Human gene essentiality

Fully haplotyped genome assemblies of healthy individuals reveal variability in 5'ss strength and support by splicing regulatory proteins

This work presents a comprehensive investigation of sequence variations at human 5' splice sites (5'ss), exploring their impact on 5'ss strength and predicted splicing regulatory protein (SRP) binding. Leveraging 44 high-quality genomes, with fully haplotyped assemblies, we were able to fully assess homozygous and heterozygous sequence variations around and within 5'ss. Variations showed differing tolerance levels in protein-coding and non-coding transcripts. Around half of 5'ss variations did not alter 5'ss strength (measured by the HBond score, that estimates binding of the 11 nucleotides at the free U1 spliceosomal RNA 5' end). Heterozygous variations resulted in stronger 5'ss strength reductions and less compensatory effects of multiple sequence variations at the same 5'ss, compared to homozygous variations. Additionally, we observed a slight balance between changes in 5'ss strength and predicted SRP binding. Strong 5'ss (HBond score >18.8) showed strength variations in both directions, as theoretically expected for random distributions, whereas weaker 5'ss consistently showed lower strength reductions than expected or achievable. Variations at 5'ss of essential genes were less frequent than in other genes and showed a higher amount of variations that did not alter 5'ss strength. Acceptable changes in predicted SRP binding sites were highly dependent on their respective 5'ss strength.

Targeting vulnerabilities of aneuploid cells for cancer therapy

Aneuploidy is a common feature of cancer that drives tumor evolution, but it also creates cellular vulnerabilities that might be exploited therapeutically. Recent advances in genomic technologies and experimental models have uncovered diverse cellular consequences of aneuploidy, revealing dependencies on mitotic regulation, DNA replication and repair, proteostasis, metabolism, and immune interactions. Harnessing aneuploidy for precision oncology requires the combination of genomic, functional, and clinical studies that will enable translation of our improved understanding of aneuploidy to targeted therapies. In this review we discuss approaches to targeting both highly aneuploid cells and cells with specific common aneuploidies, summarize the biological underpinning of these aneuploidy-induced vulnerabilities, and explore their therapeutic implications.

Genetic Background and Gene Essentiality

BACKGROUND/OBJECTIVES

Essential genes are those required for an organism's survival and reproduction. However, gene essentiality is not absolute; it can be highly context-dependent, varying across genetic and environmental conditions. Most previous studies have assessed gene essentiality in a single genetic background, limiting our understanding of its variability. The objective of this study was to investigate how genetic background influences gene essentiality in the multicellular model organism Caenorhabditis elegans.

METHODS

We examined gene essentiality in three genetically distinct C. elegans strains: N2, LKC34, and MY16. A total of 294 genes were selected for RNA interference (RNAi) knockdown: 101 previously classified as essential, 175 as nonessential and 18 as conditional (condition-dependent essentiality). Each gene-strain combination was tested in multiple biological and technical replicates, and rigorous quality control and statistical analyses were used to identify strain-specific effects.

RESULTS

Our results demonstrate substantial variation in gene essentiality across genetic backgrounds. Among the 101 genes previously identified as essential in the N2 strain, only 56% were consistently essential in all three strains. We identified 23 genes that were newly essential across all strains, 13 genes essential in two strains, and 9 genes essential in only one strain. These results reveal that a significant proportion of essential genes exhibit strain-dependent essentiality.

CONCLUSIONS

This study underscores the importance of genetic context in determining gene essentiality. Our findings suggest that relying on a single genetic background, such as N2, may lead to an incomplete or misleading view of gene essentiality. Understanding context-dependent gene essentiality has important implications for functional genomics, evolutionary biology, and potentially for translational research where genetic background can modulate phenotypic outcomes.

The Endo-GeneScreen Platform Identifies Drug-Like Probes that Regulate Endogenous Protein Levels within Physiological Contexts

Traditional phenotypic drug discovery platforms have suffered from poor scalability and a lack of mechanistic understanding of newly discovered phenotypic probes. To address this, we created Endo- GeneScreen (EGS), a high-throughput enabled screening platform that identifies bioactive small molecules capable of regulating endogenous protein expression encoded by any preselected target gene within a biologically appropriate context. As a proof-of-concept, EGS successfully identified drug candidates that up-regulate endogenous expression of neuronal Syngap1, a gene that causes a neurodevelopmental disorder when haploinsufficient. For example, SR-1815, a previously unknown and undescribed kinase inhibitor, alleviated major cellular consequences of Syngap1 loss-of-function by restoring normal SynGAP protein levels and dampening neuronal hyperactivity within haploinsufficient neurons. Moreover, we demonstrate that EGS assays accelerate preclinical development of identified drug candidates and facilitate mode-of-action deconvolution studies. Thus, EGS identifies first-in-class bioactive small molecule probes that promote biological discovery and precision therapeutic development.

Identifying colorectal cancer-specific vulnerabilities in the Wnt-driven long non-coding transcriptome

BACKGROUND

Aberrant Wnt pathway activation is a key driver of colorectal cancer (CRC) and is essential to sustain tumour growth and progression. Although the downstream protein-coding target genes of the Wnt cascade are well known, the long non-coding transcriptome has not yet been fully resolved.

OBJECTIVE

In this study, we aim to comprehensively reveal the Wnt-regulated long non-coding transcriptome and exploit essential molecules as novel therapeutic targets.

DESIGN

We used global run-on sequencing to define β-catenin-regulated long non-coding RNAs (lncRNAs) in CRC. CRISPRi dropout screens were subsequently used to establish the functional relevance of a subset of these lncRNAs for long-term expansion of CRC.

RESULTS

We uncovered that LINC02418 is essential for cancer cell clonogenic outgrowth. Mechanistically, LINC02418 regulates MYC expression levels to promote CRC stem cell functionality and prevent terminal differentiation. Furthermore, we developed effective small interfering RNA (siRNA)-based therapeutics to target LINC02418 RNA in vivo.

CONCLUSION

We propose that cancer-specific Wnt-regulated lncRNAs provide novel therapeutic opportunities to interfere with the Wnt pathway, which has so far defied effective pharmacological inhibition.

