Discovering predisposing genes for hereditary breast cancer using deep learning.

Breast cancer (BC) is the most common malignancy affecting Western women today. It is estimated that as many as 10% of BC cases can be attributed to germline variants. However, the genetic basis of the majority of familial BC cases has yet to be identified. Discovering predisposing genes contributing to familial BC is challenging due to their presumed rarity, low penetrance, and complex biological mechanisms. Here, we focused on an analysis of rare missense variants in a cohort of 12 families of Middle Eastern origins characterized by a high incidence of BC cases. We devised a novel, high-throughput, variant analysis pipeline adapted for family studies, which aims to analyze variants at the protein level by employing state-of-the-art machine learning models and three-dimensional protein structural analysis. Using our pipeline, we analyzed 1218 rare missense variants that are shared between affected family members and classified 80 genes as candidate pathogenic. Among these genes, we found significant functional enrichment in peroxisomal and mitochondrial biological pathways which segregated across seven families in the study and covered diverse ethnic groups. We present multiple evidence that peroxisomal and mitochondrial pathways play an important, yet underappreciated, role in both germline BC predisposition and BC survival.

Optical Genome Mapping for Applications in Repeat Expansion Disorders.

Short tandem repeat (STR) expansions are associated with more than 60 genetic disorders. The size and stability of these expansions correlate with the severity and age of onset of the disease. Therefore, being able to accurately detect the absolute length of STRs is important. Current diagnostic assays include laborious lab experiments, including repeat-primed PCR and Southern blotting, that still cannot precisely determine the exact length of very long repeat expansions. Optical genome mapping (OGM) is a cost-effective and easy-to-use alternative to traditional cytogenetic techniques and allows the comprehensive detection of chromosomal aberrations and structural variants >500 bp in length, including insertions, deletions, duplications, inversions, translocations, and copy number variants. Here, we provide methodological guidance for preparing samples and performing OGM as well as running the analysis pipelines and using the specific repeat expansion workflows to determine the exact repeat length of repeat expansions expanded beyond 500 bp. Together these protocols provide all details needed to analyze the length and stability of any repeat expansion with an expected repeat size difference from the expected wild-type allele of >500 bp. © 2024 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Genomic ultra-high-molecular-weight DNA isolation, labeling, and staining Basic Protocol 2: Data generation and genome mapping using the Bionano Saphyr® System Basic Protocol 3: Manual De Novo Assembly workflow Basic Protocol 4: Local guided assembly workflow Basic Protocol 5: EnFocus Fragile X workflow Basic Protocol 6: Molecule distance script workflow.

Multi-Allelic Mitochondrial DNA Deletions in an Adult Dog with Chronic Weakness, Exercise Intolerance and Lactic Acidemia.

(1) Background: An adult dog was presented to a board-certified veterinary neurologist for evaluation of chronic weakness, exercise intolerance and lactic acidemia. (2) Methods: A mitochondrial myopathy was diagnosed based on the histological and histochemical phenotype of numerous COX-negative muscle fibers. Whole-genome sequencing established the presence of multiple extended deletions in the mitochondrial DNA (mtDNA), with the highest prevalence between the 1-11 kb positions of the approximately 16 kb mitochondrial chromosome. Such findings are typically suggestive of an underlying nuclear genome variant affecting mitochondrial replication, repair, or metabolism. (3) Results: Numerous variants in the nuclear genome unique to the case were identified in the whole-genome sequence data, and one, the insertion of a DYNLT1 retrogene, whose parent gene is a regulator of the mitochondrial voltage-dependent anion channel (VDAC), was considered a plausible causal variant. (4) Conclusions: Here, we add mitochondrial deletion disorders to the spectrum of myopathies affecting adult dogs.

Germline variant analysis from a cohort of patients with severe hypertriglyceridemia in Brazil.

