high level of diversity, with a significant proportion of putative PhaC genes exhibiting low sequence identity with each other and PhaC gene in the database. Notably, the sequence variation observed within the same PhaC gene classes suggests the potential presence of previously unidentified PhaC gene variants.ConclusionsOverall, this research improves our understanding of the diversity of PHA-producing taxa and PhaC genes in municipal solid waste environments, providing opportunities for sustainable PHA production and waste management strategies. However, additional studies, including the isolation and characterization of specific strains, are necessary to confirm the PHA production capabilities of these strains and explore their biotechnological potential.

At the core of salinity: Divergent transcriptomic responses to neutral and alkaline salinity in Arabidopsis thaliana

In the context of current climate change, alkaline salinity is increasingly challenging crop yields, especially in arid and semiarid regions. Alkaline salinity is more detrimental to plant performance than neutral salinity and tolerance to neutral salinity may not confer tolerance to alkaline salinity. The mechanisms behind are still poorly understood. This study aims to identify physiological and genetic traits underlying this differential tolerance to neutral and alkaline salinity by exploiting the variation present in natural populations (demes) of Arabidopsis thaliana. Growth, photosynthesis, phytohormone and mineral nutrient profiles, plant water status and transcriptomic changes were analyzed in four demes with contrasting tolerance to neutral and alkaline salinity. Results of this novel holistic approach suggest low internal Fe use efficiency caused by bicarbonate as a driver of enhanced sensitivity to alkaline salinity in plants adapted to neutral salinity prompting photosynthesis inhibition and alteration of the plant’s carbon budget for primary and secondary metabolism. Moreover, alkaline salinity specifically altered the auxin and jasmonic acid signaling pathways, while sustained ABA biosynthesis was an adaptive trait under neutral salinity. Exploring the genes with non-shared expression trends between salinity types, we identified sequence variation at the BGAL4 locus associated with advantageous responses to each type of salinity. Weighted correlation network analysis (WGCNA) validated the significant involvement of gene co-expression modules targeted by the enrichment analyses, highlighting the hubs correlated with favorable responses to both salinity types. Overall, the present study points out the complex physiological and genetic mechanisms responsible for plant tolerance to alkaline salinity and proposes target genes for breeding strategies under alkaline saline soils.

Female fertility and the mammalian egg's zona pellucida.

All mammalian eggs are surrounded by a relatively thick extracellular matrix (ECM) or zona pellucida (ZP) to which free-swimming sperm bind in a species-restricted manner during fertilization. The ZP consists of either three (e.g., Mus musculus) or four (e.g., Homo sapiens) glycosylated proteins, called ZP1-4. These proteins are unlike those found in somatic cell ECM, are encoded by single-copy genes on different chromosomes, and are well conserved among different mammals. Mammalian ZP proteins are synthesized as polypeptide precursors by growing oocytes that will become ovulated, unfertilized eggs. These precursors are processed to remove a signal-sequence and carboxy-terminal propeptide and are secreted into the extracellular space. Secreted ZP proteins assemble into long, crosslinked fibrils that exhibit a structural repeat due to the presence of ZP2-ZP3 dimers every 140 Å or so along fibrils. Fibrils are crosslinked by ZP1 and are oriented either perpendicular, parallel, or randomly to the plasma membrane of eggs depending on their position in the ZP. Free-swimming mouse sperm recognize and bind to ZP2 or ZP3 that serve as sperm receptors. Acrosome-intact sperm bind to ZP3 oligosaccharides and acrosome-reacted sperm bind to ZP2 polypeptide. ZP fibrils fail to assemble in the absence of either nascent ZP2 or ZP3 and results in mouse eggs that lack a ZP and female infertility. Gene sequence variations due to point, missense, or frameshift mutations in genes encoding ZP1-4 result in human eggs that lack a ZP or have an abnormal ZP and female infertility. These and other features of the mouse and human egg's ZP are discussed here.