Comprehensive prediction and analysis of human protein essentiality based on a pretrained large language model

Human essential proteins (HEPs) are indispensable for individual viability and development. However, experimental methods to identify HEPs are often costly, time consuming and labor intensive. In addition, existing computational methods predict HEPs only at the cell line level, but HEPs vary across living human, cell line and animal models. Here we develop a sequence-based deep learning model, Protein Importance Calculator (PIC), by fine-tuning a pretrained protein language model. PIC not only substantially outperforms existing methods for predicting HEPs but also provides comprehensive prediction results across three levels: human, cell line and mouse. Furthermore, we define the protein essential score, derived from PIC, to quantify human protein essentiality and validate its effectiveness by a series of biological analyses. We also demonstrate the biomedical value of the protein essential score by identifying potential prognostic biomarkers for breast cancer and quantifying the essentiality of 617,462 human microproteins.

A new hypothesis to explain disease dominance

The onset and progression of dominant diseases are thought to result from haploinsufficiency or dominant negative effects. Here, we propose transcriptional adaptation (TA), a newly identified response to mRNA decay, as an additional cause of some dominant diseases. TA modulates the expression of so-called adapting genes, likely via mRNA decay products, resulting in genetic compensation or a worsening of the phenotype. Recent studies have challenged the current concepts of haploinsufficiency or poison proteins as the mechanisms underlying certain dominant diseases, including Brugada syndrome, hypertrophic cardiomyopathy, and frontotemporal lobar degeneration. We hypothesize that for these and other dominant diseases, when the underlying mutation leads to mRNA decay, the phenotype is due at least partly to the dysregulation of gene expression via TA.

PRODE recovers essential and context-essential genes through neighborhood-informed scores

Gene context-essentiality assessment supports precision oncology opportunities. The variability of gene effects inference from loss-of-function screenings across models and technologies limits identifying robust hits. We propose a computational framework named PRODE that integrates gene effects with protein-protein interactions to generate neighborhood-informed essential (NIE) and neighborhood-informed context essential (NICE) scores. It outperforms the canonical gene effect approach in recovering missed essential genes in shRNA screens and prioritizing context-essential hits from CRISPR-KO screens, as supported by in vitro validations. Applied to Her2 + breast cancer tumor samples, PRODE identifies oxidative phosphorylation genes as vulnerabilities with prognostic value, highlighting new therapeutic opportunities.

Transcriptome-scale RNA-targeting CRISPR screens reveal essential lncRNAs in human cells

Mammalian genomes host a diverse array of RNA that includes protein-coding and noncoding transcripts. However, the functional roles of most long noncoding RNAs (lncRNAs) remain elusive. Using RNA-targeting CRISPR-Cas13 screens, we probed how the loss of ∼6,200 lncRNAs impacts cell fitness across five human cell lines and identified 778 lncRNAs with context-specific or broad essentiality. We confirm their essentiality with individual perturbations and find that the majority of essential lncRNAs operate independently of their nearest protein-coding genes. Using transcriptome profiling in single cells, we discover that the loss of essential lncRNAs impairs cell-cycle progression and drives apoptosis. Many essential lncRNAs demonstrate dynamic expression across tissues during development. Using ∼9,000 primary tumors, we pinpoint those lncRNAs whose expression in tumors correlates with survival, yielding new biomarkers and potential therapeutic targets. This transcriptome-wide survey of functional lncRNAs advances our understanding of noncoding transcripts and demonstrates the potential of transcriptome-scale noncoding screens with Cas13.

Polymorphic pseudogenes in the human genome - a comprehensive assessment

BACKGROUND

Over the past decade, variations of the coding portion of the human genome have become increasingly evident. In this study, we focus on polymorphic pseudogenes, a unique and relatively unexplored type of pseudogene whose inactivating mutations have not yet been fixed in the human genome at the global population level. Thus, polymorphic pseudogenes are characterized by the presence in the population of both coding alleles and non-coding alleles originating from Loss-of-Function (LoF) mutations. These alleles can be found both in heterozygosity and in homozygosity in different human populations and thus represent pseudogenes that have not yet been fixed in the population.

RESULTS

A methodical cross-population analysis of 232 polymorphic pseudogenes, including 35 new examples, reveals that human olfactory signalling, drug metabolism and immunity are among the systems most impacted by the variable presence of LoF variants at high frequencies. Within this dataset, a total of 179 genes presented polymorphic LoF variants in all analysed populations. Transcriptome and proteome analysis confirmed that although these genes may harbour LoF alleles, when the coding allele is present, the gene remains active and can play a functional role in various metabolic pathways, including drug/xenobiotic metabolism and immunity. The observation that many polymorphic pseudogenes are members of multigene families argues that genetic redundancy may play a key role in compensating for the inactivation of one paralogue.

CONCLUSIONS

The distribution, expression and integration of cellular/biological networks in relation to human polymorphic pseudogenes, provide novel insights into the architecture of the human genome and the dynamics of gene gain and loss with likely functional impact.

The identification of essential cellular genes is critical for validating drug targets

Accurately identifying biological targets is crucial for advancing treatment options. Essential genes, vital for cell or organism survival, hold promise as potential drug targets in disease treatment. Although many studies have sought to identify essential genes as therapeutic targets in medicine and bioinformatics, systematic reviews on their relationship with drug targets are relatively rare. This work presents a comprehensive analysis to aid in identifying essential genes as potential targets for drug discovery, encompassing their relevance, identification methods, successful case studies, and challenges. This work will facilitate the identification of essential genes as therapeutic targets, thereby boosting new drug development.

GBF1 deficiency causes cataracts in human and mouse

Any opacification of the lens can be defined as cataracts, and lens epithelium cells play a crucial role in guaranteeing lens transparency by maintaining its homeostasis. Although several causative genes of congenital cataracts have been reported, the mechanisms underlying lens opacity remain unclear. In this study, a large family with congenital cataracts was collected and genetic analysis revealed a pathological mutation (c.3857 C > T, p.T1287I) in the GBF1 gene; all affected individuals in the family carried this heterozygous mutation, while unaffected family members did not. Functional studies in human lens epithelium cell line revealed that this mutation led to a reduction in GBF1 protein levels. Knockdown of endogenous GBF1 activated XBP1s in the unfolded protein response signal pathway, and enhances autophagy in an mTOR-independent manner. Heterozygous Gbf1 knockout mice also displayed typic cataract phenotype. Together, our study identified GBF1 as a novel causative gene for congenital cataracts. Additionally, we found that GBF1 deficiency activates the unfolded protein response and leads to enhanced autophagy, which may contribute to lens opacity.

