Genomic dissection of the correlation between milk yield and various health traits using functional and evolutionary information about imputed sequence variants of 34,497 German Holstein cows.

BACKGROUND: Over the last decades, it was subject of many studies to investigate the genomic connection of milk production and health traits in dairy cattle. Thereby, incorporating functional information in genomic analyses has been shown to improve the understanding of biological and molecular mechanisms shaping complex traits and the accuracies of genomic prediction, especially in small populations and across-breed settings. Still, little is known about the contribution of different functional and evolutionary genome partitioning subsets to milk production and dairy health. Thus, we performed a uni- and a bivariate analysis of milk yield (MY) and eight health traits using a set of ~34,497 German Holstein cows with 50K chip genotypes and ~17 million imputed sequence variants divided into 27 subsets depending on their functional and evolutionary annotation. In the bivariate analysis, eight trait-combinations were observed that contrasted MY with each health trait. Two genomic relationship matrices (GRM) were included, one consisting of the 50K chip variants and one consisting of each set of subset variants, to obtain subset heritabilities and genetic correlations. In addition, 50K chip heritabilities and genetic correlations were estimated applying merely the 50K GRM. RESULTS: In general, 50K chip heritabilities were larger than the subset heritabilities. The largest heritabilities were found for MY, which was 0.4358 for the 50K and 0.2757 for the subset heritabilities. Whereas all 50K genetic correlations were negative, subset genetic correlations were both, positive and negative (ranging from -0.9324 between MY and mastitis to 0.6662 between MY and digital dermatitis). The subsets containing variants which were annotated as noncoding related, splice sites, untranslated regions, metabolic quantitative trait loci, and young variants ranked highest in terms of their contribution to the traits` genetic variance. We were able to show that linkage disequilibrium between subset variants and adjacent variants did not cause these subsets` high effect. CONCLUSION: Our results confirm the connection of milk production and health traits in dairy cattle via the animals` metabolic state. In addition, they highlight the potential of including functional information in genomic analyses, which helps to dissect the extent and direction of the observed traits` connection in more detail.

Sequence variants in the BTD underlying biotinidase deficiency in families of Pakistani origin.

BACKGROUND: Biotinidase deficiency (BTD) is a rare autosomal recessive metabolic disease, which develops neurological symptoms because of the impaired biotin recycling. Pathogenic mutations on BTD gene cause BTD deficiency. The clinical features and mutation analysis of Pakistani children with BTD deficiency have rarely been described. Herein, for the first time, we report the clinical features, BTD gene mutations and biochemical analysis of seven symptomatic children with BTD deficiency from Pakistan. METHODS: Seven suspected BTD-deficient patients who presented abnormal organic acid profiles and clinical features were subjected to Sanger sequencing to identify pathogenic mutations in the BTD gene. The results were analyzed by Mutation Surveyor Software. RESULTS: All seven patients exhibited common biotinidase deficiency symptoms including hypotonia, developmental delay and seizures. Biochemical analysis shows marked excretion of 3-hydroxy isovalerate in all cases, followed by 3-hydroxy propionate and methyl citrate. Sanger sequencing revealed one frame-shift mutation, c.98_104delinsTCC (p.Cys33Phefs), and two missense mutations, c.1612C>A (p.Arg538Ser) and c.1330G>C (p.Asp444His). All mutations were in the homozygous state and classified as pathogenic in published studies and mutation databases. CONCLUSIONS: This study has validated the BTD variants as the underlying cause of biotinidase deficiency in which molecular testing of BTD is supported by urinary organic acid analysis and clinical diagnosis. Secondly, the strength of the local availability of this test in Pakistan will paved the way for the neonatal screening of biotinidase deficiency.

Community metabarcoding reveals the relative role of environmental filtering and spatial processes in metacommunity dynamics of soil microarthropods across a mosaic of montane forests.