Characterizing age-related changes in intact mitochondrial proteoforms in murine hearts using quantitative top-down proteomics

BackgroundCardiovascular diseases (CVDs) are the leading cause of death worldwide, and the prevalence of CVDs increases markedly with age. Due to the high energetic demand, the heart is highly sensitive to mitochondrial dysfunction. The complexity of the cardiac mitochondrial proteome hinders the development of effective strategies that target mitochondrial dysfunction in CVDs. Mammalian mitochondria are composed of over 1000 proteins, most of which can undergo post-translational modifications (PTMs). Top-down proteomics is a powerful technique for characterizing and quantifying proteoform sequence variations and PTMs. However, there are still knowledge gaps in the study of age-related mitochondrial proteoform changes using this technique. In this study, we used top-down proteomics to identify intact mitochondrial proteoforms in young and old hearts and determined changes in protein abundance and PTMs in cardiac aging.MethodsIntact mitochondria were isolated from the hearts of young (4-month-old) and old (24-25-month-old) mice. The mitochondria were lysed, and mitochondrial lysates were subjected to denaturation, reduction, and alkylation. For quantitative top-down analysis, there were 12 runs in total arising from 3 biological replicates in two conditions, with technical duplicates for each sample. The collected top-down datasets were deconvoluted and quantified, and then the proteoforms were identified.ResultsFrom a total of 12 LC-MS/MS runs, we identified 134 unique mitochondrial proteins in the different sub-mitochondrial compartments (OMM, IMS, IMM, matrix). 823 unique proteoforms in different mass ranges were identified. Compared to cardiac mitochondria of young mice, 7 proteoforms exhibited increased abundance and 13 proteoforms exhibited decreased abundance in cardiac mitochondria of old mice. Our analysis also detected PTMs of mitochondrial proteoforms, including N-terminal acetylation, lysine succinylation, lysine acetylation, oxidation, and phosphorylation. Data are available via ProteomeXchange with the identifier PXD051505.ConclusionBy combining mitochondrial protein enrichment using mitochondrial fractionation with quantitative top-down analysis using ultrahigh-pressure liquid chromatography (UPLC)-MS and label-free quantitation, we successfully identified and quantified intact proteoforms in the complex mitochondrial proteome. Using this approach, we detected age-related changes in abundance and PTMs of mitochondrial proteoforms in the heart.

Assessment of Gut Microbiome Variations in the Mother and Twin Infant of Captive White-handed Gibbons (Hylobates lar) Reveals the Presence of Beneficial and Pathogenic Bacteria

The white-handed gibbon (Hylobates lar) is one of the three Hylobatidae species found in the Peninsular Malaysia region. Studies on primate gut microbiota have yet to be conducted in Malaysian Hylobatidae, but previous studies have covered Cercopithecidae. Knowledge of the gut microbiota of endangered captive Malaysian Hylobatidae primates is important for health assessment and conservation management. Thus, the main objective of this study was to determine the gut microbiota profile of captive white-handed gibbons using the metabarcode 16S rRNA gene. Two H. lar fecal samples collected from the mother and twin infant at Zoo Melaka were used for DNA extraction. Next-generation sequencing (NGS) of the 16S rRNA gene was performed. The NGS data were analyzed on the basis of amplicon sequence variation. The gut microbiota of H. lar was dominated by Firmicutes and Bacteroidota because of their important roles in fermentation and nutrient assimilation from plant-based food sources. Predominance of Prevotella in the gut microbiota of H. lar indicates that the captivity condition causes the gut microbiota of gibbons to resemble that of humans. Treponema, a pathogenic bacterium, was also detected in the mother and twin infant. Variation in the bacterial community profile between the mother and the infant indicated differences in dietary adaptations and physiological state. This study provides an overview of gibbon health levels through gut microbiome screening and can be used when considering Malaysian primate health welfare and captive management.

Predictive Model Building for Aggregation Kinetics Based on Molecular Dynamics Simulations of an Antibody Fragment.

Computational methods including machine learning and molecular dynamics simulations have strong potential to characterize, understand, and ultimately predict the properties of proteins relevant to their stability and function as therapeutics. Such methods would streamline the development pathway by minimizing the current experimental testing required for many protein variants and formulations. The molecular understanding of thermostability and aggregation propensity has advanced significantly along with predictive algorithms based on the sequence-level or structural-level information on a protein. However, these approaches focus largely on a comparison of protein sequence variations to correlate the properties of proteins to their stability, solubility, and aggregation propensity. For therapeutic protein development, it is of equal importance to take into account the impact of the formulation conditions to elucidate and predict the stability of the antibody drugs. At the macroscopic level, changing temperature, pH, ionic strength, and the addition of excipients can significantly alter the kinetics of protein aggregation. The mechanisms controlling aggregation kinetics have been traced back to a combination of molecular features, including conformational stability, partial unfolding to aggregation-prone states, and the colloidal stability governed by surface charges and hydrophobicity. However, very little has been done to evaluate these features in the context of protein dynamics in different formulations. In this work, we have combined a range of molecular features calculated from the Fab A33 protein sequence and molecular dynamics simulations. Using the power of advanced, yet interpretable, statistical tools, it has been possible to uncover greater insights into the mechanisms behind protein stability, validating previous findings, and also develop models that can predict the aggregation kinetics within a range of 49 different solution conditions.

