Large-scale Genome Sequencing Research Articles

Chromatin remodellers as therapeutic targets.

Large-scale cancer genome sequencing studies have revealed that chromatin regulators are frequently mutated in cancer. In particular, more than 20% of cancers harbour mutations in genes that encode subunits of SWI/SNF (BAF) chromatin remodelling complexes. Additional links of SWI/SNF complexes to disease have emerged with the findings that some oncogenes drive transformation by co-opting SWI/SNF function and that germline mutations in select SWI/SNF subunits are the basis of several neurodevelopmental disorders. Other chromatin remodellers, including members of the ISWI, CHD and INO80/SWR complexes, have also been linked to cancer and developmental disorders. Consequently, therapeutic manipulation of SWI/SNF and other remodelling complexes has become of great interest, and drugs that target SWI/SNF subunits have entered clinical trials. Genome-wide perturbation screens in cancer cell lines with SWI/SNF mutations have identified additional synthetic lethal targets and led to further compounds in clinical trials, including one that has progressed to FDA approval. Here, we review the progress in understanding the structure and function of SWI/SNF and other chromatin remodelling complexes, mechanisms by which SWI/SNF mutations cause cancer and neurological diseases, vulnerabilities that arise because of these mutations and efforts to target SWI/SNF complexes and synthetic lethal targets for therapeutic benefit.

Population structure and antibiotic resistance of swine extraintestinal pathogenic Escherichia coli from China

Extraintestinal Pathogenic Escherichia coli (ExPEC) pose a significant threat to human and animal health. However, the diversity and antibiotic resistance of animal ExPEC, and their connection to human infections, remain largely unexplored. The study performs large-scale genome sequencing and antibiotic resistance testing of 499 swine-derived ExPEC isolates from China. Results show swine ExPEC are phylogenetically diverse, with over 80% belonging to phylogroups B1 and A. Importantly, 15 swine ExPEC isolates exhibit genetic relatedness to human-origin E. coli strains. Additionally, 49 strains harbor toxins typical of enteric E. coli pathotypes, implying hybrid pathotypes. Notably, 97% of the total strains are multidrug resistant, including resistance to critical human drugs like third- and fourth-generation cephalosporins. Correspondingly, genomic analysis unveils prevalent antibiotic resistance genes (ARGs), often associated with co-transfer mechanisms. Furthermore, analysis of 20 complete genomes illuminates the transmission pathways of ARGs within swine ExPEC and to human pathogens. For example, the transmission of plasmids co-harboring fosA3, blaCTX-M-14, and mcr-1 genes between swine ExPEC and human-origin Salmonella enterica is observed. These findings underscore the importance of monitoring and controlling ExPEC infections in animals, as they can serve as a reservoir of ARGs with the potential to affect human health or even be the origin of pathogens infecting humans.

Population structure and antibiotic resistance of swine extraintestinal pathogenic Escherichia coli from China

Extraintestinal Pathogenic Escherichia coli (ExPEC) pose a significant threat to human and animal health. However, the diversity and antibiotic resistance of animal ExPEC, and their connection to human infections, remain largely unexplored. The study performs large-scale genome sequencing and antibiotic resistance testing of 499 swine-derived ExPEC isolates from China. Results show swine ExPEC are phylogenetically diverse, with over 80% belonging to phylogroups B1 and A. Importantly, 15 swine ExPEC isolates exhibit genetic relatedness to human-origin E. coli strains. Additionally, 49 strains harbor toxins typical of enteric E. coli pathotypes, implying hybrid pathotypes. Notably, 97% of the total strains are multidrug resistant, including resistance to critical human drugs like third- and fourth-generation cephalosporins. Correspondingly, genomic analysis unveils prevalent antibiotic resistance genes (ARGs), often associated with co-transfer mechanisms. Furthermore, analysis of 20 complete genomes illuminates the transmission pathways of ARGs within swine ExPEC and to human pathogens. For example, the transmission of plasmids co-harboring fosA3, blaCTX-M-14, and mcr-1 genes between swine ExPEC and human-origin Salmonella enterica is observed. These findings underscore the importance of monitoring and controlling ExPEC infections in animals, as they can serve as a reservoir of ARGs with the potential to affect human health or even be the origin of pathogens infecting humans.