Disentangling the relative role of environmental filtering and spatial processes in driving metacommunity structure across mountainous regions remains challenging, as the way we quantify spatial connectivity in topographically and environmentally heterogeneous landscapes can influence our perception of which process predominates. More empirical data sets are required to account for taxon- and context-dependency, but relevant research in understudied areas is often compromised by the taxonomic impediment. Here we used haplotype-level community DNA metabarcoding, enabled by stringent filtering of amplicon sequence variants (ASVs), to characterize metacommunity structure of soil microarthropod assemblages across a mosaic of five forest habitats on the Troodos mountain range in Cyprus. We found similar ß diversity patterns at ASV and species (OTU, operational taxonomic unit) levels, which pointed to a primary role of habitat filtering resulting in the existence of largely distinct metacommunities linked to different forest types. Within-habitat turnover was correlated to topoclimatic heterogeneity, again emphasizing the role of environmental filtering. However, when integrating landscape matrix information for the highly fragmented Quercus alnifolia habitat, we also detected a major role of spatial isolation determined by patch connectivity, indicating that stochastic and niche-based processes synergistically govern community assembly. Alpha diversity patterns varied between ASV and OTU levels, with OTU richness decreasing with elevation and ASV richness following a longitudinal gradient, potentially reflecting a decline of genetic diversity eastwards due to historical pressures. Our study demonstrates the utility of haplotype-level community metabarcoding for characterizing metacommunity structure of complex assemblages and improving our understanding of biodiversity dynamics across mountainous landscapes worldwide.

The microbiome of the pelagic tunicate Dolioletta gegenbauri: A potential link between the grazing and microbial food web.

Bloom-forming gelatinous zooplankton occur circumglobally and significantly influence the structure of pelagic marine food webs and biogeochemical cycling through interactions with microbial communities. During bloom conditions especially, gelatinous zooplankton are keystone taxa that help determine the fate of primary production, nutrient remineralization, and carbon export. Using the pelagic tunicate Dolioletta gegenbauri as a model system for gelatinous zooplankton, we carried out a laboratory-based feeding experiment to investigate the potential ecosystem impacts of doliolid gut microbiomes and microbial communities associated with doliolid faecal pellets and the surrounding seawater. Metabarcoding targeting Bacteria and Archaea 16S rRNA genes/Archaea) and qPCR approaches were used to characterize microbiome assemblages. Comparison between sample types revealed distinct patterns in microbial diversity and biomass that were replicable across experiments. These observations support the hypothesis that through their presence and trophic activity, doliolids influence the structure of pelagic food webs and biogeochemical cycling in subtropical continental shelf systems where tunicate blooms are common. Bacteria associated with starved doliolids (representative of the resident gut microbiome) possessed distinct low-biomass and low-diversity microbial assemblages, suggesting that the doliolid microbiome is optimized to support a detrital trophic mode. Bacterial genera Pseudoalteromomas and Shimia were the most abundant potential core microbiome taxa, similar to patterns observed in other marine invertebrates. Exploratory bioinformatic analyses of predicted functional genes suggest that doliolids, via their interactions with bacterial communities, may affect important biogeochemical processes including nitrogen, sulphur, and organic matter cycling.

Association between sequence variants in the FSHR gene and reproductive outcomes following IVF in predicted normoresponders.