Hypertriglyceridemia (HTG) is a common dyslipidemia associated with an increased risk of cardiovascular disease and pancreatitis. It is well stablished that the severe cases of disease often present with an underlying genetic cause. In this study, we determined the frequency and variation spectrum of genes involved in the triglyceride metabolism in a series of Brazilian patients with severe HTG. A total of 212 patients with very high HTG, defined with fasting triglycerides (TG) ≥ 880 mg/ dL, that underwent a multi-gene panel testing were included in this research. Germline deleterious variants (i.e. Pathogenic/Likely Pathogenic (P/LP) variants) were identified in 28 out of 212 patients, reflecting an overall diagnostic yield of 13% in our cohort. Variants of unknown significance (VUS) were identified in 87 patients, and represent 80% of detected variants in this dataset. We confirm the LPL as the most frequently mutated gene in patients with severe HTG, and we had only one suspected case of familial chylomicronemia syndrome, caused by a homozygous variant in LMF1, in our cohort. Notably, we report 16 distinct and novel variants (P/LP and VUS), each of them representing a single case, not previously reported in any public databases or other studies. Our data expand our knowledge of genetic variation spectrum in patients with severe HTG in the Brazilian population, often underrepresented in public genomic databases, being also a valuable clinical resource for genetic counseling and healthcare programs in the country.

Integrated approach to generate artificial samples with low tumor fraction for somatic variant calling benchmarking.

BACKGROUND: High-throughput sequencing (HTS) has become the gold standard approach for variant analysis in cancer research. However, somatic variants may occur at low fractions due to contamination from normal cells or tumor heterogeneity; this poses a significant challenge for standard HTS analysis pipelines. The problem is exacerbated in scenarios with minimal tumor DNA, such as circulating tumor DNA in plasma. Assessing sensitivity and detection of HTS approaches in such cases is paramount, but time-consuming and expensive: specialized experimental protocols and a sufficient quantity of samples are required for processing and analysis. To overcome these limitations, we propose a new computational approach specifically designed for the generation of artificial datasets suitable for this task, simulating ultra-deep targeted sequencing data with low-fraction variants and demonstrating their effectiveness in benchmarking low-fraction variant calling. RESULTS: Our approach enables the generation of artificial raw reads that mimic real data without relying on pre-existing data by using NEAT, a fine-grained read simulator that generates artificial datasets using models learned from multiple different datasets. Then, it incorporates low-fraction variants to simulate somatic mutations in samples with minimal tumor DNA content. To prove the suitability of the created artificial datasets for low-fraction variant calling benchmarking, we used them as ground truth to evaluate the performance of widely-used variant calling algorithms: they allowed us to define tuned parameter values of major variant callers, considerably improving their detection of very low-fraction variants. CONCLUSIONS: Our findings highlight both the pivotal role of our approach in creating adequate artificial datasets with low tumor fraction, facilitating rapid prototyping and benchmarking of algorithms for such dataset type, as well as the important need of advancing low-fraction variant calling techniques.

Comprehensive prognostic assessment in kidney cancer: A multidimensional approach analyzing Fufang Sanling granules, genetic variants, and immune infiltration.

This study delves into the potential therapeutic benefits of Fufang Sanling Granules for kidney cancer, focusing on their active components and the underlying mechanisms of their interaction with cancer-related targets. By constructing a drug-active component-target network based on eight herbs, key active compounds such as kaempferol, quercetin, and linolenic acid were identified, suggesting their pivotal roles in modulating immune responses and cellular signaling pathways relevant to cancer progression. The research further identified 51 central drug-disease genes through comprehensive bioinformatics analyses, implicating their involvement in crucial biological processes and pathways. A novel risk score model, encompassing six genes with significant prognostic value for renal cancer, was established and validated, showcasing its effectiveness in predicting patient outcomes through mutation analysis and survival studies. The model's predictive power was further confirmed by its ability to stratify patients into distinct risk groups with significant survival differences, highlighting its potential as a prognostic tool. Additionally, the study explored the relationship between gene expression within the identified black module and the risk score, uncovering significant associations with the extracellular matrix and immune infiltration patterns. This reveals the complex interplay between the tumor microenvironment and cancer progression. The integration of the risk score with clinical parameters through a nomogram significantly improved the model's predictive accuracy, offering a more comprehensive tool for predicting kidney cancer prognosis. In summary, by combining detailed molecular analyses with clinical insights, this study presents a robust framework for understanding the therapeutic potential of Fufang Sanling Granules in kidney cancer. It not only sheds light on the active components and their interactions with cancer-related genes but also introduces a reliable risk score model, paving the way for personalized treatment strategies and improved patient management in the future.

