The broken Alzheimer's disease genome.

The complex pathobiology of late-onset Alzheimer's disease (AD) poses significant challenges to therapeutic and preventative interventions. Despite these difficulties, genomics and related disciplines are allowing fundamental mechanistic insights to emerge with clarity, particularly with the introduction of high-resolution sequencing technologies. After all, the disrupted processes at the interface between DNA and gene expression, which we call the broken AD genome, offer detailed quantitative evidence unrestrained by preconceived notions about the disease. In addition to highlighting biological pathways beyond the classical pathology hallmarks, these advances have revitalized drug discovery efforts and are driving improvements in clinical tools. We review genetic, epigenomic, and gene expression findings related to AD pathogenesis and explore how their integration enables a better understanding of the multicellular imbalances contributing to this heterogeneous condition. The frontiers opening on the back of these research milestones promise a future of AD care that is both more personalized and predictive.

Pivotal functions and impact of long con-coding RNAs on cellular processes and genome integrity.

Recent advances in uncovering the mysteries of the human genome suggest that long non-coding RNAs (lncRNAs) are important regulatory components. Although lncRNAs are known to affect gene transcription, their mechanisms and biological implications are still unclear. Experimental research has shown that lncRNA synthesis, subcellular localization, and interactions with macromolecules like DNA, other RNAs, or proteins can all have an impact on gene expression in various biological processes. In this review, we highlight and discuss the major mechanisms through which lncRNAs function as master regulators of the human genome. Specifically, the objective of our review is to examine how lncRNAs regulate different processes like cell division, cell cycle, and immune responses, and unravel their roles in maintaining genomic architecture and integrity.

Identification of Host Factors of Paramyxoviruses by siRNA Genome-Wide Screens.

Measles is a highly infectious disease that continues to spread mainly in developing countries, often resulting in child mortality. Despite the existence of effective vaccines, no specific antivirals are available as targeted therapy to combat measles virus (MeV). The implementation of genome-wide siRNA screens can provide a powerful platform to discover host factors that mediate MeV infection and replication, which could be essential to develop novel therapeutic strategies against this disease. Here, we describe a human genome-wide siRNA screen for MeV.

Recurrent neural network for predicting absence of heterozygosity from low pass WGS with ultra-low depth.

BACKGROUND: The absence of heterozygosity (AOH) is a kind of genomic change characterized by a long contiguous region of homozygous alleles in a chromosome, which may cause human genetic disorders. However, no method of low-pass whole genome sequencing (LP-WGS) has been reported for the detection of AOH in a low-pass setting of less than onefold. We developed a method, termed CNVseq-AOH, for predicting the absence of heterozygosity using LP-WGS with ultra-low sequencing data, which overcomes the sparse nature of typical LP-WGS data by combing population-based haplotype information, adjustable sliding windows, and recurrent neural network (RNN). We tested the feasibility of CNVseq-AOH for the detection of AOH in 409 cases (11 AOH regions for model training and 863 AOH regions for validation) from the 1000 Genomes Project (1KGP). AOH detection using CNVseq-AOH was also performed on 6 clinical cases with previously ascertained AOHs by whole exome sequencing (WES). RESULTS: Using SNP-based microarray results as reference (AOHs detected by CNVseq-AOH with at least a 50% overlap with the AOHs detected by chromosomal microarray analysis), 409 samples (863 AOH regions) in the 1KGP were used for concordant analysis. For 784 AOHs on autosomes and 79 AOHs on the X chromosome, CNVseq-AOH can predict AOHs with a concordant rate of 96.23% and 59.49% respectively based on the analysis of 0.1-fold LP-WGS data, which is far lower than the current standard in the field. Using 0.1-fold LP-WGS data, CNVseq-AOH revealed 5 additional AOHs (larger than 10 Mb in size) in the 409 samples. We further analyzed AOHs larger than 10 Mb, which is recommended for reporting the possibility of UPD. For the 291 AOH regions larger than 10 Mb, CNVseq-AOH can predict AOHs with a concordant rate of 99.66% with only 0.1-fold LP-WGS data. In the 6 clinical cases, CNVseq-AOH revealed all 15 known AOH regions. CONCLUSIONS: Here we reported a method for analyzing LP-WGS data to accurately identify regions of AOH, which possesses great potential to improve genetic testing of AOH.