RESEARCH QUESTION: Is there an association between FSHR sequence variants and reproductive outcomes following IVF in predicted normoresponders? DESIGN: Multicentre prospective cohort study conducted from November 2016 to June 2019 in Vietnam, Belgium and Spain including patients aged <38 years, and undergoing IVF with a predicted normal response with fixed-dose 150 IU rFSH in an antagonist protocol. Genotyping was performed for three FSHR (c.919A>G, c.2039A>G, c.-29G>A) and one FSHB sequence variants (c.-211G>T). Clinical pregnancy rate (CPR), live birth rate (LBR) and miscarriage rate in the first embryo transfer and cumulative live birth rate (CLBR) were compared between the different genotypes. RESULTS: A total of 351 patients underwent at least one embryo transfer. Genetic model analysis that adjusted for patient age, body mass index, ethnicity, type of embryo transfer, embryo stage and number of top-quality embryos transferred revealed a higher CPR for homozygous patients for the variant allele G of c.919A>G when compared to patients with genotype AA (60.3% versus 46.3%, adjusted odds ratio [ORadj] 1.96, 95% confidence interval [CI] 1.09-3.53). Also, c.919A>G genotypes AG and GG presented a higher CPR and LBR when compared with genotype AA (59.1% versus 46.3%, ORadj 1.80, 95% CI 1.08-3.00, and 51.3% versus 39.0%, ORadj 1.69, 95% CI 1.01-2.80, respectively). Cox regression models revealed a statistically significantly lower CLBR for c.2039A>G genotype GG in the codominant model (hazard ratio [HR] 0.66, 95% CI 0.43-0.99). CONCLUSION: These results demonstrate a previously unreported association between variant c.919A>G genotype GG and higher CPR and LBR in infertile patients and reinforce a potential role for genetic background in predicting the reproductive prognosis following IVF.

A Spike-Control Approach that Evaluates High Resolution Mass Spectrometry-Based Sequence Variant Analytical Method Performance for Therapeutic Proteins.

An amino acid sequence variant (SV) is defined as an unintended amino acid substitution in protein drug products. SVs contribute to product heterogeneity and can potentially impact product quality, safety, immunogenicity, and efficacy. The analysis of biotherapeutics for SVs is important throughout the product life cycle including clone selection, development of nutrient feed strategies, commercial manufacturing process, and post-approval changes to monitor product quality. The proposed analytical procedure for SVs consists of both qualitative (identification of SVs) and quantitative (quantitation of identified SVs) components. The complexities of SV analysis and the variety of current procedures highlight the need for a systematic approach for assessing the capability of these methodologies to reliably identify and quantitate SVs in biotherapeutics. We described here a "spike-control" approach for evaluating SV analytical procedure. The concept was adopted from quality control samples routinely used in analytical procedure validation. One FDA approved monoclonal antibody (mAb) was spiked with accurate amounts of highly homologous mAb to create mAb samples containing low yet accurate levels of "artificial" SVs. Spike-control samples were denatured, reduced, alkylated, digested and then analyzed by high resolution Orbitrap mass spectrometry. In silico analysis revealed four single amino acid differences between the two mAbs that could be used to represent SVs in the spike-control samples. All four "artificial" SVs were reliably identified by the current workflow. Analytical range (0.01% to 2%), accuracy and precision of identified SVs have also been evaluated. Overall, spike-control sample(s) helped to demonstrate that the SV analytical procedure (i.e., sample preparation, LC separation, mass spectrometry determinations and bioinformatic software) was fit for purpose and suitable for the identification and quantitation of SVs at a pre-determined threshold.

FungalTraits vs. FUNGuild: Comparison of Ecological Functional Assignments of Leaf- and Needle-Associated Fungi Across 12 Temperate Tree Species.

Recently, a new annotation tool "FungalTraits" was created based on the previous FUNGuild and FunFun databases, which has attracted high attention in the scientific community. These databases were widely used to gain more information from fungal sequencing datasets by assigning fungal functional traits. More than 1500 publications so far employed FUNGuild and the aim of this study is to compare this successful database with the recent FungalTraits database. Quality and quantity of the assignment by FUNGuild and FungalTraits to a fungal internal transcribed spacer (ITS)-based amplicon sequencing dataset on amplicon sequence variants (ASVs) were addressed. Sequencing dataset was derived from leaves and needles of 12 temperate broadleaved and coniferous tree species. We found that FungalTraits assigned more functional traits than FUNGuild, and especially the coverage of saprotrophs, plant pathogens, and endophytes was higher while lichenized fungi revealed similar findings. Moreover, ASVs derived from leaves and needles of each tree species were better assigned to all available fungal traits as well as to saprotrophs by FungalTraits compared to FUNGuild in particular for broadleaved tree species. Assigned ASV richness as well as fungal functional community composition was higher and more diverse after analyses with FungalTraits compared to FUNGuild. Moreover, datasets of both databases showed similar effect of environmental factors for saprotrophs but for endophytes, unidentical patterns of significant corresponding factors were obtained. As a conclusion, FungalTraits is superior to FUNGuild in assigning a higher quantity and quality of ASVs as well as a higher frequency of significant correlations with environmental factors.