Genome-scale exon perturbation screens uncover exons critical for cell fitness

CRISPR-Cas technology has transformed functional genomics, yet understanding of how individual exons differentially shape cellular phenotypes remains limited. Here, we optimized and conducted massively parallel exon deletion and splice-site mutation screens in human cell lines to identify exons that regulate cellular fitness. Fitness-promoting exons are prevalent in essential and highly expressed genes and commonly overlap with protein domains and interaction interfaces. Conversely, fitness-suppressing exons are enriched in nonessential genes, exhibiting lower inclusion levels, and overlap with intrinsically disordered regions and disease-associated mutations. In-depth mechanistic investigation of the screen-hit TAF5 alternative exon-8 revealed that its inclusion is required for assembly of the TFIID general transcription initiation complex, thereby regulating global gene expression output. Collectively, our orthogonal exon perturbation screens established a comprehensive repository of phenotypically important exons and uncovered regulatory mechanisms governing cellular fitness and gene expression.

A deep catalogue of protein-coding variation in 983,578 individuals

Rare coding variants that substantially affect function provide insights into the biology of a gene1-3. However, ascertaining the frequency of such variants requires large sample sizes4-8. Here we present a catalogue of human protein-coding variation, derived from exome sequencing of 983,578 individuals across diverse populations. In total, 23% of the Regeneron Genetics Center Million Exome (RGC-ME) data come from individuals of African, East Asian, Indigenous American, Middle Eastern and South Asian ancestry. The catalogue includes more than 10.4 million missense and 1.1 million predicted loss-of-function (pLOF) variants. We identify individuals with rare biallelic pLOF variants in 4,848 genes, 1,751 of which have not been previously reported. From precise quantitative estimates of selection against heterozygous loss of function (LOF), we identify 3,988 LOF-intolerant genes, including 86 that were previously assessed as tolerant and 1,153 that lack established disease annotation. We also define regions of missense depletion at high resolution. Notably, 1,482 genes have regions that are depleted of missense variants despite being tolerant of pLOF variants. Finally, we estimate that 3% of individuals have a clinically actionable genetic variant, and that 11,773 variants reported in ClinVar with unknown significance are likely to be deleterious cryptic splice sites. To facilitate variant interpretation and genetics-informed precision medicine, we make this resource of coding variation from the RGC-ME dataset publicly accessible through a variant allele frequency browser.

High-throughput assays to assess variant effects on disease

Interpreting the wealth of rare genetic variants discovered in population-scale sequencing efforts and deciphering their associations with human health and disease present a critical challenge due to the lack of sufficient clinical case reports. One promising avenue to overcome this problem is deep mutational scanning (DMS), a method of introducing and evaluating large-scale genetic variants in model cell lines. DMS allows unbiased investigation of variants, including those that are not found in clinical reports, thus improving rare disease diagnostics. Currently, the main obstacle limiting the full potential of DMS is the availability of functional assays that are specific to disease mechanisms. Thus, we explore high-throughput functional methodologies suitable to examine broad disease mechanisms. We specifically focus on methods that do not require robotics or automation but instead use well-designed molecular tools to transform biological mechanisms into easily detectable signals, such as cell survival rate, fluorescence or drug resistance. Here, we aim to bridge the gap between disease-relevant assays and their integration into the DMS framework.

Relationships between genomic dissipation and de novo SNP evolution

Patterns of single nucleotide polymorphisms (SNPs) in eukaryotic DNA are traditionally attributed to selective pressure, drift, identity descent, or related factors-without accounting for ways in which bias during de novo SNP formation, itself, might contribute. A functional and phenotypic analysis based on evolutionary resilience of DNA points to decreased numbers of non-synonymous SNPs in human and other genomes, with a predominant component of SNP depletion in the human gene pool caused by robust preferences during de novo SNP formation (rather than selective constraint). Ramifications of these findings are broad, belie a number of concepts regarding human evolution, and point to a novel interpretation of evolving DNA across diverse species.

Deep learning the cis-regulatory code for gene expression in selected model plants

Elucidating the relationship between non-coding regulatory element sequences and gene expression is crucial for understanding gene regulation and genetic variation. We explored this link with the training of interpretable deep learning models predicting gene expression profiles from gene flanking regions of the plant species Arabidopsis thaliana, Solanum lycopersicum, Sorghum bicolor, and Zea mays. With over 80% accuracy, our models enabled predictive feature selection, highlighting e.g. the significant role of UTR regions in determining gene expression levels. The models demonstrated remarkable cross-species performance, effectively identifying both conserved and species-specific regulatory sequence features and their predictive power for gene expression. We illustrated the application of our approach by revealing causal links between genetic variation and gene expression changes across fourteen tomato genomes. Lastly, our models efficiently predicted genotype-specific expression of key functional gene groups, exemplified by underscoring known phenotypic and metabolic differences between Solanum lycopersicum and its wild, drought-resistant relative, Solanum pennellii.

Evolutionary analysis of gene ages across TADs associates chromatin topology with whole-genome duplications

Topologically associated domains (TADs) are interaction subnetworks of chromosomal regions in 3D genomes. TAD boundaries frequently coincide with genome breaks while boundary deletion is under negative selection, suggesting that TADs may facilitate genome rearrangements and evolution. We show that genes co-localize by evolutionary age in humans and mice, resulting in TADs having different proportions of younger and older genes. We observe a major transition in the age co-localization patterns between the genes born during vertebrate whole-genome duplications (WGDs) or before and those born afterward. We also find that genes recently duplicated in primates and rodents are more frequently essential when they are located in old-enriched TADs and interact with genes that last duplicated during the WGD. Therefore, the evolutionary relevance of recent genes may increase when located in TADs with established regulatory networks. Our data suggest that TADs could play a role in organizing ancestral functions and evolutionary novelty.