Genetic variant detection in a South African haemophilia B population.

BACKGROUND: Haemophilia B is characterised by a deficiency of factor IX (FIX) protein due to genetic variants in the FIX gene (F9). Genetic testing may have a vital role in effectively managing haemophilia B. However, in many developing countries, comprehensive genetic variant detection is unavailable. This study aimed to address the lack of genetic data in our country by conducting genetic variant detection on people affected by haemophilia B in our region. METHODS: Twenty-one participants were screened with a direct Sanger sequencing method to identify variants in the F9 gene. The identified variants were then compared to previously published variants and/or to a reference database. RESULTS AND DISCUSSION: A total of ten F9 genetic changes were detected, with five of them being novel. These identified variants were distributed across different domains of the FIX protein. Only one participant had a history of inhibitor formation against FIX replacement therapy. Notably, this participant had two distinct genetic changes present adjacent to each other. Thus, we hypothesise that the presence of multiple variants within the same functional region of the gene may increase the risk for inhibitor development. CONCLUSION: The discovery of novel pathogenic variations in the F9 gene highlights the importance of genetic analysis in specific geographical regions. The possible link between a complex variant and inhibitor formation illustrates the potential role that genetic screening has as a pre-treatment tool in predicting treatment reactions and outcomes.

Validated WGS and WES protocols proved saliva-derived gDNA as an equivalent to blood-derived gDNA for clinical and population genomic analyses.

BACKGROUND: Whole exome sequencing (WES) and whole genome sequencing (WGS) have become standard methods in human clinical diagnostics as well as in population genomics (POPGEN). Blood-derived genomic DNA (gDNA) is routinely used in the clinical environment. Conversely, many POPGEN studies and commercial tests benefit from easy saliva sampling. Here, we evaluated the quality of variant call sets and the level of genotype concordance of single nucleotide variants (SNVs) and small insertions and deletions (indels) for WES and WGS using paired blood- and saliva-derived gDNA isolates employing genomic reference-based validated protocols. METHODS: The genomic reference standard Coriell NA12878 was repeatedly analyzed using optimized WES and WGS protocols, and data calls were compared with the truth dataset published by the Genome in a Bottle Consortium. gDNA was extracted from the paired blood and saliva samples of 10 participants and processed using the same protocols. A comparison of paired blood-saliva call sets was performed in the context of WGS and WES genomic reference-based technical validation results. RESULTS: The quality pattern of called variants obtained from genomic-reference-based technical replicates correlates with data calls of paired blood-saliva-derived samples in all levels of tested examinations despite a higher rate of non-human contamination found in the saliva samples. The F1 score of 10 blood-to-saliva-derived comparisons ranged between 0.8030-0.9998 for SNVs and between 0.8883-0.9991 for small-indels in the case of the WGS protocol, and between 0.8643-0.999 for SNVs and between 0.7781-1.000 for small-indels in the case of the WES protocol. CONCLUSION: Saliva may be considered an equivalent material to blood for genetic analysis for both WGS and WES under strict protocol conditions. The accuracy of sequencing metrics and variant-detection accuracy is not affected by choosing saliva as the gDNA source instead of blood but much more significantly by the genomic context, variant types, and the sequencing technology used.