Proprotein convertase subtilisin/kexin type 9 genetic screening using the vervet (Chlorocebus aethiops) model.

BACKGROUND: The proprotein convertase subtilisin/kexin type 9 (PCSK9) gene has come to prominence due to its reported function in the clearance of low-density lipoprotein cholesterol. The vervet monkey (Chlorocebus aethiops) was utilized to study the genetics of PCSK9 gene. METHOD: Sixteen vervet monkeys were selected to screen for possible PCSK9 polymorphisms and to determine gene expression. RESULTS: Four PCSK9 sequence variants (T112T, R148S, H177N and G635G) were identified and three of these variants (H177N, R148S, and G635G) were categorized as loss of function mutations. A decline in gene expression levels was also observed in animals harboring these three variants. Although the selected variants might have affected the level of gene expression in the selected animals, individual variation was also noticed in some of these individuals with the G635G variant. CONCLUSION: Based on the findings obtained from this study, it is suggestive that the activity of PCSK9 was hindered.

Interpretation of genomic sequence variants in heritable skin diseases: A primer for clinicians.

Over 1000 heritable disorders have cutaneous manifestations, some of which are syndromicin association with extracutaneous manifestations, whereas others are limited to the skin. The genetic basis of many of these conditions has been deciphered, and mutation analyses using next-generation sequencing approaches, including whole-exome sequencing, whole-genome sequencing, and whole-transcriptome analysis, are now increasingly becoming part of the diagnostic process. Besides confirming the diagnosis, information on the specific mutations can be used for subclassification with prognostication and identification of the carriers, leading to accurate genetic counseling. It also forms a basis for prenatal testing and preimplantation genetic diagnosis. Furthermore, the ongoing therapeutics developments for heritable skin diseases are often allele-specific, necessitating the knowledge of the specific genes and mutations. Although practicing clinicians increasingly employ molecular diagnostics for heritable skin diseases, they often lack the sufficient knowledge required to interpret the implications of the mutations with precision. The purpose of this primer is to provide an overview of mutation-detection strategies and how to interpret genetic information for improved diagnostics and the management of such patients.

Comparative effect of acid and heat inoculum pretreatment on dark fermentative biohydrogen production.

This study delves into the impact of various pretreatment methods on the inoculum in dark fermentation trials, specifically exploring thermal shock at different temperatures (60, 80, and 100 °C) and durations (15, 30, and 60 min), as well as acid shock at pH 5.5. Initial acidification of the substrate/inoculum mixture facilitates H2 generation, making acid shock an effective pretreatment option. However, it is also observed that combining thermal and acid pretreatments boosts H2 production synergistically. The synergy between thermal and acid pretreatments results in a significant improvement, increasing the overall hydrogen production efficiency by more than 9% compared to assays involving acidification alone. This highlights the considerable potential for optimizing pretreatment strategies. Furthermore, the study sheds light on the critical role of inoculum characteristics in the process, with diverse hydrogen-generating bacteria significantly influencing outcomes. The established equivalent performance of HCl and H2SO4 in inoculum pretreatment demonstrates the versatility of these acids in shaping the microbial community and influencing hydrogen production. The analysis of glucose conversion data highlights a prevalence of butyric acid in all trials, irrespective of the pretreatment method, emphasizing the dominance of the butyrate pathway in hydrogen generation. Additionally, an examination of the microbial community offers valuable insights into the intricate relationships between temperature, pH, and microbial diversity. Bacteroidota established its dominance among the bacterial populations, with a relative abundance exceeding 20-25% in the raw inoculum, and this dominance further increased following the treatment. Thermal and acid pretreatments result in significant shifts in dominant microbial communities, with some non-dominant phyla like Cloacimonadota and Spirochaetota becoming more prominent. These shifts in microbial diversity underscore the sensitivity of microbial communities to environmental conditions and pretreatment methods, further highlighting the importance of understanding their dynamics in dark fermentation processes.