COPO - Managing sample metadata for biodiversity: considerations from the Darwin Tree of Life project

Large-scale reference genome sequencing projects for all of biodiversity are underway and common standards have been in place for some years to enable the understanding and sharing of sequence data. However, the metadata that describes the collection, processing and management of samples, and link to the associated sequencing and genome data, are not yet adequately developed and standardised for these projects. At the time of writing, the Darwin Tree of Life (DToL) Project is over two years into its ten-year ambition to sequence all described eukaryotic species in Britain and Ireland. We have sought consensus from a wide range of scientists across taxonomic domains to determine the minimal set of metadata that we collectively deem as critically important to accompany each sequenced specimen. These metadata are made available throughout the subsequent laboratory processes, and once collected, need to be adequately managed to fulfil the requirements of good data management practice. Due to the size and scale of management required, software tools are needed. These tools need to implement rigorous development pathways and change management procedures to ensure that effective research data management of key project and sample metadata is maintained. Tracking of sample properties through the sequencing process is handled by Lab Information Management Systems (LIMS), so publication of the sequenced data is achieved via technical integration of LIMS and data management tools. Discussions with community members on how metadata standards need to be managed within large-scale programmes is a priority in the planning process. Here we report on the standards we developed with respect to a robust and reusable mechanism of metadata collection, in the hopes that other projects forthcoming or underway will adopt these practices for metadata.

Real-time genomic surveillance for enhanced control of infectious diseases and antimicrobial resistance

This article advocates for mobilizing pathogen genomic surveillance to contain and mitigate health threats from infectious diseases and antimicrobial resistance (AMR), building upon successes achieved by large-scale genome sequencing analysis of SARS-CoV-2 variants in guiding COVID-19 monitoring and public health responses and adopting a One Health approach. Capabilities of laboratory-based surveillance and epidemic alert systems should be enhanced by fostering (i) universal access to real-time whole genome sequence (WGS) data of pathogens to inform clinical practice, infection control, public health policies, and vaccine and antimicrobial drug research and development; (ii) integration of diagnostic microbiology data, data from testing asymptomatic individuals, pathogen sequence data, clinical data, and epidemiological data into surveillance programs; (iii) stronger cross-sectorial collaborations between healthcare, public health, animal health, and environmental surveillance and research using One Health approaches, toward understanding the ecology and transmission pathways of pathogens and AMR across ecosystems; (iv) international collaboration and interconnection of surveillance networks, harmonization of laboratory methods, and standardization of surveillance methods for global reporting, including on pathogen genomic variant or strain nomenclature; (v) responsible data sharing between surveillance networks, databases, and platforms according to FAIR (findability, accessibility, interoperability, and reusability) principles; and (vi) research on genomic surveillance system implementation and its cost-effectiveness for different pathogens and AMR threats across different settings. Regional and global One Health policies and governance initiatives should foster the concerted development and efficient utilization of pathogen genomic surveillance to protect the health of humans, animals, and the environment.