123VCF: an intuitive and efficient tool for filtering VCF files.

BACKGROUND: The advent of Next-Generation Sequencing (NGS) has catalyzed a paradigm shift in medical genetics, enabling the identification of disease-associated variants. However, the vast quantum of data produced by NGS necessitates a robust and dependable mechanism for filtering irrelevant variants. Annotation-based variant filtering, a pivotal step in this process, demands a profound understanding of the case-specific conditions and the relevant annotation instruments. To tackle this complex task, we sought to design an accessible, efficient and more importantly easy to understand variant filtering tool. RESULTS: Our efforts culminated in the creation of 123VCF, a tool capable of processing both compressed and uncompressed Variant Calling Format (VCF) files. Built on a Java framework, the tool employs a disk-streaming real-time filtering algorithm, allowing it to manage sizable variant files on conventional desktop computers. 123VCF filters input variants in accordance with a predefined filter sequence applied to the input variants. Users are provided the flexibility to define various filtering parameters, such as quality, coverage depth, and variant frequency within the populations. Additionally, 123VCF accommodates user-defined filters tailored to specific case requirements, affording users enhanced control over the filtering process. We evaluated the performance of 123VCF by analyzing different types of variant files and comparing its runtimes to the most similar algorithms like BCFtools filter and GATK VariantFiltration. The results indicated that 123VCF performs relatively well. The tool's intuitive interface and potential for reproducibility make it a valuable asset for both researchers and clinicians. CONCLUSION: The 123VCF filtering tool provides an effective, dependable approach for filtering variants in both research and clinical settings. As an open-source tool available at https://project123vcf.sourceforge.io , it is accessible to the global scientific and clinical community, paving the way for the discovery of disease-causing variants and facilitating the advancement of personalized medicine.

A Deep Sequencing Strategy for Investigation of Virus Variants within African Swine Fever Virus-Infected Pigs.

African swine fever virus (ASFV) is the causative agent of African swine fever, an economically important disease of pigs, often with a high case fatality rate. ASFV has demonstrated low genetic diversity among isolates collected within Eurasia. To explore the influence of viral variants on clinical outcomes and infection dynamics in pigs experimentally infected with ASFV, we have designed a deep sequencing strategy. The variant analysis revealed unique SNPs at <10% frequency in several infected pigs as well as some SNPs that were found in more than one pig. In addition, a deletion of 10,487 bp (resulting in the complete loss of 21 genes) was present at a nearly 100% frequency in the ASFV DNA from one pig at position 6362-16849. This deletion was also found to be present at low levels in the virus inoculum and in two other infected pigs. The current methodology can be used for the currently circulating Eurasian ASFVs and also adapted to other ASFV strains and genotypes. Comprehensive deep sequencing is critical for following ASFV molecular evolution, especially for the identification of modifications that affect virus virulence.

TOR2A Variants in Blepharospasm.

Background: Genetic factors have been implicated in the pathogenesis of blepharospasm (BSP), a dystonia characterized by excessive blinking and involuntary eyelid closure. Previous research identified a co-segregating deleterious TOR2A variant (GRCh38/hg38, NC_000009.12: g.127733410G>A, NM_001085347.3:c.568C>T, p. Arg190Cys) in three subjects with BSP and three carriers within a multi-generation pedigree. Other TOR2A variants have been reported in patients with dystonia. Methods: Sanger sequencing was used to screen a cohort of 307 subjects with isolated BSP or BSP-plus dystonia affecting additional anatomical segments (BSP+). We also utilized computational tools to uniformly assess the deleteriousness and potential pathogenicity of previously reported TOR2A variants. Results: There were no highly deleterious TOR2A variants in the coding or contiguous splice site regions of TOR2A within our cohort of 307 subjects. Discussion: Highly deleterious variants in TOR2A are rare in patients with BSP/BSP+ phenotypes. Highlights: Over 300 patients with BSP were screened for variants in TOR2A, a TOR1A (DYT1) homologue. No highly deleterious variants were identified in our cohort. The role of TOR2A in BSP and other forms of dystonia remains indeterminant.