The review of genetic screening services and common BRCA1/2 variants among South African breast cancer patients.

The South African genetic screening services for breast cancer comprise targeted and comprehensive tests that screen for the presence of genetic alterations. Clinically, these variants determine the risk of disease development as well as treatment approaches best suited for carriers. The current targeted tests screen for seven pathogenic sequence variants, which are mainly common among Whites, a population that constitutes 9.1% of South Africa. However, these tests are offered to all patients despite consistent negative results observed among Blacks, Indians, and Mixed ancestry (known as Coloreds in South Africa). Consequently, Blacks, White, and Colored patients who potentially carry other variants receive unbefitting treatment, resulting in poor clinical response, recurrence, and high mortality. This review aimed to identify the presence and incidence of pathogenic variants in BRCA1/2 previously reported in all South African populations. We selected literature using a scoping review approach, from which we included eight articles and two reports. Overall, we identified 59 BRCA1 and 60 BRCA2 pathogenic sequence variants from a cohort of 5709 patients and unknown patients from 90 families. The most reported variant was BRCA2 c.7943delG, which was common in White and Colored patients. None of the seven common variants was reported in either Blacks or Indians, which demonstrates the urgency to tailor genetic tests which are optimal for all South African patients and present a range of variants which could serve as diagnostic targets for Black, Indian, and Colored patients.

Amplicon-based next-generation sequencing for comparative analysis of root canal microbiome of teeth with primary and persistent/secondary endodontic infections.

OBJECTIVES: To compare the root canal microbiome profiles of primary and persistent/secondary infections using high-throughput sequencing with the help of a reliable bioinformatics algorithm. MATERIALS AND METHODS: Root canal samples of 10 teeth in the primary endodontic infection (PEI) group and 10 teeth in the persistent/secondary endodontic infection (SEI) group were included resulting in a total of 20 samples. After DNA extraction from the samples, sequencing was performed on the Illumina MiSeq platform. Pair-end Illumina reads were imported to QIIME 2; amplicon sequence variants (ASVs) generated by DADA2 were mapped to GreenGenes database. Weighted UniFrac distances were calculated and principal coordinates analysis (PCoA) was used to compare beta diversity patterns. The multiple response permutation procedure (MRPP), the analysis of similarities (ANOSIM), and permutational multivariate analysis of variance (adonis) were conducted for testing group differences. Linear discriminant analysis effect size (LEfSe) analysis was utilized to identify differentially abundant taxa between the groups. The linear discriminant analysis (LDA) score threshold was set to 4.0. RESULTS: Within the Gram-negative facultative anaerobic Gammaproteobacteria class outgroup, two orders (Pasteurellales, Vibrionales) and two families (Pasteurellaceae, Vibrionaceae) were significantly more abundant in the PEI group, whereas Gram-positive bacteria, Actinomycetales order, and Gram-positive anaerobic taxa, one genus (Olsenella) and one species (Olsenella uli), were identified as significantly more abundant in the SEI group. CONCLUSIONS: A few taxa were differentially abundant within either the PEI or SEI group. CLINICAL RELEVANCE: Reliable bioinformatic tools are needed to define microbial profiles of endodontic infections. Based on a limited number of samples, no distinct variation was determined between the bacterial diversity of initial and recurrent endodontic infections.

Hooked Up from a Distance: Charting Genome-Wide Long-Range Interaction Maps in Neural Cells Chromatin to Identify Novel Candidate Genes for Neurodevelopmental Disorders.