Dynamic analysis of SARS-CoV-2 evolution based on different countries

Since late 2019, COVID-19 has significantly impacted the world. Understanding the evolution of SARS-CoV-2 is crucial for protecting against future infectious pathogens. In this study, we conducted a comprehensive chronological analysis of SARS-CoV-2 evolution by examining mutation prevalence from the source countries of VOCs: United Kingdom, India, Brazil, South Africa, plus two countries: United States, Russia, utilizing genomic sequences from GISAID. Our methodological approach involved large-scale genomic sequence alignment using MAFFT, Python-based data processing on a high-performance computing platform, and advanced statistical methods the Maximal Information Coefficient (MIC), and also Long Short-Term Memory (LSTM) models for correlation analysis.Our findings elucidate the dynamics of SARS-CoV-2 evolution, highlighting the virus's changing behaviour over various pandemic stages. Key results include the discovery of three temporal mutation patterns—lineage distinct, long-span, and competitive mutations—with varying levels of impact on the virus. Notably, we observed a convergence of advantageous mutations in the spike protein, especially in the later stages of the pandemic, indicating a substantial evolutionary pressure on the virus.One of the most significant revelations is the predominant role of natural immunity over vaccination-induced immunity in driving these evolutionary changes. This emphasizes the critical need for regular vaccine updates to maintain efficacy against evolving strains.In conclusion, our study not only sheds light on the evolutionary trajectory of SARS-CoV-2 but also underscores the urgency for robust, continuous global data collection and sharing. It highlights the necessity for rapid adaptations in medical countermeasures, including vaccine development, to stay ahead of pathogen evolution. This research provides valuable insights for future pandemic preparedness and response strategies.

Species-aware DNA language models capture regulatory elements and their evolution

BackgroundThe rise of large-scale multi-species genome sequencing projects promises to shed new light on how genomes encode gene regulatory instructions. To this end, new algorithms are needed that can leverage conservation to capture regulatory elements while accounting for their evolution.ResultsHere, we introduce species-aware DNA language models, which we trained on more than 800 species spanning over 500 million years of evolution. Investigating their ability to predict masked nucleotides from context, we show that DNA language models distinguish transcription factor and RNA-binding protein motifs from background non-coding sequence. Owing to their flexibility, DNA language models capture conserved regulatory elements over much further evolutionary distances than sequence alignment would allow. Remarkably, DNA language models reconstruct motif instances bound in vivo better than unbound ones and account for the evolution of motif sequences and their positional constraints, showing that these models capture functional high-order sequence and evolutionary context. We further show that species-aware training yields improved sequence representations for endogenous and MPRA-based gene expression prediction, as well as motif discovery.ConclusionsCollectively, these results demonstrate that species-aware DNA language models are a powerful, flexible, and scalable tool to integrate information from large compendia of highly diverged genomes.

Abstract 6937: High throughput single cell simultaneous DNA and RNA sequencing identified cell-of-origin of breast cancer

Abstract Understanding the relationship between genotype and phenotype in breast cancer cells has been challenging at single cell resolution, mainly because existing high-throughput methods are limited to measuring a single modality and data must be integrated indirectly via computational methods. To address this challenge, we developed wellDR-seq, a high-throughput single cell method that can simultaneously measure the single cell whole genome and transcriptome directly from thousands of single cells in parallel. Using this method, we profiled 17,427 single cells in 6 different ER+ breast cancer patients with either premalignant disease (Ductal-carcinoma-in-situ) or invasive ductal carcinoma (IDC). From these data we identified the epithelial cell-of-origin in 3 cases, showing that ER+ breast cancer cells originated from Luminal Hormone Responsive (LumHR) in the normal breast tissues. We also found that autosomal somatic copy number aberrations were exclusively present in LumSec (< 2%) and LumHR epithelial cells, while chrX gain or loss also occurred in stromal cells (eg fibroblast and endothelial) but a low frequency (<2%) in our datasets. Additionally, our data show the impact of subclonal copy number profiles on gene expression programs, which reflects both gene dosage effects in CNA regions and more complex deregulation in copy-neutral regions. wellDR-seq offers a powerful tool for large-scale single cell genome and transcriptome simultaneous sequencing across different clinical samples, opens new avenue to investigate genotype and phenotypes interactions, identify tumor cells origins, reveal somatic copy number and mutation events, quantify the gene dosage effect and discerning differential genes expression between tumor subclones. Citation Format: Kaile Wang, Rui Ye, Shanshan Bai, Zhenna Xiao, Lei Yano, Jianzhuo Li, Yiyun Lin, Emi Sei, Steven Lin, Alastair Thompson, Savitri Krishnamurthy, Nicholas Navin. High throughput single cell simultaneous DNA and RNA sequencing identified cell-of-origin of breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6937.