Uncovering the Molecular Drivers of NHEJ DNA Repair-Implicated Missense Variants and Their Functional Consequences.

Variants in non-homologous end joining (NHEJ) DNA repair genes are associated with various human syndromes, including microcephaly, growth delay, Fanconi anemia, and different hereditary cancers. However, very little has been done previously to systematically record the underlying molecular consequences of NHEJ variants and their link to phenotypic outcomes. In this study, a list of over 2983 missense variants of the principal components of the NHEJ system, including DNA Ligase IV, DNA-PKcs, Ku70/80 and XRCC4, reported in the clinical literature, was initially collected. The molecular consequences of variants were evaluated using in silico biophysical tools to quantitatively assess their impact on protein folding, dynamics, stability, and interactions. Cancer-causing and population variants within these NHEJ factors were statistically analyzed to identify molecular drivers. A comprehensive catalog of NHEJ variants from genes known to be mutated in cancer was curated, providing a resource for better understanding their role and molecular mechanisms in diseases. The variant analysis highlighted different molecular drivers among the distinct proteins, where cancer-driving variants in anchor proteins, such as Ku70/80, were more likely to affect key protein-protein interactions, whilst those in the enzymatic components, such as DNA-PKcs, were likely to be found in intolerant regions undergoing purifying selection. We believe that the information acquired in our database will be a powerful resource to better understand the role of non-homologous end-joining DNA repair in genetic disorders, and will serve as a source to inspire other investigations to understand the disease further, vital for the development of improved therapeutic strategies.

PATHOLOGICAL SIGNIFICANCE OF CDH1/E-CADHERIN GERMLINE SEQUENCE VARIANTS IN BREAST CANCER PATIENTS.

BACKGROUND: Germline alterations of the CDH1 (E-cadherin) tumor suppressor gene have been reported in several epithelial malignancies like hereditary diffuse gastric cancer and lobular breast cancer. E-cadherin plays a central role in proliferation, maintenance of cell-to-cell adhesion, polarity, and epithelial-mesenchymal transition of tissue cells. It is necessary to analyze the impact of the CDH1 germline sequence variants on protein and predict its clinical significance in breast cancer (BC) progression. The aim of the current study was to evaluate the impact and association of CDH1 gene potentially pathogenic variants/likely pathogenic variants (PVs/LPVs) with the initiation and progression of BC. MATERIALS AND METHODS: In this study, the clinical data of 200 BC patients have been analyzed based on the type of BC, age, grade, stage, hormonal status, and risk factors. Blood samples from 50 healthy donors were used as a control. Furthermore, CDH1 gene molecular analysis, along with in silico analysis, was provided to assess the invasiveness and progression of BC caused by the E-cadherin protein. RESULTS: Four variants were identified by genetic screening within the CDH1 gene that included variations in exons 7, 8, 10, 11, and 13. Exon 10 had splice site mutation at position c.1337C>A, affecting the protein structure. In exon 11, there was an insertion of T base at position 1669, resulting in truncated protein compared to a normal one that can lead to the disease-causing non- sense-mediated decay and exon 13 variant c.2076T>C has already known polymorphism. In silico analysis of CDH1 showed the presence of the different variants that indicated the overall disruption of protein structure and function. CONCLUSIONS: The further functional analysis of these variants and their association with BC can be ensured by increasing the sample size and in vivo studies using mouse models.

Leveraging AI Advances and Online Tools for Structure-Based Variant Analysis.