A novel SNP within the Rsa10025320 gene is highly associated with hollowness in red-skinned radish fleshy roots.

Hollowness is a physiological disorder that frequently occurs during the growth and postharvest storage phases of fleshy radish roots, significantly diminishing quality, yield, and marketability. However, the molecular mechanism for hollowness remains elusive. To identify the QTLs and potential candidate genes for hollowness tolerance in radish, F2 and BC1 populations were constructed from hollowness-tolerant radish (C16) and hollowness-sensitive radish (C17) in the present study. Genetic analysis indicated that hollowness tolerance may be governed by two independent recessive genes. By employing bulked segregant analysis sequencing (BSA-seq), two significant candidate genomic intervals were pinpointed on chromosomes R04 (960kb, 6.48-7.44Mb) and R05 (600kb, 31.44-32.04Mb), which together harbor 107 annotated genes. Transcriptomic sequencing revealed that the downregulated differentially expressed genes (DEGs) were significantly enriched in biological processes related to cell death and the response to water stress, whereas the upregulated DEGs were significantly associated with the chitin catabolic process and the cell wall macromolecule metabolic process. A total of 46 intersecting genes were identified among these DEGs within the genomic intervals of interest. One gene with high expression (Rsa10025345) and two with low expression (Rsa10025320 and Rsa10018106) were detected in the tolerant variety C16. Furthermore, a SNP within Rsa10025320 resulting in an amino acid change (A188E) was characterized through sequence variation observed in both BSA-seq and RNA-seq data and further developed as a derived cleaved amplified polymorphic sequence (dCAPS) marker. Our study reveals potential target genes for tolerance to hollowness and paves the way for marker-assisted breeding of hollowness tolerance in red-skinned radishes.

A large structural variant collection in Holstein cattle and associated database for variant discovery, characterization, and application

BackgroundStructural variants (SVs) such as deletions, duplications, and insertions are known to contribute to phenotypic variation but remain challenging to identify and genotype. A more complete, accessible, and assessable collection of SVs will assist efforts to study SV function in cattle and to incorporate SV genotyping into animal evaluation.ResultsIn this work we produced a large and deeply characterized collection of SVs in Holstein cattle using two popular SV callers (Manta and Smoove) and publicly available Illumina whole-genome sequence (WGS) read sets from 310 samples (290 male, 20 female, mean 20X coverage). Manta and Smoove identified 31 K and 68 K SVs, respectively. In total the SVs cover 5% (Manta) and 6% (Smoove) of the reference genome, in contrast to the 1% impacted by SNPs and indels. SV genotypes from each caller were confirmed to accurately recapitulate animal relationships estimated using WGS SNP genotypes from the same dataset, with Manta genotypes outperforming Smoove, and deletions outperforming duplications. To support efforts to link the SVs to phenotypic variation, overlapping and tag SNPs were identified for each SV, using genotype sets extracted from the WGS results corresponding to two bovine SNP chips (BovineSNP50 and BovineHD). 9% (Manta) and 11% (Smoove) of the SVs were found to have overlapping BovineHD panel SNPs, while 21% (Manta) and 9% (Smoove) have BovineHD panel tag SNPs. A custom interactive database (https://svdb-dc.pslab.ca) containing the identified sequence variants with extensive annotations, gene feature information, and BAM file content for all SVs was created to enable the evaluation and prioritization of SVs for further study. Illustrative examples involving the genes POPDC3, ORM1, G2E3, FANCI, TFB1M, FOXC2, N4BP2, GSTA3, and COPA show how this resource can be used to find well-supported genic SVs, determine SV breakpoints, design genotyping approaches, and identify processed pseudogenes masquerading as deletions.ConclusionsThe resources developed through this study can be used to explore sequence variation in Holstein cattle and to develop strategies for studying SVs of interest. The lack of overlapping and tag SNPs from commonly used SNP chips for most of the SVs suggests that other genotyping approaches will be needed (for example direct genotyping) to understand their potential contributions to phenotype. The included SV genotype assessments point to challenges in characterizing SVs, especially duplications, using short-read data and support ongoing efforts to better characterize cattle genomes through long-read sequencing. Lastly, the identification of previously known functional SVs and additional CDS-overlapping SVs supports the phenotypic relevance of this dataset.