A catalogue of chromosome counts for Phylum Nematoda

Nematodes are important biological models in genetics and genomics, with research driven by basic biological as well as applied questions. The presence of holocentric chromosomes, clades with frequent polyploidy and the phenomenon of programmed DNA elimination make nematode karyotypic diversity of particular interest. Here we present a catalogue of published karyotypes of nematode species, rationalising and normalising descriptions from the previous 135 years. Karyotypes of 257 species are presented in taxonomic context. Nuclear chromosome counts range from 2 to 60. Tylenchina is identified as particularly diverse in karyotype. We highlight that Rhabditida and especially parasitic Rhabditina are well-represented, but there is a paucity of data from Enoplea, Dorylaimia, and from free-living marine groups in Chromadorea. The data have been uploaded to the Genomes on a Tree (GoaT) datasystem (https://goat.genomehubs.org/) for integration with ongoing, large-scale genome sequencing efforts.

Mobilisation and analyses of publicly available SARS-CoV-2 data for pandemic responses.

The COVID-19 pandemic has seen large-scale pathogen genomic sequencing efforts, becoming part of the toolbox for surveillance and epidemic research. This resulted in an unprecedented level of data sharing to open repositories, which has actively supported the identification of SARS-CoV-2 structure, molecular interactions, mutations and variants, and facilitated vaccine development and drug reuse studies and design. The European COVID-19 Data Platform was launched to support this data sharing, and has resulted in the deposition of several million SARS-CoV-2 raw reads. In this paper we describe (1) open data sharing, (2) tools for submission, analysis, visualisation and data claiming (e.g. ORCiD), (3) the systematic analysis of these datasets, at scale via the SARS-CoV-2 Data Hubs as well as (4) lessons learnt. This paper describes a component of the Platform, the SARS-CoV-2 Data Hubs, which enable the extension and set up of infrastructure that we intend to use more widely in the future for pathogen surveillance and pandemic preparedness.

A randomized optimal k-mer indexing approach for efficient parallel genome sequence compression

Next Generation Sequencing (NGS) technology generates massive amounts of genome sequence that increases rapidly over time. As a result, there is a growing need for efficient compression algorithms to facilitate the processing, storage, transmission, and analysis of large-scale genome sequences. Over the past 31 years, numerous state-of-the-art compression algorithms have been developed. The performance of any compression algorithm is measured by three main compression metrics: compression ratio, time, and memory usage. Existing k-mer hash indexing systems take more time, due to the decision-making process based on compression results. In this paper, we propose a two-phase reference genome compression algorithm using optimal k-mer length (RGCOK). Reference-based compression takes advantage of the inter-similarity between chromosomes of the same species. RGCOK achieves this by finding the optimal k-mer length for matching, using a randomization method and hashing. The performance of RGCOK was evaluated on three different benchmark data sets: novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), Homo sapiens, and other species sequences using an Amazon AWS virtual cloud machine. Experiments showed that the optimal k-mer finding time by RGCOK is around 45.28 min, whereas the time for existing state-of-the-art algorithms HiRGC, SCCG, and HRCM ranges from 58 min to 8.97 h.