Novel genome-wide DNA methylation profiling reveals distinct epigenetic landscape, prognostic model and cellular composition of early-stage lung adenocarcinoma.

BACKGROUND: Lung adenocarcinoma (LUAD) has been a leading cause of cancer-related mortality worldwide. Early intervention can significantly improve prognosis. DNA methylation could occur in the early stage of tumor. Comprehensive understanding the epigenetic landscape of early-stage LUAD is crucial in understanding tumorigenesis. METHODS: Enzymatic methyl sequencing (EM-seq) was performed on 23 tumors and paired normal tissue to reveal distinct epigenetic landscape, for compared with The Cancer Genome Atlas (TCGA) 450K methylation microarray data. Then, an integrative analysis was performed combined with TCGA LUAD RNA-seq data to identify significant differential methylated and expressed genes. Subsequently, the prognostic risk model was constructed and cellular composition was analyzed. RESULTS: Methylome analysis of EM-seq comparing tumor and normal tissues identified 25 million cytosine-phosphate-guanine (CpG) sites and 30,187 differentially methylated regions (DMR) with a greater number of untraditional types. EM-seq identified a significantly higher number of CpG sites and DMRs compared to the 450K microarray. By integrating the differentially methylated genes (DMGs) with LUAD-related differentially expressed genes (DEGs) from the TCGA database, we constructed prognostic model based on six differentially methylated-expressed genes (MEGs) and verified our prognostic model in GSE13213 and GSE42127 dataset. Finally, cell deconvolution based on the in-house EM-seq methylation profile was used to estimate cellular composition of early-stage LUAD. CONCLUSIONS: This study firstly delves into novel pattern of epigenomic DNA methylation and provides a multidimensional analysis of the role of DNA methylation revealed by EM-seq in early-stage LUAD, providing distinctive insights into its potential epigenetic mechanisms.

Genomic context- and H2AK119 ubiquitination-dependent inheritance of human Polycomb silencing.

Polycomb repressive complexes 1 and 2 (PRC1 and 2) are required for heritable repression of developmental genes. The cis- and trans-acting factors that contribute to epigenetic inheritance of mammalian Polycomb repression are not fully understood. Here, we show that, in human cells, ectopically induced Polycomb silencing at initially active developmental genes, but not near ubiquitously expressed housekeeping genes, is inherited for many cell divisions. Unexpectedly, silencing is heritable in cells with mutations in the H3K27me3 binding pocket of the Embryonic Ectoderm Development (EED) subunit of PRC2, which are known to disrupt H3K27me3 recognition and lead to loss of H3K27me3. This mode of inheritance is less stable and requires intact PRC2 and recognition of H2AK119ub1 by PRC1. Our findings suggest that maintenance of Polycomb silencing is sensitive to local genomic context and can be mediated by PRC1-dependent H2AK119ub1 and PRC2 independently of H3K27me3 recognition.

Annotation of nuclear lncRNAs based on chromatin interactions.

The human genome is pervasively transcribed and produces a wide variety of long non-coding RNAs (lncRNAs), constituting the majority of transcripts across human cell types. Some specific nuclear lncRNAs have been shown to be important regulatory components acting locally. As RNA-chromatin interaction and Hi-C chromatin conformation data showed that chromatin interactions of nuclear lncRNAs are determined by the local chromatin 3D conformation, we used Hi-C data to identify potential target genes of lncRNAs. RNA-protein interaction data suggested that nuclear lncRNAs act as scaffolds to recruit regulatory proteins to target promoters and enhancers. Nuclear lncRNAs may therefore play a role in directing regulatory factors to locations spatially close to the lncRNA gene. We provide the analysis results through an interactive visualization web portal at https://fantom.gsc.riken.jp/zenbu/reports/#F6_3D_lncRNA.

Understanding the genomic heterogeneity of North African Imazighen: from broad to microgeographical perspectives.