Complete mitochondrial genome assembly of Juglans regia unveiled its molecular characteristics, genome evolution, and phylogenetic implications

BackgroundThe Persian walnut (Juglans regia), an economically vital species within the Juglandaceae family, has seen its mitochondrial genome sequenced and assembled in the current study using advanced Illumina and Nanopore sequencing technology.ResultsThe 1,007,576 bp mitogenome of J. regia consisted of three circular chromosomes with a 44.52% GC content encoding 39 PCGs, 47 tRNA, and five rRNA genes. Extensive repetitive sequences, including 320 SSRs, 512 interspersed, and 83 tandem repeats, were identified, contributing to genomic complexity. The protein-coding sequences (PCGs) favored A/T-ending codons, and the codon usage bias was primarily shaped by selective pressure. Intracellular gene transfer occurred among the mitogenome, chloroplast, and nuclear genomes. Comparative genomic analysis unveiled abundant structure and sequence variation among J. regia and related species. The results of selective pressure analysis indicated that most PCGs underwent purifying selection, whereas the atp4 and ccmB genes had experienced positive selection between many species pairs. In addition, the phylogenetic examination, grounded in mitochondrial genome data, precisely delineated the evolutionary and taxonomic relationships of J. regia and its relatives. We identified a total of 539 RNA editing sites, among which 288 were corroborated by transcriptome sequencing data. Furthermore, expression profiling under temperature stress highlighted the complex regulation pattern of 28 differently expressed PCGs, wherein NADH dehydrogenase and ATP synthase genes might be critical in the mitochondria response to cold stress.ConclusionsOur results provided valuable molecular resources for understanding the genetic characteristics of J. regia and offered novel perspectives for population genetics and evolutionary studies in Juglans and related woody species.

Identification and fine mapping of a QTL-rich region for yield-and quality-related traits on chromosome 4BS in common wheat (Triticum aestivum L.).

Yield and quality are important for plant breeding. To better understand the genetic basis underlying yield- and quality-related traits in wheat (Triticum aestivum L.), we conducted the quantitative trait locus (QTL) analysis using recombinant inbred lines (RILs) and a high-density genetic linkage map with a 90K array. In this study, a total of 117 QTLs were detected for spike number per area (SNPA), thousand grain weight (TGW), grain number per spike (GNS), plant height (PH), spike length (SL), total spikelet number (TSN), spikelet density (SD), grain protein content (GPC), and grain starch content (GSC). Among these QTLs, 30 environmentally stable QTLs for yield- and quality-related traits were detected. Notably, five QTL-rich regions (Qrr) for yield-and/or quality-related traits were identified, including the QTL-rich region on chromosome 4BS (QQrr.cau-4B) for eight traits (SNPA, GNS, PH, SL, TSN, SD, GPC, and GSC). The stable QTL-rich region QQrr.cau-4B was delimited into a physical interval of approximately 2.47Mb. Based on the annotation information of the Chinese spring wheat genome v1.0 and parental re-sequencing results, the interval included twelve genes with sequence variations. Taken together, these results contribute to further understanding of the genetic basis of SNPA, GNS, PH, SL, TSN, SD, GPC, and GSC, and fine mapping of QQrr.cau-4B will be beneficial for gene cloning and marker-assisted selection in the genetic improvement of wheat varieties.

Examining the Effects of Environment, Geography, and Elevation on Patterns of DNA Methylation Across Populations of Two Widespread Bumble Bee Species.