Development of an accurate and rapid method for whole genome characterization of canine parvovirus

Canine parvovirus is a highly contagious pathogen affecting domestic dogs and other carnivores globally. Monitoring CPV through continuous genomic surveillance is crucial for mapping variability and developing effective control measures. Here, we developed a method using multiplex-PCR-next-generation sequencing to obtain full-length CPV genomes directly from clinical samples. This approach utilizes tiling and tailed amplicons to amplify overlapping fragments of roughly 250 base pairs. This enables the creation of Illumina libraries by conducting two PCR reaction runs. We tested the assay in 10 fecal samples from dogs diagnosed with CPV and one CPV-2 vaccine strain. Furthermore, we applied it to a feline sample previously diagnosed with the feline panleukopenia virus. The assay provided 100 % genome coverage and high sequencing depth across all 12 samples. It successfully provided the sequence of the coding regions and the left and right non-translated regions, including tandem and terminal repeats. The assay effectively amplified viral variants from divergent evolutionary groups, including the antigenic variants (2a, 2b, and 2c) and the ancestral CPV-2 strain included in vaccine formulations. Moreover, it successfully amplified the entire genome of the feline panleukopenia virus found in cat feces. This method is cost-effective, time-efficient, and does not require lab expertise in Illumina library preparation. The multiplex-PCR-next-generation methodology facilitates large-scale genomic sequencing, expanding the limited number of complete genomes currently available in databases and enabling real-time genomic surveillance. Furthermore, the method helps identify and track emerging CPV viral variants, facilitating molecular epidemiology and control. Adopting this approach can enhance our understanding of the evolution and genetic diversity of Protoparvovirus carnivoran1.

Exploring the genetic diversity of the Japanese population: Insights from a large-scale whole genome sequencing analysis.

The Japanese archipelago is a terminal location for human migration, and the contemporary Japanese people represent a unique population whose genomic diversity has been shaped by multiple migrations from Eurasia. We analyzed the genomic characteristics that define the genetic makeup of the modern Japanese population from a population genetics perspective from the genomic data of 9,287 samples obtained by high-coverage whole-genome sequencing (WGS) by the National Center Biobank Network. The dataset comprised populations from the Ryukyu Islands and other parts of the Japanese archipelago (Hondo). The Hondo population underwent two episodes of population decline during the Jomon period, corresponding to the Late Neolithic, and the Edo period, corresponding to the Early Modern era, while the Ryukyu population experienced a population decline during the shell midden period of the Late Neolithic in this region. Haplotype analysis suggested increased allele frequencies for genes related to alcohol and fatty acid metabolism, which were reported as loci that had experienced positive natural selection. Two genes related to alcohol metabolism were found to be 12,500 years out of phase with the time when they began to increase in the allele frequency; this finding indicates that the genomic diversity of Japanese people has been shaped by events closely related to agriculture and food production.

PMFFRC: a large-scale genomic short reads compression optimizer via memory modeling and redundant clustering

BackgroundGenomic sequencing reads compressors are essential for balancing high-throughput sequencing short reads generation speed, large-scale genomic data sharing, and infrastructure storage expenditure. However, most existing short reads compressors rarely utilize big-memory systems and duplicative information between diverse sequencing files to achieve a higher compression ratio for conserving reads data storage space.ResultsWe employ compression ratio as the optimization objective and propose a large-scale genomic sequencing short reads data compression optimizer, named PMFFRC, through novelty memory modeling and redundant reads clustering technologies. By cascading PMFFRC, in 982 GB fastq format sequencing data, with 274 GB and 3.3 billion short reads, the state-of-the-art and reference-free compressors HARC, SPRING, Mstcom, and FastqCLS achieve 77.89%, 77.56%, 73.51%, and 29.36% average maximum compression ratio gains, respectively. PMFFRC saves 39.41%, 41.62%, 40.99%, and 20.19% of storage space sizes compared with the four unoptimized compressors.ConclusionsPMFFRC rational usage big-memory of compression server, effectively saving the sequencing reads data storage space sizes, which relieves the basic storage facilities costs and community sharing transmitting overhead. Our work furnishes a novel solution for improving sequencing reads compression and saving storage space. The proposed PMFFRC algorithm is packaged in a same-name Linux toolkit, available un-limited at https://github.com/fahaihi/PMFFRC.