Essentiality, protein-protein interactions and evolutionary properties are key predictors for identifying cancer-associated genes using machine learning

The distinctive nature of cancer as a disease prompts an exploration of the special characteristics the genes implicated in cancer exhibit. The identification of cancer-associated genes and their characteristics is crucial to further our understanding of this disease and enhanced likelihood of therapeutic drug targets success. However, the rate at which cancer genes are being identified experimentally is slow. Applying predictive analysis techniques, through the building of accurate machine learning models, is potentially a useful approach in enhancing the identification rate of these genes and their characteristics. Here, we investigated gene essentiality scores and found that they tend to be higher for cancer-associated genes compared to other protein-coding human genes. We built a dataset of extended gene properties linked to essentiality and used it to train a machine-learning model; this model reached 89% accuracy and > 0.85 for the Area Under Curve (AUC). The model showed that essentiality, evolutionary-related properties, and properties arising from protein-protein interaction networks are particularly effective in predicting cancer-associated genes. We were able to use the model to identify potential candidate genes that have not been previously linked to cancer. Prioritising genes that score highly by our methods could aid scientists in their cancer genes research.

Multi-omics pan-cancer analyses identify MCM4 as a promising prognostic and diagnostic biomarker

Minichromosome Maintenance Complex Component 4 (MCM4) is a vital component of the mini-chromosome maintenance complex family, crucial for initiating the replication of eukaryotic genomes. Recently, there has been a growing interest in investigating the significance of MCM4 in different types of cancer. Despite the existing research on this topic, a comprehensive analysis of MCM4 across various cancer types has been lacking. This study aims to bridge this knowledge gap by presenting a thorough pan-cancer analysis of MCM4, shedding light on its functional implications and potential clinical applications. The study utilized multi-omics samples from various databases. Bioinformatic tools were employed to explore the expression profiles, genetic alterations, phosphorylation states, immune cell infiltration patterns, immune subtypes, functional enrichment, disease prognosis, as well as the diagnostic potential of MCM4 and its responsiveness to drugs in a range of cancers. Our research demonstrates that MCM4 is closely associated with the oncogenesis, prognosis and diagnosis of various tumors and proposes that MCM4 may function as a potential biomarker in pan-cancer, providing a deeper understanding of its potential role in cancer development and treatment.

Ferroptosis in cancer: From molecular mechanisms to therapeutic strategies

Ferroptosis is a non-apoptotic form of regulated cell death characterized by the lethal accumulation of iron-dependent membrane-localized lipid peroxides. It acts as an innate tumor suppressor mechanism and participates in the biological processes of tumors. Intriguingly, mesenchymal and dedifferentiated cancer cells, which are usually resistant to apoptosis and traditional therapies, are exquisitely vulnerable to ferroptosis, further underscoring its potential as a treatment approach for cancers, especially for refractory cancers. However, the impact of ferroptosis on cancer extends beyond its direct cytotoxic effect on tumor cells. Ferroptosis induction not only inhibits cancer but also promotes cancer development due to its potential negative impact on anticancer immunity. Thus, a comprehensive understanding of the role of ferroptosis in cancer is crucial for the successful translation of ferroptosis therapy from the laboratory to clinical applications. In this review, we provide an overview of the recent advancements in understanding ferroptosis in cancer, covering molecular mechanisms, biological functions, regulatory pathways, and interactions with the tumor microenvironment. We also summarize the potential applications of ferroptosis induction in immunotherapy, radiotherapy, and systemic therapy, as well as ferroptosis inhibition for cancer treatment in various conditions. We finally discuss ferroptosis markers, the current challenges and future directions of ferroptosis in the treatment of cancer.

Decoding semiotic minimal genome: a non-genocentric approach

The search for the minimum information required for an organism to sustain a cellular system network has rendered both the identification of a fixed number of known genes and those genes whose function remains to be identified. The approaches used in such search generally focus their analysis on coding genomic regions, based on the genome to proteic-product perspective. Such approaches leave other fundamental processes aside, mainly those that include higher-level information management. To cope with this limitation, a non-genocentric approach based on genomic sequence analysis using language processing tools and gene ontology may prove an effective strategy for the identification of those fundamental genomic elements for life autonomy. Additionally, this approach will provide us with an integrative analysis of the information value present in all genomic elements, regardless of their coding status.

Functional annotation of variants of the BRCA2 gene via locally haploid human pluripotent stem cells

Mutations in the BRCA2 gene are associated with sporadic and familial cancer, cause genomic instability and sensitize cancer cells to inhibition by the poly(ADP-ribose) polymerase (PARP). Here we show that human pluripotent stem cells (hPSCs) with one copy of BRCA2 deleted can be used to annotate variants of this gene and to test their sensitivities to PARP inhibition. By using Cas9 to edit the functional BRCA2 allele in the locally haploid hPSCs and in fibroblasts differentiated from them, we characterized essential regions in the gene to identify permissive and loss-of-function mutations. We also used Cas9 to directly test the function of individual amino acids, including amino acids encoded by clinical BRCA2 variants of uncertain significance, and identified alleles that are sensitive to PARP inhibitors used as a standard of care in BRCA2-deficient cancers. Locally haploid human pluripotent stem cells can facilitate detailed structure-function analyses of genes and the rapid functional evaluation of clinically observed mutations.

Nuclear to cytoplasmic transport is a druggable dependency in MYC-driven hepatocellular carcinoma

The MYC oncogene is often dysregulated in human cancer, including hepatocellular carcinoma (HCC). MYC is considered undruggable to date. Here, we comprehensively identify genes essential for survival of MYChigh but not MYClow cells by a CRISPR/Cas9 genome-wide screen in a MYC-conditional HCC model. Our screen uncovers novel MYC synthetic lethal (MYC-SL) interactions and identifies most MYC-SL genes described previously. In particular, the screen reveals nucleocytoplasmic transport to be a MYC-SL interaction. We show that the majority of MYC-SL nucleocytoplasmic transport genes are upregulated in MYChigh murine HCC and are associated with poor survival in HCC patients. Inhibiting Exportin-1 (XPO1) in vivo induces marked tumor regression in an autochthonous MYC-transgenic HCC model and inhibits tumor growth in HCC patient-derived xenografts. XPO1 expression is associated with poor prognosis only in HCC patients with high MYC activity. We infer that MYC may generally regulate and require altered expression of nucleocytoplasmic transport genes for tumorigenesis.