The strategic location of North Africa has led to cultural and demographic shifts, shaping its genetic structure. Historical migrations brought different genetic components that are evident in present-day North African genomes, along with autochthonous components. The Imazighen (plural of Amazigh) are believed to be the descendants of autochthonous North Africans and speak various Amazigh languages, which belong to the Afro-Asiatic language family. However, the arrival of different human groups, especially during the Arab conquest, caused cultural and linguistic changes in local populations, increasing their heterogeneity. We aim to characterize the genetic structure of the region, using the largest Amazigh dataset to date and other reference samples. Our findings indicate microgeographical genetic heterogeneity among Amazigh populations, modeled by various admixture waves and different effective population sizes. A first admixture wave is detected group-wide around the twelfth century, whereas a second wave appears in some Amazigh groups around the nineteenth century. These events involved populations with higher genetic ancestry from south of the Sahara compared to the current North Africans. A plausible explanation would be the historical trans-Saharan slave trade, which lasted from the Roman times to the nineteenth century. Furthermore, our investigation shows that assortative mating in North Africa has been rare.

Single-cell 3D genome structure reveals distinct human pluripotent states.

BACKGROUND: Pluripotent states of embryonic stem cells (ESCs) with distinct transcriptional profiles affect ESC differentiative capacity and therapeutic potential. Although single-cell RNA sequencing has revealed additional subpopulations and specific features of naive and primed human pluripotent stem cells (hPSCs), the underlying mechanisms that regulate their specific transcription and that control their pluripotent states remain elusive. RESULTS: By single-cell analysis of high-resolution, three-dimensional (3D) genomic structure, we herein demonstrate that remodeling of genomic structure is highly associated with the pluripotent states of human ESCs (hESCs). The naive pluripotent state is featured with specialized 3D genomic structures and clear chromatin compartmentalization that is distinct from the primed state. The naive pluripotent state is achieved by remodeling the active euchromatin compartment and reducing chromatin interactions at the nuclear center. This unique genomic organization is linked to enhanced chromatin accessibility on enhancers and elevated expression levels of naive pluripotent genes localized to this region. In contradistinction, the primed state exhibits intermingled genomic organization. Moreover, active euchromatin and primed pluripotent genes are distributed at the nuclear periphery, while repressive heterochromatin is densely concentrated at the nuclear center, reducing chromatin accessibility and the transcription of naive genes. CONCLUSIONS: Our data provide insights into the chromatin structure of ESCs in their naive and primed states, and we identify specific patterns of modifications in transcription and chromatin structure that might explain the genes that are differentially expressed between naive and primed hESCs. Thus, the inversion or relocation of heterochromatin to euchromatin via compartmentalization is related to the regulation of chromatin accessibility, thereby defining pluripotent states and cellular identity.

Mutation rate heterogeneity at the sub-gene scale due to local DNA hypomethylation.

Local mutation rates in human are highly heterogeneous, with known variability at the scale of megabase-sized chromosomal domains, and, on the other extreme, at the scale of oligonucleotides. The intermediate, kilobase-scale heterogeneity in mutation risk is less well characterized. Here, by analyzing thousands of somatic genomes, we studied mutation risk gradients along gene bodies, representing a genomic scale spanning roughly 1-10 kb, hypothesizing that different mutational mechanisms are differently distributed across gene segments. The main heterogeneity concerns several kilobases at the transcription start site and further downstream into 5' ends of gene bodies; these are commonly hypomutated with several mutational signatures, most prominently the ubiquitous C > T changes at CpG dinucleotides. The width and shape of this mutational coldspot at 5' gene ends is variable across genes, and corresponds to variable interval of lowered DNA methylation depending on gene activity level and regulation. Such hypomutated loci, at 5' gene ends or elsewhere, correspond to DNA hypomethylation that can associate with various landmarks, including intragenic enhancers, Polycomb-marked regions, or chromatin loop anchor points. Tissue-specific DNA hypomethylation begets tissue-specific local hypomutation. Of note, direction of mutation risk is inverted for AID/APOBEC3 cytosine deaminase activity, whose signatures are enriched in hypomethylated regions.