Global phylogenomic diversity of Brucella abortus: spread of a dominant lineage

Brucella abortus is a globally important zoonotic pathogen largely found in cattle hosts and is typically transmitted to humans through contaminated dairy products or contact with diseased animals. Despite the long, shared history of cattle and humans, little is known about how trade in cattle has spread this pathogen throughout the world. Whole genome sequencing provides unparalleled resolution to investigate the global evolutionary history of a bacterium such as B. abortus by providing phylogenetic resolution that has been unobtainable using other methods. We report on large-scale genome sequencing and analysis of B. abortus collected globally from cattle and 16 other hosts from 52 countries. We used single nucleotide polymorphisms (SNPs) to identify genetic variation in 1,074 B. abortus genomes and using maximum parsimony generated a phylogeny that identified four major clades. Two of these clades, clade A (median date 972 CE; 95% HPD, 781–1142 CE) and clade B (median date 150 BCE; 95% HPD, 515 BCE–164 CE), were exceptionally diverse for this species and are exclusively of African origin where provenance is known. The third clade, clade C (median date 949 CE; 95% HPD, 766–1102 CE), had most isolates coming from a broad swath of the Middle East, Europe, and Asia, also had relatively high diversity. Finally, the fourth major clade, clade D (median date 1467 CE; 95% HPD, 1367–1553 CE) comprises the large majority of genomes in a dominant but relatively monomorphic group that predominantly infects cattle in Europe and the Americas. These data are consistent with an African origin for B. abortus and a subsequent spread to the Middle East, Europe, and Asia, probably through the movement of infected cattle. We hypothesize that European arrival to the Americas starting in the 15th century introduced B. abortus from Western Europe through the introduction of a few common cattle breeds infected with strains from clade D. These data provide the foundation of a comprehensive global phylogeny of this important zoonotic pathogen that should be an important resource in human and veterinary epidemiology.

A Longitudinal Single-Cell Atlas of Treatment Response in Pediatric AML

Introduction: Pediatric acute myeloid leukemia (pAML) is a heterogeneous disease in terms of driver alterations, treatment response and patient outcomes. Although approximately 90% of pAML patients respond to initial treatment, tumours relapse frequently. Despite large-scale genomic sequencing efforts, it remains elusive why tumors relapse. Previously, mutations have been identified that affect transcriptional regulation, indicating that epigenetic processes may contribute to treatment resistance in pAML. We therefore studied how tumor epigenetic patterns, transcriptional regulation and cell populations change over the course of treatment using single-cell ATAC sequencing (scATAC-seq) and single-cell RNA sequencing (scRNA-seq) in pediatric populations. Methods: We profiled 330,047 cells using scRNA-seq and 353,984 cells using scATAC-seq derived from 28 uniformly treated pAML patients, driven by either MLL fusions, CBFB fusions, RUNX1 fusions, FLT3-ITD or other aberrations (i.e. CEBPA mutations). All patients were enrolled in the AAML1031 trial (https://childrensoncologygroup.org/aaml1031), a randomized phase 3 clinical trial that investigated the addition of Bortezomib (BTZ) and Sorafenib to standard treatment modalities. Samples were obtained serially at diagnosis, at remission and at relapse and single-cell profiles were compared between these stages. Results: We identified malignant and non-malignant populations in the scATAC-seq and scRNA-seq data, showing that in all cases, malignant cells from patients at diagnosis were distinct from those taken at relapse. Major differences between diagnosis and relapse were associated with differentiation, and by inferring distinct cell populations we identified that relapsed cell populations appeared to shift towards a more primitive state. The extent of this shift was dependent on the genetic subtype and the effects were more pronounced in MLL-rearranged tumors. To identify the candidate mechanisms and transcription factors involved in this shift we studied the monocytic, lymphocytic and erythrocytic lineages in non-malignant cells and compared these to the tumor at diagnosis and relapse. Based on motif enrichment we found that across all different subgroups, transcription factors (TFs) that drive monocytic differentiation (i.e. CEBPA) have a lower activity while TFs driving erythrocytic and lymphocytic differentiation (i.e. TCF3) have a higher activity. Analysis of hematopoietic stem-like and progenitor-like cells (HSPC-like) confirmed this change, indicating that the “priming” towards other lineages is not only a result of shifts in cell populations but also a result of rewiring of early progenitors. To investigate this rewiring, we analysed regions of open chromatin that were enriched in malignant HSPCs compared to normal HSPCs across different subgroups. We found that those regions were highly specific to the driving alteration and were enriched for MEF2C binding sites in MLL-driven tumors and AP-1 binding sites in other tumors. Overall, upon relapse MEF2C expression and inferred TF activity appeared to increase, coinciding with an apparent increase in lymphoid priming and early lymphoid factors including LMO2, LYL1 and TCF3, among others. Two cases in the cohort appeared to switch lineage from a monocytic to lymphocytic phenotype and were enriched in CD19 and CD79A positive malignant cells upon relapse. Both cases shared the characteristic that MEF2C was upregulated upon relapse and showed upregulation of typical B-lymphoid transcription factors such as PAX5. Discussion: By generating a large dataset of pediatric AML patient samples collected at diagnosis, remission and relapse, we have identified changes in cellular hierarchies and transcriptional networks that occur upon relapse. Our study shows that shifts in cell populations readily occur upon treatment and are dependent on subgroup-defining alterations. Particularly MLL-rearranged tumors show a high level of plasticity that could explain the poor outcome of this group. Furthermore, we show that tumors appear to adapt their transcriptional programs towards a more “stem-like” state, with a likely higher propensity to differentiate towards other lineages such as the lymphoid lineage. We have identified MEF2C as a possible regulator of these processes, opening a new perspective to target the plasticity of these tumors.