Understanding the myriad avenues through which spatial and environmental factors shape evolution is a major focus in biological research. From a molecular perspective, much work has been focused on genomic sequence variation; however, recently there has been increased interest in how epigenetic variation may be shaped by different variables across the landscape. DNA methylation has been of particular interest given that it is dynamic and can alter gene expression, potentially offering a path for a rapid response to environmental change. We utilized whole genome enzymatic methyl sequencing to evaluate the distribution of CpG methylation across the genome and to analyze patterns of spatial and environmental association in the methylomes of two broadly distributed montane bumble bees (Bombus vancouverensis Cresson and Bombus vosnesenskii Radoszkowski) across elevational gradients in the western US. Methylation patterns in both species are similar at the genomic scale with ∼1% of CpGs being methylated and most methylation being found in exons. At the landscape scale, neither species exhibited strong spatial or population structuring in patterns of methylation, although some weak relationships between methylation and distance or environmental variables were detected. Differential methylation analysis suggests a stronger environment association in B. vancouverensis given the larger number of differentially methylated CpG's compared to B. vosnesenskii. We also observed only a handful of genes with both differentially methylated CpGs and previously detected environmentally associated outlier SNPs. Overall results reveal a weak but present pattern in variation in methylation over the landscape in both species.

Enhancing the Cytotoxicity and Apoptotic Efficacy of Parasporin-2-Derived Variants (Mpp46Aa1) on Cancer Cell Lines.

Parasporin PS2Aa1, recently renamed Mpp46Aa1, is an anti-cancer protein known for its selectivity against various human cancer cell lines. We genetically modified native PS2Aa1 to create a library of approximately 100 mutants. From this library, we selected promising mutants based on their half-maximal inhibitory concentration (IC50) and sequence variations. In this study, Variant 3-35, with the G257V substitution, demonstrated increased cytotoxicity and selectivity against the colon cancer cell line SW480. Conversely, Variant N65, featuring substitutions N92D, K175R, and S218G, yielded the most favorable results against the cancer cell lines SW-620, MOLT-4, and Jurkat. The caspase 3/7 and 9, Annexin V-Cy3 and 6-GFDA activities, and, most notably, mitochondrial membrane permeabilization assays confirmed the apoptotic marker elevation. These findings indicate that residues 92, 175, 218, and 257 may play a critical role in the cytotoxic activity and selectivity. We successfully obtained genetically improved variants with substitutions at these key amino acid positions. Additionally, we conducted molecular dynamic simulations to explore the potential interactions between PS2Aa1 and the CD59 GPI-anchored protein. The simulation results revealed that residues 57, 92, and 101 were consistently present, suggesting their possible significance in the interactions between parasporin and the CD59 protein.

Characterisation of Ovine KRTAP19-3 and Its Impact on Wool Traits in Chinese Tan Sheep.

Wool, a natural fibre derived from sheep, can present a challenge to wool processing and manufacturing industries because of the variation in fibre traits. Genetic improvement offers one solution to this challenge, and having a better understanding of the genes that affect wool fibre traits is therefore important. Here, we describe ovine KRTAP19-3, a new member of the KAP19 gene family. Phylogenetic analysis revealed its relationship to other known KRTAP19 gene sequences, and an analysis of the nucleotide sequence variation in KRTAP19-3 from 288 sheep of a variety of breeds revealed six unique variant sequences. Among these variants, eleven single nucleotide polymorphisms (SNPs) were detected, with six located in the coding region. Three of these coding region SNPs were non-synonymous and would result in amino acid changes. Associations were observed between the presence of specific sequence variants in Chinese Tan sheep and wool trait variation, particularly an increase in fibre diameter variability in the heterotypic hair fibres. These findings enhance our understanding of the genes that encode sheep wool proteins.

Identification and characterization of QSFS.sau-MC-5A for sterile florets genetically independent of fertile ones per spike in wheat.