Recent advances in the characterization of essential genes and development of a database of essential genes

Over the past few decades, there has been a significant interest in the study of essential genes, which are crucial for the survival of an organism under specific environmental conditions and thus have practical applications in the fields of synthetic biology and medicine. An increasing amount of experimental data on essential genes has been obtained with the continuous development of technological methods. Meanwhile, various computational prediction methods, related databases and web servers have emerged accordingly. To facilitate the study of essential genes, we have established a database of essential genes (DEG), which has become popular with continuous updates to facilitate essential gene feature analysis and prediction, drug and vaccine development, as well as artificial genome design and construction. In this article, we summarized the studies of essential genes, overviewed the relevant databases, and discussed their practical applications. Furthermore, we provided an overview of the main applications of DEG and conducted comprehensive analyses based on its latest version. However, it should be noted that the essential gene is a dynamic concept instead of a binary one, which presents both opportunities and challenges for their future development.

Haploinsufficient Transcription Factors in Myeloid Neoplasms

Many transcription factors (TFs) function as tumor suppressor genes with heterozygous phenotypes, yet haploinsufficiency generally has an underappreciated role in neoplasia. This is no less true in myeloid cells, which are normally regulated by a delicately balanced and interconnected transcriptional network. Detailed understanding of TF dose in this circuitry sheds light on the leukemic transcriptome. In this review, we discuss the emerging features of haploinsufficient transcription factors (HITFs). We posit that: (a) monoallelic and biallelic losses can have distinct cellular outcomes; (b) the activity of a TF exists in a greater range than the traditional Mendelian genetic doses; and (c) how a TF is deleted or mutated impacts the cellular phenotype. The net effect of a HITF is a myeloid differentiation block and increased intercellular heterogeneity in the course of myeloid neoplasia.

NetVA: an R package for network vulnerability and influence analysis

In biological network analysis, identifying key molecules plays a decisive role in the development of potential diagnostic and therapeutic candidates. Among various approaches of network analysis, network vulnerability analysis is quite important, as it assesses significant associations between topological properties and the functional essentiality of a network. Similarly, some node centralities are also used to screen out key molecules. Among these node centralities, escape velocity centrality (EVC), and its extended version (EVC+) outperform others, viz., Degree, Betweenness, and Clustering coefficient. Keeping this in mind, we aimed to develop a first-of-its-kind R package named NetVA, which analyzes networks to identify key molecular players (individual proteins and protein pairs/triplets) through network vulnerability and EVC+-based approaches. To demonstrate the application and relevance of our package in network analysis, previously published and publicly available protein-protein interactions (PPIs) data of human breast cancer were analyzed. This resulted in identifying some most important proteins. These included essential proteins, non-essential proteins, hubs, and bottlenecks, which play vital roles in breast cancer development. Thus, the NetVA package, available at https://github.com/kr-swapnil/NetVA with a detailed tutorial to download and use, assists in predicting potential candidates for therapeutic and diagnostic purposes by exploring various topological features of a disease-specific PPIs network.Communicated by Ramaswamy H. Sarma.

A Machine Learning Approach for Predicting Essentiality of Metabolic Genes

The identification of essential genes is a key challenge in systems and synthetic biology, particularly for engineering metabolic pathways that convert feedstocks into valuable products. Assessment of gene essentiality at a genome scale requires large and costly growth assays of knockout strains. Here we describe a strategy to predict the essentiality of metabolic genes using binary classification algorithms. The approach combines elements from genome-scale metabolic models, directed graphs, and machine learning into a predictive model that can be trained on small knockout data. We demonstrate the efficacy of this approach using the most complete metabolic model of Escherichia coli and various machine learning algorithms for binary classification.

Untangling the Context-Specificity of Essential Genes by Means of Machine Learning: A Constructive Experience

Gene essentiality is a genetic concept crucial for a comprehensive understanding of life and evolution. In the last decade, many essential genes (EGs) have been determined using different experimental and computational approaches, and this information has been used to reduce the genomes of model organisms. A growing amount of evidence highlights that essentiality is a property that depends on the context. Because of their importance in vital biological processes, recognising context-specific EGs (csEGs) could help for identifying new potential pharmacological targets and to improve precision therapeutics. Since most of the computational procedures proposed to identify and predict EGs neglect their context-specificity, we focused on this aspect, providing a theoretical and experimental overview of the literature, data and computational methods dedicated to recognising csEGs. To this end, we adapted existing computational methods to exploit a specific context (the kidney tissue) and experimented with four different prediction methods using the labels provided by four different identification approaches. The considerations derived from the analysis of the obtained results, confirmed and validated also by further experiments for a different tissue context, provide the reader with guidance on exploiting existing tools for achieving csEGs identification and prediction.

Mesenchymal stromal cells alleviate depressive and anxiety-like behaviors via a lung vagal-to-brain axis in male mice

Major depressive disorder (MDD) is one of the most common and disabling mental disorders, and current strategies remain inadequate. Although mesenchymal stromal cells (MSCs) have shown beneficial effects in experimental models of depression, underlying mechanisms remain elusive. Here, using murine depression models, we demonstrated that MSCs could alleviate depressive and anxiety-like behaviors not due to a reduction in proinflammatory cytokines, but rather activation of dorsal raphe nucleus (DRN) 5-hydroxytryptamine (5-HT) neurons. Mechanistically, peripheral delivery of MSCs activated pulmonary innervating vagal sensory neurons, which projected to the nucleus tractus solitarius, inducing the release of 5-HT in DRN. Furthermore, MSC-secreted brain-derived neurotrophic factor activated lung sensory neurons through tropomyosin receptor kinase B (TrkB), and inhalation of a TrkB agonist also achieved significant therapeutic effects in male mice. This study reveals a role of peripheral MSCs in regulating central nervous system function and demonstrates a potential "lung vagal-to-brain axis" strategy for MDD.