Clinical study of ferredoxin-reductase-related mitochondriopathy: Genotype-phenotype correlation and proposal of ancestry-based carrier screening in the Mexican population

PurposeFerredoxin reductase (FDXR) is a flavoprotein that functions in both iron sulfur cluster biogenesis and steroid biosynthesis pathways in the mitochondria. Not surprisingly, loss of FDXR function causes severe mitochondrial diseases in humans. Although several FDXR-related mitochondriopathy (FRM) cohorts have been reported in the literature, further characterization of the natural history of FRM is warranted. MethodsTo better understand the spectrum of FRM, a natural history study of FRM was performed. New cases were added to previously reported FRM cases for analysis (n = 62 cases). ResultsOptic atrophy, movement disorder, and developmental delay were frequent findings. Mortality is high, with 18% of patients, often infants, passing from complications. Notably, 25% of cases were homozygous or compound heterozygous for the previously reported p.Arg386Trp “hotspot” variant. Of the obtained ancestry, all but 1 individual heterozygous for the p.Arg386Trp variant was Hispanic, with many reporting Mexican heritage. Utilizing recent large-scale genome sequencing surveys, the carrier frequency of the p.Arg386Trp variant was estimated as 1 of 185 in the Mexican population. ConclusionGiven the high mortality of FRM and carrier frequency of the common variant, consideration of a new approach for population carrier screening and development of therapeutics for affected individuals is needed.

Genomic and Epidemiologic Surveillance of SARS-CoV-2 in the Pandemic Period: Sequencing Network of the Lazio Region, Italy.