Identifying human pre-mRNA cleavage and polyadenylation factors by genome-wide CRISPR screens using a dual fluorescence readthrough reporter.

Messenger RNA precursors (pre-mRNA) generally undergo 3' end processing by cleavage and polyadenylation (CPA), which is specified by a polyadenylation site (PAS) and adjacent RNA sequences and regulated by a large variety of core and auxiliary CPA factors. To date, most of the human CPA factors have been discovered through biochemical and proteomic studies. However, genetic identification of the human CPA factors has been hampered by the lack of a reliable genome-wide screening method. We describe here a dual fluorescence readthrough reporter system with a PAS inserted between two fluorescent reporters. This system enables measurement of the efficiency of 3' end processing in living cells. Using this system in combination with a human genome-wide CRISPR/Cas9 library, we conducted a screen for CPA factors. The screens identified most components of the known core CPA complexes and other known CPA factors. The screens also identified CCNK/CDK12 as a potential core CPA factor, and RPRD1B as a CPA factor that binds RNA and regulates the release of RNA polymerase II at the 3' ends of genes. Thus, this dual fluorescence reporter coupled with CRISPR/Cas9 screens reliably identifies bona fide CPA factors and provides a platform for investigating the requirements for CPA in various contexts.

AlphaFun: Structural-Alignment-Based Proteome Annotation Reveals why the Functionally Unknown Proteins (uPE1) Are So Understudied.

With the rapid expansion of sequencing of genomes, the functional annotation of proteins becomes a bottleneck in understanding proteomes. The Chromosome-centric Human Proteome Project (C-HPP) aims to identify all proteins encoded by the human genome and find functional annotations for them. However, until now there are still 1137 identified human proteins without functional annotation, called uPE1 proteins. Sequence alignment was insufficient to predict their functions, and the crystal structures of most proteins were unavailable. In this study, we demonstrated a new functional annotation strategy, AlphaFun, based on structural alignment using deep-learning-predicted protein structures. Using this strategy, we functionally annotated 99% of the human proteome, including the uPE1 proteins and missing proteins, which have not been identified yet. The accuracy of the functional annotations was validated using the known-function proteins. The uPE1 proteins shared similar functions to the known-function PE1 proteins and tend to express only in very limited tissues. They are evolutionally young genes and thus should conduct functions only in specific tissues and conditions, limiting their occurrence in commonly studied biological models. Such functional annotations provide hints for functional investigations on the uPE1 proteins. This proteome-wide-scale functional annotation strategy is also applicable to any other species.

Critical assessment of variant prioritization methods for rare disease diagnosis within the rare genomes project.

BACKGROUND: A major obstacle faced by families with rare diseases is obtaining a genetic diagnosis. The average "diagnostic odyssey" lasts over five years and causal variants are identified in under 50%, even when capturing variants genome-wide. To aid in the interpretation and prioritization of the vast number of variants detected, computational methods are proliferating. Knowing which tools are most effective remains unclear. To evaluate the performance of computational methods, and to encourage innovation in method development, we designed a Critical Assessment of Genome Interpretation (CAGI) community challenge to place variant prioritization models head-to-head in a real-life clinical diagnostic setting. METHODS: We utilized genome sequencing (GS) data from families sequenced in the Rare Genomes Project (RGP), a direct-to-participant research study on the utility of GS for rare disease diagnosis and gene discovery. Challenge predictors were provided with a dataset of variant calls and phenotype terms from 175 RGP individuals (65 families), including 35 solved training set families with causal variants specified, and 30 unlabeled test set families (14 solved, 16 unsolved). We tasked teams to identify causal variants in as many families as possible. Predictors submitted variant predictions with estimated probability of causal relationship (EPCR) values. Model performance was determined by two metrics, a weighted score based on the rank position of causal variants, and the maximum F-measure, based on precision and recall of causal variants across all EPCR values. RESULTS: Sixteen teams submitted predictions from 52 models, some with manual review incorporated. Top performers recalled causal variants in up to 13 of 14 solved families within the top 5 ranked variants. Newly discovered diagnostic variants were returned to two previously unsolved families following confirmatory RNA sequencing, and two novel disease gene candidates were entered into Matchmaker Exchange. In one example, RNA sequencing demonstrated aberrant splicing due to a deep intronic indel in ASNS, identified in trans with a frameshift variant in an unsolved proband with phenotypes consistent with asparagine synthetase deficiency. CONCLUSIONS: Model methodology and performance was highly variable. Models weighing call quality, allele frequency, predicted deleteriousness, segregation, and phenotype were effective in identifying causal variants, and models open to phenotype expansion and non-coding variants were able to capture more difficult diagnoses and discover new diagnoses. Overall, computational models can significantly aid variant prioritization. For use in diagnostics, detailed review and conservative assessment of prioritized variants against established criteria is needed.