DNA sequence variants (single nucleotide polymorphisms or variants, SNPs/SNVs; copy number variants, CNVs) associated to neurodevelopmental disorders (NDD) and traits often map on putative transcriptional regulatory elements, including, in particular, enhancers. However, the genes controlled by these enhancers remain poorly defined. Traditionally, the activity of a given enhancer, and the effect of its possible alteration associated to the sequence variants, has been thought to influence the nearest gene promoter. However, the obtainment of genome-wide long-range interaction maps in neural cells chromatin challenged this view, showing that a given enhancer is very frequently not connected to the nearest promoter, but to a more distant one, skipping genes in between. In this Perspective, we review some recent papers, who generated long-range interaction maps (by HiC, RNApolII ChIA-PET, Capture-HiC, or PLACseq), and overlapped the identified long-range interacting DNA segments with DNA sequence variants associated to NDD (such as schizophrenia, bipolar disorder and autism) and traits (intelligence). This strategy allowed to attribute the function of enhancers, hosting the NDD-related sequence variants, to a connected gene promoter lying far away on the linear chromosome map. Some of these enhancer-connected genes had indeed been already identified as contributive to the diseases, by the identification of mutations within the gene's protein-coding regions (exons), validating the approach. Significantly, however, the connected genes also include many genes that were not previously found mutated in their exons, pointing to novel candidate contributors to NDD and traits. Thus, long-range interaction maps, in combination with DNA variants detected in association with NDD, can be used as "pointers" to identify novel candidate disease-relevant genes. Functional manipulation of the long-range interaction network involving enhancers and promoters by CRISPR-Cas9-based approaches is beginning to probe for the functional significance of the identified interactions, and the enhancers and the genes involved, improving our understanding of neural development and its pathology.

A Highly Efficient Workflow for Detecting and Identifying Sequence Variants in Therapeutic Proteins with a High Resolution LC-MS/MS Method.

Large molecule protein therapeutics have steadily grown and now represent a significant portion of the overall pharmaceutical market. These complex therapies are commonly manufactured using cell culture technology. Sequence variants (SVs) are undesired minor variants that may arise from the cell culture biomanufacturing process that can potentially affect the safety and efficacy of a protein therapeutic. SVs have unintended amino acid substitutions and can come from genetic mutations or translation errors. These SVs can either be detected using genetic screening methods or by mass spectrometry (MS). Recent advances in Next-generation Sequencing (NGS) technology have made genetic testing cheaper, faster, and more convenient compared to time-consuming low-resolution tandem MS and Mascot Error Tolerant Search (ETS)-based workflows which often require ~6 to 8 weeks data turnaround time. However, NGS still cannot detect non-genetic derived SVs while MS analysis can do both. Here, we report a highly efficient Sequence Variant Analysis (SVA) workflow using high-resolution MS and tandem mass spectrometry combined with improved software to greatly reduce the time and resource cost associated with MS SVA workflows. Method development was performed to optimize the high-resolution tandem MS and software score cutoff for both SV identification and quantitation. We discovered that a feature of the Fusion Lumos caused significant relative under-quantitation of low-level peptides and turned it off. A comparison of common Orbitrap platforms showed that similar quantitation values were obtained on a spiked-in sample. With this new workflow, the amount of false positive SVs was decreased by up to 93%, and SVA turnaround time by LC-MS/MS was shortened to 2 weeks, comparable to NGS analysis speed and making LC-MS/MS the top choice for SVA workflow.

SPiP: Splicing Prediction Pipeline, a machine learning tool for massive detection of exonic and intronic variant effects on mRNA splicing.