Since the beginning of the COVID-19 pandemic, large-scale genomic sequencing has immediately pointed out that SARS-CoV-2 has rapidly mutated during the course of the pandemic, resulting in the emergence of variants with a public health impact. In this context, strictly monitoring the circulating strains via NGS has proven to be crucial for the early identification of new emerging variants and the study of the genomic evolution and transmission of SARS-CoV-2. Following national and international guidelines, the Lazio region has created a sequencing laboratory network (WGSnet-Lazio) that works in synergy with the reference center for epidemiological surveillance (SERESMI) to monitor the circulation of SARS-CoV-2. Sequencing was carried out with the aims of characterizing outbreak transmission dynamics, performing the genomic analysis of viruses infecting specific categories of patients (i.e., immune-depressed, travelers, and people with severe symptoms) and randomly monitoring variant circulation. Here we report data emerging from sequencing activities carried out by WGSnet-Lazio (from February 2020 to October 2022) linked with epidemiological data to correlate the circulation of variants with the clinical and demographic characteristics of patients. The model of the sequencing network developed in the Lazio region proved to be a useful tool for SARS-CoV-2 surveillance and to support public health measures for epidemic containment.

The Hong Kong genome project: building genome sequencing capacity and capability for advancing genomic science in Hong Kong

Aim: The Hong Kong Genome Project (HKGP) is the first large-scale genome sequencing (GS) project in the Hong Kong Special Administrative Region. The Hong Kong Genome Institute (HKGI) is entrusted with the task of implementing the HKGP. With the aim to sequence 45,000-50,000 genomes in five years, it is the project’s goal to provide participants with more precise diagnosis and personalised treatment, and to drive the application and integration of genomic medicine into routine clinical care. Methods: The HKGI Laboratory’s hardware and software components were customised to tailor to the needs of the project. Sample handling and storage protocol, DNA extraction, and PCR-free GS workflow were developed and optimised. Quality control indicators and metrics for assessing the quality of samples, sequencing libraries and sequencing data were established. Results: The Laboratory is designed to facilitate a unidirectional GS workflow to minimise the risk of contamination. The Sample Manager system handles laboratory data generated from the HKGP samples and biobank. The Laboratory handles and analyses approximately 350-500 samples per week, the majority of which are whole blood. During the first 24 months since the launch of the HKGP, 12,937 participants and their family members (6,680 genomes) have been recruited and sequenced. The sequencing capacity of the Laboratory has been further enhanced to include the latest technologies, such as long-read sequencing and multi-omics in order to meet the target of the HKGP. Conclusion: HKGI Laboratory established a robust GS workflow for the HKGP. The clinical utility of GS will bring precision medicine into routine clinical practice.

Data-adaptive and pathway-based tests for association studies between somatic mutations and germline variations in human cancers.

Cancer is a disease driven by a combination of inherited genetic variants and somatic mutations. Recently available large-scale sequencing data of cancer genomes have provided an unprecedented opportunity to study the interactions between them. However, previous studies on this topic have been limited by simple, low statistical power tests such as Fisher's exact test. In this paper, we design data-adaptive and pathway-based tests based on the score statistic for association studies between somatic mutations and germline variations. Previous research has shown that two single-nucleotide polymorphism(SNP)-set-based association tests, adaptive sum of powered score (aSPU) and data-adaptive pathway-based(aSPUpath) tests, increase the power in genome-wide association studies (GWASs) with a single disease trait in a case-control study. We extend aSPU and aSPUpath to multi-traits, that is, somatic mutations of multiple genes in a cohort study, allowing extensive information aggregation at both SNP and gene levels. -values from different parameters assuming varying genetic architecture are combined to yield data-adaptive tests for somatic mutations and germline variations. Extensive simulations show that, in comparison with some commonly used methods, our data-adaptive somatic mutations/germline variations tests can be applied to multiple germline SNPs/genes/pathways, and generally have much higher statistical powers while maintaining the appropriate type I error. The proposed tests are applied to a large-scale real-world International Cancer Genome Consortiumwhole genome sequencing data set of 2583 subjects, detecting more significant and biologically relevant associations compared with the other existing methods on both gene and pathway levels. Our study has systematically identified the associations between various germline variations and somatic mutations across different cancer types, which potentially provides valuable utility for cancer risk prediction, prognosis, and therapeutics.