A deep catalog of protein-coding variation in 985,830 individuals

Coding variants that have significant impact on function can provide insights into the biology of a gene but are typically rare in the population. Identifying and ascertaining the frequency of such rare variants requires very large sample sizes. Here, we present the largest catalog of human protein-coding variation to date, derived from exome sequencing of 985,830 individuals of diverse ancestry to serve as a rich resource for studying rare coding variants. Individuals of African, Admixed American, East Asian, Middle Eastern, and South Asian ancestry account for 20% of this Exome dataset. Our catalog of variants includes approximately 10.5 million missense (54% novel) and 1.1 million predicted loss-of-function (pLOF) variants (65% novel, 53% observed only once). We identified individuals with rare homozygous pLOF variants in 4,874 genes, and for 1,838 of these this work is the first to document at least one pLOF homozygote. Additional insights from the RGC-ME dataset include 1) improved estimates of selection against heterozygous loss-of-function and identification of 3,459 genes intolerant to loss-of-function, 83 of which were previously assessed as tolerant to loss-of-function and 1,241 that lack disease annotations; 2) identification of regions depleted of missense variation in 457 genes that are tolerant to loss-of-function; 3) functional interpretation for 10,708 variants of unknown or conflicting significance reported in ClinVar as cryptic splice sites using splicing score thresholds based on empirical variant deleteriousness scores derived from RGC-ME; and 4) an observation that approximately 3% of sequenced individuals carry a clinically actionable genetic variant in the ACMG SF 3.1 list of genes. We make this important resource of coding variation available to the public through a variant allele frequency browser. We anticipate that this report and the RGC-ME dataset will serve as a valuable reference for understanding rare coding variation and help advance precision medicine efforts.

Repurposing Normal Chromosomal Microarray Data to Harbor Genetic Insights into Congenital Heart Disease

About 15% of congenital heart disease (CHD) patients have a known pathogenic copy number variant. The majority of their chromosomal microarray (CMA) tests are deemed normal. Diagnostic interpretation typically ignores microdeletions smaller than 100 kb. We hypothesized that unreported microdeletions are enriched for CHD genes. We analyzed "normal" CMAs of 1762 patients who were evaluated at a pediatric referral center, of which 319 (18%) had CHD. Using CMAs from monozygotic twins or replicates from the same individual, we established a size threshold based on probe count for the reproducible detection of small microdeletions. Genes in the microdeletions were sequentially filtered by their nominal association with a CHD diagnosis, the expression level in the fetal heart, and the deleteriousness of a loss-of-function mutation. The subsequent enrichment for CHD genes was assessed using the presence of known or potentially novel genes implicated by a large whole-exome sequencing study of CHD. The unreported microdeletions were modestly enriched for both known CHD genes and those of unknown significance identified using their de novo mutation in CHD patients. Our results show that readily available "normal" CMA data can be a fruitful resource for genetic discovery and that smaller deletions should receive more attention in clinical evaluation.

Genic constraint against nonsynonymous variation across the mouse genome

BACKGROUND

Selective constraint, the depletion of variation due to negative selection, provides insights into the functional impact of variants and disease mechanisms. However, its characterization in mice, the most commonly used mammalian model, remains limited. This study aims to quantify mouse gene constraint using a new metric called the nonsynonymous observed expected ratio (NOER) and investigate its relationship with gene function.

RESULTS

NOER was calculated using whole-genome sequencing data from wild mouse populations (Mus musculus sp and Mus spretus). Positive correlations were observed between mouse gene constraint and the number of associated knockout phenotypes, indicating stronger constraint on pleiotropic genes. Furthermore, mouse gene constraint showed a positive correlation with the number of pathogenic variant sites in their human orthologues, supporting the relevance of mouse models in studying human disease variants.

CONCLUSIONS

NOER provides a resource for assessing the fitness consequences of genetic variants in mouse genes and understanding the relationship between gene constraint and function. The study's findings highlight the importance of pleiotropy in selective constraint and support the utility of mouse models in investigating human disease variants. Further research with larger sample sizes can refine constraint estimates in mice and enable more comprehensive comparisons of constraint between mouse and human orthologues.

An unsupervised deep learning framework for predicting human essential genes from population and functional genomic data

BACKGROUND

The ability to accurately predict essential genes intolerant to loss-of-function (LOF) mutations can dramatically improve the identification of disease-associated genes. Recently, there have been numerous computational methods developed to predict human essential genes from population genomic data. While the existing methods are highly predictive of essential genes of long length, they have limited power in pinpointing short essential genes due to the sparsity of polymorphisms in the human genome.

RESULTS

Motivated by the premise that population and functional genomic data may provide complementary evidence for gene essentiality, here we present an evolution-based deep learning model, DeepLOF, to predict essential genes in an unsupervised manner. Unlike previous population genetic methods, DeepLOF utilizes a novel deep learning framework to integrate both population and functional genomic data, allowing us to pinpoint short essential genes that can hardly be predicted from population genomic data alone. Compared with previous methods, DeepLOF shows unmatched performance in predicting ClinGen haploinsufficient genes, mouse essential genes, and essential genes in human cell lines. Notably, at a false positive rate of 5%, DeepLOF detects 50% more ClinGen haploinsufficient genes than previous methods. Furthermore, DeepLOF discovers 109 novel essential genes that are too short to be identified by previous methods.

CONCLUSION

The predictive power of DeepLOF shows that it is a compelling computational method to aid in the discovery of essential genes.

TimeTalk uses single-cell RNA-seq datasets to decipher cell-cell communication during early embryo development

Early embryonic development is a dynamic process that relies on proper cell-cell communication to form a correctly patterned embryo. Early embryo development-related ligand-receptor pairs (eLRs) have been shown to guide cell fate decisions and morphogenesis. However, the scope of eLRs and their influence on early embryo development remain elusive. Here, we developed a computational framework named TimeTalk from integrated public time-course mouse scRNA-seq datasets to decipher the secret of eLRs. Extensive validations and analyses were performed to ensure the involvement of identified eLRs in early embryo development. Process analysis identified that eLRs could be divided into six temporal windows corresponding to sequential events in the early embryo development process. With the interpolation strategy, TimeTalk is powerful in revealing paracrine settings and studying cell-cell communication during early embryo development. Furthermore, by using TimeTalk in the blastocyst and blastoid models, we found that the blastoid models share the core communication pathways with the epiblast and primitive endoderm lineages in the blastocysts. This result suggests that TimeTalk has transferability to other bio-dynamic processes. We also curated eLRs recognized by TimeTalk, which may provide valuable clues for understanding early embryo development and relevant disorders.