Human Vault RNAs: Exploring Their Potential Role in Cellular Metabolism.

Non-coding RNAs have been described as crucial regulators of gene expression and guards of cellular homeostasis. Some recent papers focused on vault RNAs, one of the classes of non-coding RNA, and their role in cell proliferation, tumorigenesis, apoptosis, cancer response to therapy, and autophagy, which makes them potential therapy targets in oncology. In the human genome, four vault RNA paralogues can be distinguished. They are associated with vault complexes, considered the largest ribonucleoprotein complexes. The protein part of these complexes consists of a major vault protein (MVP) and two minor vault proteins (vPARP and TEP1). The name of the complex, as well as vault RNA, comes from the hollow barrel-shaped structure that resembles a vault. Their sequence and structure are highly evolutionarily conserved and show many similarities in comparison with different species, but vault RNAs have various roles. Vaults were discovered in 1986, and their functions remained unclear for many years. Although not much is known about their contribution to cell metabolism, it has become clear that vault RNAs are involved in various processes and pathways associated with cancer progression and modulating cell functioning in normal and pathological stages. In this review, we discuss known functions of human vault RNAs in the context of cellular metabolism, emphasizing processes related to cancer and cancer therapy efficacy.

Recent Developments in the Regulation of Heritable Human Genome Editing.

In 2018, the Chinese scientist He Jiankui presented his research at the Second International Summit on Human Genome Editing in Hong Kong. While it was intended that he facilitate a workshop, he was instead called on to present his research in heritable human genome editing, where he made the announcement that he had taken great strides in advancement of his research, to the extent that he had gene-edited human embryos and that this had resulted in the live births of two children. While his research ethic and methodology was interrogated, he insisted that two children, twin girls, had been born healthy and that there was another pregnancy (at the time) where birth of a third gene edited child would be imminent. This announcement generated a ripple effect in the scientific community and exposed the gaps in regulation and absence of law relating to the technology. This resulted in a flurry of activity and conversation around regulation of the technology, which scientists stated was not ready for human trials. This article reviews the Third Summit which was held in London in March 2023 and comments on the latest developments in the regulation of heritable human genome editing.

VarChat: the generative AI assistant for the interpretation of human genomic variations.

MOTIVATION: In the modern era of genomic research, the scientific community is witnessing an explosive growth in the volume of published findings. While this abundance of data offers invaluable insights, it also places a pressing responsibility on genetic professionals and researchers to stay informed about the latest findings and their clinical significance. Genomic variant interpretation is currently facing a challenge in identifying the most up-to-date and relevant scientific papers, while also extracting meaningful information to accelerate the process from clinical assessment to reporting. Computer-aided literature search and summarization can play a pivotal role in this context. By synthesizing complex genomic findings into concise, interpretable summaries, this approach facilitates the translation of extensive genomic datasets into clinically relevant insights. RESULTS: To bridge this gap, we present VarChat (varchat.engenome.com), an innovative tool based on generative AI, developed to find and summarize the fragmented scientific literature associated with genomic variants into brief yet informative texts. VarChat provides users with a concise description of specific genetic variants, detailing their impact on related proteins and possible effects on human health. In addition, VarChat offers direct links to related scientific trustable sources, and encourages deeper research. AVAILABILITY AND IMPLEMENTATION: varchat.engenome.com.

sOCP: a framework predicting smORF coding potential based on TIS and in-frame features and effectively applied in the human genome.