Understanding how a gene variant affects protein function is important in life science, as it helps explain traits or dysfunctions in organisms. In a clinical setting, this understanding makes it possible to improve and personalize patient care. Bioinformatic tools often only assign a pathogenicity score, rather than providing information about the molecular basis for phenotypes. Experimental testing can furnish this information, but this is slow and costly and requires expertise and equipment not available in a clinical setting. Conversely, mapping a gene variant onto the three-dimensional (3D) protein structure provides a fast molecular assessment free of charge. Before 2021, this type of analysis was severely limited by the availability of experimentally determined 3D protein structures. Advances in artificial intelligence algorithms now allow confident prediction of protein structural features from sequence alone. The aim of the protocols presented here is to enable non-experts to use databases and online tools to investigate the molecular effect of a genetic variant. The Basic Protocol relies only on the online resources AlphaFold, Protein Structure Database, and UniProt. Alternate Protocols document the usage of the Protein Data Bank, SWISS-MODEL, ColabFold, and PyMOL for structure-based variant analysis. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol: 3D Mapping based on UniProt and AlphaFold Alternate Protocol 1: Using experimental models from the PDB Alternate Protocol 2: Using information from homology modeling with SWISS-MODEL Alternate Protocol 3: Predicting 3D structures with ColabFold Alternate Protocol 4: Structure visualization and analysis with PyMOL.

Cation exchange chromatography on a monodisperse 3 µm particle enables extensive analytical similarity assessment of biosimilars.

Biosimilarity assessment requires extensive characterization and comparability exercises to investigate product quality attributes of an originator product and its potential biosimilar(s) and to highlight any differences between them. Performing a thorough comparison allows a shortened approval path, which also eliminates lengthy and expensive clinical trials, ensuring comparable product quality and efficacy but at lower drug prices. The wide variety of analytical methods available for biosimilar assessment ranges from biological to analytical assays, each providing orthogonal information to fully characterize biosimilar candidates. Intact native mass spectrometry (MS) has been shown to be an excellent tool for detection and monitoring of important quality attributes such as N-glycosylation, deamidation, sequence truncation and higher order structures. When combined with efficient upfront separation methods, simplification of the proteoform heterogeneity and associated complexity prior to MS analysis can be achieved. Native mass spectrometry can provide robust and accurate results within short analysis times and requires minimal sample preparation. In this study we report the use of a monodisperse strong cation exchange chromatography phase hyphenated with Orbitrap mass spectrometry (SCX-MS) to compare the best-selling biopharmaceutical product Humira® with 7 commercially approved biosimilar products. SCX-MS analysis allowed for the identification of previously described as well as so far unreported proteoforms and their relative quantitation across all samples, revealing differences in N-glycosylation and lysine truncation, as well as unique features for some products such as sialylation and N-terminal clipping. SCX-MS analysis, powered by a highly efficient separation column, enabled deep and efficient analytical comparison of biosimilar products.

Genomic Evidence of Multiple Introductions of SARS-CoV-2 in Mauritania.

The rapid and global spread of the novel coronavirus severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has raised serious public health concerns, including in Mauritania. We sequenced and analyzed the entire genome of 13 SARS-CoV-2 virus strains isolated from polymerase chain reaction (PCR)-positive symptomatic patients sampled from March 3 to May 31, 2021 to better understand SARS-CoV-2 introduction, propagation, and evolution in Mauritania. A phylogenetic tree using available data from the EpiCoV GISAID database and a variant network with non-Mauritanian sequences were constructed. Variant analysis of the 13 Mauritanian SARS-CoV-2 genome sequences indicated an average mutational percentage of 0.39, which is similar to that in other countries. Phylogenetic analysis revealed multiple spatiotemporal introductions, mainly from Europe (France, Belgium) and Africa (Senegal, Côte d'Ivoire), which also provided evidence of early community transmission. A total of 2 unique mutations, namely, NSP6_Q208K and NSP15_S273T, were detected in the NSP6 and NSP15 genes, respectively, confirming the aforementioned introduction of SARS-CoV-2 in Mauritania. These findings highlight the relevance of continuous genomic monitoring strategies for understanding virus transmission dynamics and acquiring knowledge to address forthcoming sources of infection in Africa.