Essential genes: a cross-species perspective

Protein coding genes exhibit different degrees of intolerance to loss-of-function variation. The most intolerant genes, whose function is essential for cell or/and organism survival, inform on fundamental biological processes related to cell proliferation and organism development and provide a window on the molecular mechanisms of human disease. Here we present a brief overview of the resources and knowledge gathered around gene essentiality, from cancer cell lines to model organisms to human development. We outline the implications of using different sources of evidence and definitions to determine which genes are essential and highlight how information on the essentiality status of a gene can inform novel disease gene discovery and therapeutic target identification.

Partial gene suppression improves identification of cancer vulnerabilities when CRISPR-Cas9 knockout is pan-lethal

BACKGROUND

Hundreds of functional genomic screens have been performed across a diverse set of cancer contexts, as part of efforts such as the Cancer Dependency Map, to identify gene dependencies-genes whose loss of function reduces cell viability or fitness. Recently, large-scale screening efforts have shifted from RNAi to CRISPR-Cas9, due to superior efficacy and specificity. However, many effective oncology drugs only partially inhibit their protein targets, leading us to question whether partial suppression of genes using RNAi could reveal cancer vulnerabilities that are missed by complete knockout using CRISPR-Cas9. Here, we compare CRISPR-Cas9 and RNAi dependency profiles of genes across approximately 400 matched cancer cell lines.

RESULTS

We find that CRISPR screens accurately identify more gene dependencies per cell line, but the majority of each cell line's dependencies are part of a set of 1867 genes that are shared dependencies across the entire collection (pan-lethals). While RNAi knockdown of about 30% of these genes is also pan-lethal, approximately 50% have selective dependency patterns across cell lines, suggesting they could still be cancer vulnerabilities. The accuracy of the unique RNAi selectivity is supported by associations to multi-omics profiles, drug sensitivity, and other expected co-dependencies.

CONCLUSIONS

Incorporating RNAi data for genes that are pan-lethal knockouts facilitates the discovery of a wider range of gene targets than could be detected using the CRISPR dataset alone. This can aid in the interpretation of contrasting results obtained from CRISPR and RNAi screens and reinforce the importance of partial gene suppression methods in building a cancer dependency map.

Integrating massive RNA-seq data to elucidate transcriptome dynamics in Drosophila melanogaster

The volume of ribonucleic acid (RNA)-seq data has increased exponentially, providing numerous new insights into various biological processes. However, due to significant practical challenges, such as data heterogeneity, it is still difficult to ensure the quality of these data when integrated. Although some quality control methods have been developed, sample consistency is rarely considered and these methods are susceptible to artificial factors. Here, we developed MassiveQC, an unsupervised machine learning-based approach, to automatically download and filter large-scale high-throughput data. In addition to the read quality used in other tools, MassiveQC also uses the alignment and expression quality as model features. Meanwhile, it is user-friendly since the cutoff is generated from self-reporting and is applicable to multimodal data. To explore its value, we applied MassiveQC to Drosophila RNA-seq data and generated a comprehensive transcriptome atlas across 28 tissues from embryogenesis to adulthood. We systematically characterized fly gene expression dynamics and found that genes with high expression dynamics were likely to be evolutionarily young and expressed at late developmental stages, exhibiting high nonsynonymous substitution rates and low phenotypic severity, and they were involved in simple regulatory programs. We also discovered that human and Drosophila had strong positive correlations in gene expression in orthologous organs, revealing the great potential of the Drosophila system for studying human development and disease.

Proteins with amino acid repeats constitute a rapidly evolvable and human-specific essentialome

Protein products of essential genes, indispensable for organismal survival, are highly conserved and bring about fundamental functions. Interestingly, proteins that contain amino acid homorepeats that tend to evolve rapidly are enriched in eukaryotic essentialomes. Why are proteins with hypermutable homorepeats enriched in conserved and functionally vital essential proteins? We solve this functional versus evolutionary paradox by demonstrating that human essential proteins with homorepeats bring about crosstalk across biological processes through high interactability and have distinct regulatory functions affecting expansive global regulation. Importantly, essential proteins with homorepeats rapidly diverge with the amino acid substitutions frequently affecting functional sites, likely facilitating rapid adaptability. Strikingly, essential proteins with homorepeats influence human-specific embryonic and brain development, implying that the presence of homorepeats could contribute to the emergence of human-specific processes. Thus, we propose that homorepeat-containing essential proteins affecting species-specific traits can be potential intervention targets across pathologies, including cancers and neurological disorders.

Role of non-coding variants in cardiovascular disease

Cardiovascular diseases (CVDs) constitute one of the significant causes of death worldwide. Different pathological states are linked to CVDs, which despite interventions and treatments, still have poor prognoses. The genetic component, as a beneficial tool in the risk stratification of CVD development, plays a role in the pathogenesis of this group of diseases. The emergence of genome-wide association studies (GWAS) have led to the identification of non-coding parts associated with cardiovascular traits and disorders. Variants located in functional non-coding regions, including promoters/enhancers, introns, miRNAs and 5'/3' UTRs, account for 90% of all identified single-nucleotide polymorphisms associated with CVDs. Here, for the first time, we conducted a comprehensive review on the reported non-coding variants for different CVDs, including hypercholesterolemia, cardiomyopathies, congenital heart diseases, thoracic aortic aneurysms/dissections and coronary artery diseases. Additionally, we present the most commonly reported genes involved in each CVD. In total, 1469 non-coding variants constitute most reports on familial hypercholesterolemia, hypertrophic cardiomyopathy and dilated cardiomyopathy. The application and identification of non-coding variants are beneficial for the genetic diagnosis and better therapeutic management of CVDs.