Modeling splicing is essential for tackling the challenge of variant interpretation as each nucleotide variation can be pathogenic by affecting pre-mRNA splicing via disruption/creation of splicing motifs such as 5'/3' splice sites, branch sites, or splicing regulatory elements. Unfortunately, most in silico tools focus on a specific type of splicing motif, which is why we developed the Splicing Prediction Pipeline (SPiP) to perform, in one single bioinformatic analysis based on a machine learning approach, a comprehensive assessment of the variant effect on different splicing motifs. We gathered a curated set of 4616 variants scattered all along the sequence of 227 genes, with their corresponding splicing studies. The Bayesian analysis provided us with the number of control variants, that is, variants without impact on splicing, to mimic the deluge of variants from high-throughput sequencing data. Results show that SPiP can deal with the diversity of splicing alterations, with 83.13% sensitivity and 99% specificity to detect spliceogenic variants. Overall performance as measured by area under the receiving operator curve was 0.986, better than SpliceAI and SQUIRLS (0.965 and 0.766) for the same data set. SPiP lends itself to a unique suite for comprehensive prediction of spliceogenicity in the genomic medicine era. SPiP is available at: https://sourceforge.net/projects/splicing-prediction-pipeline/.

Pseudoexon activation in disease by non-splice site deep intronic sequence variation - wild type pseudoexons constitute high-risk sites in the human genome.

Accuracy of pre-messenger RNA (pre-mRNA) splicing is crucial for normal gene expression. Complex regulation supports the spliceosomal distinction between authentic exons and the many seemingly functional splice sites delimiting pseudoexons. Pseudoexons are nonfunctional intronic sequences that can be activated for aberrant inclusion in mRNA, which may cause disease. Pseudoexon activation is very challenging to predict, in particular when activation occurs by sequence variants that alter the splicing regulatory environment without directly affecting splice sites. As pseudoexon inclusion often evades detection due to activation of nonsense-mediated mRNA decay, and because conventional diagnostic procedures miss deep intronic sequence variation, pseudoexon activation is a heavily underreported disease mechanism. Pseudoexon characteristics have mainly been studied based on in silico predicted sequences. Moreover, because recognition of sequence variants that create or strengthen splice sites is possible by comparison with well-established consensus sequences, this type of pseudoexon activation is by far the most frequently reported. Here we review all known human disease-associated pseudoexons that carry functional splice sites and are activated by deep intronic sequence variants located outside splice site sequences. We delineate common characteristics that make this type of wild type pseudoexons distinct high-risk sites in the human genome.

Conformational variability of loops in the SARS-CoV-2 spike protein.

The SARS-CoV-2 spike (S) protein facilitates viral infection, and has been the focus of many structure determination efforts. Its flexible loop regions are known to be involved in protein binding and may adopt multiple conformations. This article identifies the S protein loops and studies their conformational variability based on the available Protein Data Bank structures. While most loops had essentially one stable conformation, 17 of 44 loop regions were observed to be structurally variable with multiple substantively distinct conformations based on a cluster analysis. Loop modeling methods were then applied to the S protein loop targets, and the prediction accuracies discussed in relation to the characteristics of the conformational clusters identified. Loops with multiple conformations were found to be challenging to model based on a single structural template.

Identifying pleiotropic variants and candidate genes for fertility and reproduction traits in Holstein cattle via association studies based on imputed whole-genome sequence genotypes.