Small open reading frames (smORFs) have been acknowledged to play various roles on essential biological pathways and affect human beings from diabetes to tumorigenesis. Predicting smORFs in silico is quite a prerequisite for processing the omics data. Here, we proposed the smORF-coding-potential-predicting framework, sOCP, which provides functions to construct a model for predicting novel smORFs in some species. The sOCP model constructed in human was based on in-frame features and the nucleotide bias around the start codon, and the small feature subset was proved to be competent enough and avoid overfitting problems for complicated models. It showed more advanced prediction metrics than previous methods and could correlate closely with experimental evidence in a heterogeneous dataset. The model was applied to Rattus norvegicus and exhibited satisfactory performance. We then scanned smORFs with ATG and non-ATG start codons from the human genome and generated a database containing about a million novel smORFs with coding potential. Around 72 000 smORFs are located on the lncRNA regions of the genome. The smORF-encoded peptides may be involved in biological pathways rare for canonical proteins, including glucocorticoid catabolic process and the prokaryotic defense system. Our work provides a model and database for human smORF investigation and a convenient tool for further smORF prediction in other species.

STRIDE-DB: a comprehensive database for exploration of instability and phenotypic relevance of short tandem repeats in the human genome.

Short Tandem Repeats (STRs) are genetic markers made up of repeating DNA sequences. The variations of the STRs are widely studied in forensic analysis, population studies and genetic testing for a variety of neuromuscular disorders. Understanding polymorphic STR variation and its cause is crucial for deciphering genetic information and finding links to various disorders. In this paper, we present STRIDE-DB, a novel and unique platform to explore STR Instability and its Phenotypic Relevance, and a comprehensive database of STRs in the human genome. We utilized RepeatMasker to identify all the STRs in the human genome (hg19) and combined it with frequency data from the 1000 Genomes Project. STRIDE-DB, a user-friendly resource, plays a pivotal role in investigating the relationship between STR variation, instability and phenotype. By harnessing data from genome-wide association studies (GWAS), ClinVar database, Alu loci, Haploblocks in genome and Conservation of the STRs, it serves as an important tool for researchers exploring the variability of STRs in the human genome and its direct impact on phenotypes. STRIDE-DB has its broad applicability and significance in various research domains like forensic sciences and other repeat expansion disorders. Database URL: https://stridedb.igib.res.in.

Multilocus pathogenic variants contribute to intrafamilial clinical heterogeneity: a retrospective study of sibling pairs with neurodevelopmental disorders.

BACKGROUND: Multilocus pathogenic variants (MPVs) are genetic changes that affect multiple gene loci or regions of the genome, collectively leading to multiple molecular diagnoses. MPVs may also contribute to intrafamilial phenotypic variability between affected individuals within a nuclear family. In this study, we aim to gain further insights into the influence of MPVs on a disease manifestation in individual research subjects and explore the complexities of the human genome within a familial context. METHODS: We conducted a systematic reanalysis of exome sequencing data and runs of homozygosity (ROH) regions of 47 sibling pairs previously diagnosed with various neurodevelopmental disorders (NDD). RESULTS: We found siblings with MPVs driven by long ROH regions in 8.5% of families (4/47). The patients with MPVs exhibited significantly higher FROH values (p-value = 1.4e-2) and larger total ROH length (p-value = 1.8e-2). Long ROH regions mainly contribute to this pattern; the siblings with MPVs have a larger total size of long ROH regions than their siblings in all families (p-value = 6.9e-3). Whereas the short ROH regions in the siblings with MPVs are lower in total size compared to their sibling pairs with single locus pathogenic variants (p-value = 0.029), and there are no statistically significant differences in medium ROH regions between sibling pairs (p-value = 0.52). CONCLUSION: This study sheds light on the significance of considering MPVs in families with affected sibling pairs and the role of ROH as an adjuvant tool in explaining clinical variability within families. Identifying individuals carrying MPVs may have implications for disease management, identification of possible disease risks to different family members, genetic counseling and exploring personalized treatment approaches.