Systematic elucidation of genetic mechanisms underlying cholesterol uptake.

Genetic variation contributes greatly to LDL cholesterol (LDL-C) levels and coronary artery disease risk. By combining analysis of rare coding variants from the UK Biobank and genome-scale CRISPR-Cas9 knockout and activation screening, we substantially improve the identification of genes whose disruption alters serum LDL-C levels. We identify 21 genes in which rare coding variants significantly alter LDL-C levels at least partially through altered LDL-C uptake. We use co-essentiality-based gene module analysis to show that dysfunction of the RAB10 vesicle transport pathway leads to hypercholesterolemia in humans and mice by impairing surface LDL receptor levels. Further, we demonstrate that loss of function of OTX2 leads to robust reduction in serum LDL-C levels in mice and humans by increasing cellular LDL-C uptake. Altogether, we present an integrated approach that improves our understanding of the genetic regulators of LDL-C levels and provides a roadmap for further efforts to dissect complex human disease genetics.

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.

Whole-genome sequencing and comparative genomic analysis of potential biotechnological strains of Trichoderma harzianum, Trichoderma atroviride, and Trichoderma reesei.

Trichoderma atroviride and Trichoderma harzianum are widely used as commercial biocontrol agents against plant diseases. Recently, T. harzianum IOC-3844 (Th3844) and T. harzianum CBMAI-0179 (Th0179) demonstrated great potential in the enzymatic conversion of lignocellulose into fermentable sugars. Herein, we performed whole-genome sequencing and assembly of the Th3844 and Th0179 strains. To assess the genetic diversity within the genus Trichoderma, the results of both strains were compared with strains of T. atroviride CBMAI-00020 (Ta0020) and T. reesei CBMAI-0711 (Tr0711). The sequencing coverage value of all genomes evaluated in this study was higher than that of previously reported genomes for the same species of Trichoderma. The resulting assembly revealed total lengths of 40 Mb (Th3844), 39 Mb (Th0179), 36 Mb (Ta0020), and 32 Mb (Tr0711). A genome-wide phylogenetic analysis provided details on the relationships of the newly sequenced species with other Trichoderma species. Structural variants revealed genomic rearrangements among Th3844, Th0179, Ta0020, and Tr0711 relative to the T. reesei QM6a reference genome and showed the functional effects of such variants. In conclusion, the findings presented herein allow the visualization of genetic diversity in the evaluated strains and offer opportunities to explore such fungal genomes in future biotechnological and industrial applications.

Testing for Facioscapulohumeral Muscular Dystrophy with Optical Genome Mapping.

The introduction of optical genome mapping has improved time constraints and a lack of specificity from previous methodologies when performing genome-wide analyses of samples. Optical genome mapping allows for the detection of structural variations, aberrations, and functionality traits from a single stained molecule of DNA. Though the preparation time is increased compared to previously utilized visualization techniques, optical genome mapping significantly reduces the time needed for analysis. Specifically, individual disease pipelines have been developed to rapidly analyze prepared samples. One of these diseases, Facioscapulohumeral Muscular Dystrophy (FSHD), is detected through quantification of the D4Z4 repeat array on chromosome 4q35. Optical genome mapping, with the ability to enumerate the repeats of the D4Z4 array, has demonstrated the capability to precisely diagnose FSHD. In this protocol, the preparation of samples and subsequent loading and analysis in an optical genome mapping system is discussed for the detection and analysis of FSHD. These methods should prove highly useful in FSHD analyses and beyond with the development of further disease analysis pipelines within the instrument. © 2023 Wiley Periodicals LLC. Basic Protocol 1: Genomic DNA isolation, labeling, and staining Basic Protocol 2: Mapping and analysis with the Bionano Saphyr® system.