BACKGROUND: Genetic progress for fertility and reproduction traits in dairy cattle has been limited due to the low heritability of most indicator traits. Moreover, most of the quantitative trait loci (QTL) and candidate genes associated with these traits remain unknown. In this study, we used 5.6 million imputed DNA sequence variants (single nucleotide polymorphisms, SNPs) for genome-wide association studies (GWAS) of 18 fertility and reproduction traits in Holstein cattle. Aiming to identify pleiotropic variants and increase detection power, multiple-trait analyses were performed using a method to efficiently combine the estimated SNP effects of single-trait GWAS based on a chi-square statistic. RESULTS: There were 87, 72, and 84 significant SNPs identified for heifer, cow, and sire traits, respectively, which showed a wide and distinct distribution across the genome, suggesting that they have relatively distinct polygenic nature. The biological functions of immune response and fatty acid metabolism were significantly enriched for the 184 and 124 positional candidate genes identified for heifer and cow traits, respectively. No known biological function was significantly enriched for the 147 positional candidate genes found for sire traits. The most important chromosomes that had three or more significant QTL identified are BTA22 and BTA23 for heifer traits, BTA8 and BTA17 for cow traits, and BTA4, BTA7, BTA17, BTA22, BTA25, and BTA28 for sire traits. Several novel and biologically important positional candidate genes were strongly suggested for heifer (SOD2, WTAP, DLEC1, PFKFB4, TRIM27, HECW1, DNAH17, and ADAM3A), cow (ANXA1, PCSK5, SPESP1, and JMJD1C), and sire (ELMO1, CFAP70, SOX30, DGCR8, SEPTIN14, PAPOLB, JMJD1C, and NELL2) traits. CONCLUSIONS: These findings contribute to better understand the underlying biological mechanisms of fertility and reproduction traits measured in heifers, cows, and sires, which may contribute to improve genomic evaluation for these traits in dairy cattle.

Optimization of in silico tools for predicting genetic variants: individualizing for genes with molecular sub-regional stratification.

Genes are unique in functional role and differ in their sensitivities to genetic defects, but with difficulties in pathogenicity prediction. This study attempted to improve the performance of existing in silico algorithms and find a common solution based on individualization strategy. We initiated the individualization with the epilepsy-related SCN1A variants by sub-regional stratification. SCN1A missense variants related to epilepsy were retrieved from mutation databases, and benign missense variants were collected from ExAC database. Predictions were performed by using 10 traditional tools with stepwise optimizations. Model predictive ability was evaluated using the five-fold cross-validations on variants of SCN1A, SCN2A, and KCNQ2. Additional validation was performed in SCN1A variants of damage-confirmed/familial epilepsy. The performance of commonly used predictors was less satisfactory for SCN1A with accuracy less than 80% and varied dramatically by functional domains of Nav1.1. Multistep individualized optimizations, including cutoff resetting, domain-based stratification, and combination of predicting algorithms, significantly increased predictive performance. Similar improvements were obtained for variants in SCN2A and KCNQ2. The predictive performance of the recently developed ensemble tools, such as Mendelian clinically applicable pathogenicity, combined annotation-dependent depletion and Eigen, was also improved dramatically by application of the strategy with molecular sub-regional stratification. The prediction scores of SCN1A variants showed linear correlations with the degree of functional defects and the severity of clinical phenotypes. This study highlights the need of individualized optimization with molecular sub-regional stratification for each gene in practice.

The polymorphism of bovine Cofilin-1 gene sequence variants and association analysis with growth traits in Qinchuan cattle.

In our study, four single nucleotide polymorphisms (SNPs) were identified in exon 2 of cofilin-1 (CFL1) gene in 488 Chinese Qinchuan (QC) cattle, which included two missense mutations T 2084G and G 2107C, two synonymous mutations T 2052C and T 2169C. Further, we evaluated haplotype frequency and linkage disequilibrium (LD) coefficient of four SNPs. At SNP T 2052C, G 2107C and T 2169C, the QC cattle population belonged to intermediate genetic diversity (0.25 < PIC-value < 0.5), whereas SNP T-2084G belonged to low polymorphism (PIC-value < 0.25). Haplotype analysis showed that 6 different haplotypes (frequency > 0.03). LD analysis showed that SNP G 2107C and T 2169C, SNP G 2107C and T 2084G were high LD, respectively (r2 > 0.33). Association analysis indicated that SNP T 2052C was significantly associated with body length, chest breadth, chest depth and body mass in the QC population (p < 0.01 or p < 0.05). SNP G 2107C was significantly associated with rump length (p < 0.05). SNP T 2169C was significantly associated with chest breadth and chest depth (p < .01 or p < .05). The results of our study suggest that the CFL1 gene may be a strong candidate gene that affects growth traits in the QC cattle breeding program.