A major and stable QTL for sterile florets per spike and sterile florets per spikelet was identified, it was mapped within a 2.22-Mb interval on chromosome 5AL, and the locus was validated using two segregating populations with different genetic backgrounds. Both the number of fertile florets per spike (FFS) and the number of sterile florets per spike (SFS) significantly influence the final yield of wheat (Triticum aestivum L.), and a trade-off theoretically exists between them. To enhance crop yield, wheat breeders have historically concentrated on easily measurable traits such as FFS, spikelets per spike, and spike length. Other traits of agronomic importance, including SFS and sterile florets per spikelet (SFPs), have been largely overlooked. In the study, reported here, genetic bases of SFS and SFPs were investigated based on the assessment of a population of recombinant inbred lines (RILs) population. The RIL population was developed by crossing a spontaneous mutant with higher SFS (msf) with the cultivar Chuannong 16. A total of 10 quantitative trait loci (QTL) were identified, with QSFS.sau-MC-5A for SFS and QSFPs.sau-MC-5A for SFPs being the major and stable ones, and they were co-located on the long arm of chromosome 5A. The locus was located within a 2.22-Mb interval, and it was further validated in two additional populations based on a tightly linked Kompetitive Allele-Specific PCR (KASP) marker, K_sau_5A_691403852. Expression differences and promoter sequence variations were observed between the parents for both TraesCS5A03G1247300 and TraesCS5A03G1250300. The locus of QSFS.sau-MC-5A/QSFPs.sau-MC-5A showed a significantly positive correlation with spike length, florets in the middle spikelet, and total florets per spike, but it showed no correlation with either kernel number per spike (KNS) or kernel weight per spike. Appropriate nitrogen fertilizer application led to reduced SFS and increased KNS, supporting results from previous reports on the positive effect of nitrogen fertilizer on wheat spike and floret development. Based on these results, we propose a promising approach for breeding wheat cultivars with multiple fertile florets per spike, which could increase the number of kernels per spike and potentially improve yield. Collectively, these findings will facilitate further fine mapping of QSFS.sau-MC-5A/QSFPs.sau-MC-5A and be instrumental in strategies to increase KNS, thereby enhancing wheat yield.

Prediction and design of transcriptional repressor domains with large-scale mutational scans and deep learning.

Regulatory proteins have evolved diverse repressor domains (RDs) to enable precise context-specific repression of transcription. However, our understanding of how sequence variation impacts the functional activity of RDs is limited. To address this gap, we generated a high-throughput mutational scanning dataset measuring the repressor activity of 115,000 variant sequences spanning more than 50 RDs in human cells. We identified thousands of clinical variants with loss or gain of repressor function, including TWIST1 HLH variants associated with Saethre-Chotzen syndrome and MECP2 domain variants associated with Rett syndrome. We also leveraged these data to annotate short linear interacting motifs (SLiMs) that are critical for repression in disordered RDs. Then, we designed a deep learning model called TENet ( T ranscriptional E ffector Net work) that integrates sequence, structure and biochemical representations of sequence variants to accurately predict repressor activity. We systematically tested generalization within and across domains with varying homology using the mutational scanning dataset. Finally, we employed TENet within a directed evolution sequence editing framework to tune the activity of both structured and disordered RDs and experimentally test thousands of designs. Our work highlights critical considerations for future dataset design and model training strategies to improve functional variant prioritization and precision design of synthetic regulatory proteins.

Scalable and unsupervised discovery from raw sequencing reads using SPLASH2.

We introduce SPLASH2, a fast, scalable implementation of SPLASH based on an efficient k-mer counting approach for regulated sequence variation detection in massive datasets from a wide range of sequencing technologies and biological contexts. We demonstrate biological discovery by SPLASH2 in single-cell RNA sequencing (RNA-seq) data and in bulk RNA-seq data from the Cancer Cell Line Encyclopedia, including unannotated alternative splicing in cancer transcriptomes and sensitive detection of circular RNA.

Cancer detection with various classification models: A comprehensive feature analysis using HMM to extract a nucleotide pattern

This work presents a novel feature extraction method for identifying complex patterns in genomic sequences by employing the Hidden Markov Model (HMM). In this study, we use HMM to identify gene nucleotide patterns that are specific to malignant and non-malignant cells. Crucial genetic components DNA and RNA are involved in many biological processes that impact both healthy and malignant cells. Early patient identification is essential to successful cancer diagnosis and therapy. Varying nucleotide patterns indicate different cellular responses, which are important to understanding the molecular causes of cancer and associated disorders. We present a detailed study of nucleotide patterns in whole raw nucleotide sequences with variations in both protein sequence (CDS) and non-protein sequence (NCDS) in both malignant and non-malignant cells. Nucleotide prediction has been achieved while computational expenses are reduced by using the proposed HMM for feature extraction and selection. The classification models implemented in this work for cancer detection are Gradient-Boosted Decision Trees (GBDT), Random Forests (RF), Decision Trees (DT), and Support Vector Machines (SVM) with kernels. The suggested classification model's accuracy and 10-fold cross-validation have been validated via comprehensive case studies. The results reveal that DT and ensemble learning techniques significantly differentiate between malignant and non-malignant DNA sequences. SVM with suitable kernels improves cancer detection accuracy significantly. Combining feature reduction approaches with nucleotide pattern classifiers based on Hidden Markov models improves performance and ensures reliable cancer detection.