The impact of local genomic properties on the evolutionary fate of genes

Functionally indispensable genes are likely to be retained and otherwise to be lost during evolution. This evolutionary fate of a gene can also be affected by factors independent of gene dispensability, including the mutability of genomic positions, but such features have not been examined well. To uncover the genomic features associated with gene loss, we investigated the characteristics of genomic regions where genes have been independently lost in multiple lineages. With a comprehensive scan of gene phylogenies of vertebrates with a careful inspection of evolutionary gene losses, we identified 813 human genes whose orthologs were lost in multiple mammalian lineages: designated 'elusive genes.' These elusive genes were located in genomic regions with rapid nucleotide substitution, high GC content, and high gene density. A comparison of the orthologous regions of such elusive genes across vertebrates revealed that these features had been established before the radiation of the extant vertebrates approximately 500 million years ago. The association of human elusive genes with transcriptomic and epigenomic characteristics illuminated that the genomic regions containing such genes were subject to repressive transcriptional regulation. Thus, the heterogeneous genomic features driving gene fates toward loss have been in place and may sometimes have relaxed the functional indispensability of such genes. This study sheds light on the complex interplay between gene function and local genomic properties in shaping gene evolution that has persisted since the vertebrate ancestor.

Advances and Challenges for GWAS Analysis in Cardiac Diseases: A Focus on Coronary Artery Disease (CAD)

The achievement of genome-wide association studies (GWAS) has rapidly progressed our understanding of the etiology of coronary artery disease (CAD). It unlocks new strategies to strengthen the stalling of CAD drug development. In this review, we highlighted the recent drawbacks, mainly pointing out those involved in identifying causal genes and interpreting the connections between disease pathology and risk variants. We also benchmark the novel insights into the biological mechanism behind the disease primarily based on outcomes of GWAS. Furthermore, we also shed light on the successful discovery of novel treatment targets by introducing various layers of "omics" data and applying systems genetics strategies. Lastly, we discuss in-depth the significance of precision medicine that is helpful to improve through GWAS analysis in cardiovascular research.

Cohesin: an emerging master regulator at the heart of cardiac development

Cohesins are ATPase complexes that play central roles in cellular processes such as chromosome division, DNA repair, and gene expression. Cohesinopathies arise from mutations in cohesin proteins or cohesin complex regulators and encompass a family of related developmental disorders that present with a range of severe birth defects, affect many different physiological systems, and often lead to embryonic fatality. Treatments for cohesinopathies are limited, in large part due to the lack of understanding of cohesin biology. Thus, characterizing the signaling networks that lie upstream and downstream of cohesin-dependent pathways remains clinically relevant. Here, we highlight alterations in cohesins and cohesin regulators that result in cohesinopathies, with a focus on cardiac defects. In addition, we suggest a novel and more unifying view regarding the mechanisms through which cohesinopathy-based heart defects may arise.

Random with Respect to Fitness or External Selection? An Important but Often Overlooked Distinction

Mutations are often described as being "random with respect to fitness." Here we show that the experiments used to establish randomness with respect to fitness are only capable of showing that mutations are random with respect to current external selection. Current debates about whether or not mutations are directed may be at least partially resolved by making use of this distinction. Additionally, this distinction has important mathematical, experimental, and inferential implications.

Integration of Human and Viral miRNAs in Epstein-Barr Virus-Associated Tumors and Implications for Drug Repurposing

Epstein-Barr virus (EBV) is associated with several tumors, and has substantial relevance for public health. Therapeutics innovation for EBV-related disorders is much needed. In this context, miRNAs are noncoding RNA molecules that play vital roles in EBV infection. miRNA-Seq and RNA-Seq data for EBV-associated clinical samples and cell lines have been generated, but their detailed integrative analyses, and exploitation for drug repurposing against EBV are lacking. Hence, we identified and analyzed the differentially expressed miRNAs (DEmiRs) in EBV-infected cell lines (28) and infected (28) and uninfected human tissue (20) samples using an in-house pipeline. We found significantly enriched host miRNAs like hsa-mir-3651, hsa-mir-1248, and hsa-mir-29c-3p in EBV-infected samples from EBV-associated nasopharyngeal carcinoma and Hodgkin's lymphoma, among others. Furthermore, we also identified significantly enriched novel miRNAs such as hsa-mir-29c-3p, hsa-mir-3651, and hsa-mir-98-3p, which were not previously reported in EBV-related tumors. Differentially expressed mRNAs (DEMs) were identified in EBV-infected cell lines (21) and uninfected human tissue (14) samples. We predicted and selected 1572 DEMs (upregulated) that are targeted by 547 DEmiRs (downregulated). These were further classified into essential (870) and nonessential (702) genes. Moreover, a miRNA-mRNA network was developed for the hub miRNAs. Importantly, we used the DEMs during EBV latent infection types I, II, and III to identify the candidate drugs for repurposing: Glyburide, Levodopa, Nateglinide, and Stiripentol, among others. To the best of our knowledge, this is the first integrative analyses that identified DEmiRs and DEMs as potential therapeutic targets and predicted drugs as potential candidates for repurposing against EBV-related tumors.

Portrait of autosomal recessive diseases in the French-Canadian founder population of Saguenay-Lac-Saint-Jean

The population of the Saguenay-Lac-Saint-Jean (SLSJ) region, located in the province of Quebec, Canada, is recognized as a founder population, where some rare autosomal recessive diseases show a high prevalence. Through the clinical and molecular study of 82 affected individuals from 60 families, this study outlines 12 diseases identified as recurrent in SLSJ. Their carrier frequency was estimated with the contribution of 1059 healthy individuals, increasing the number of autosomal recessive diseases with known carrier frequency in this region from 14 to 25. We review the main clinical and molecular features previously reported for these disorders. Five of the studied diseases have a potential lethal effect and three are associated with intellectual deficiency. Therefore, we believe that the provincial program for carrier screening should be extended to include these eight disorders. The high-carrier frequency, together with the absence of consanguinity in most of these unrelated families, suggest a founder effect and genetic drift for the 12 recurrent variants. We recommend further studies to validate this hypothesis, as well as to extend the present study to other regions in the province of Quebec, since some of these disorders could also be present in other French-Canadian families.

Molecular Landscape of Tourette's Disorder

Tourette's disorder (TD) is a highly heritable childhood-onset neurodevelopmental disorder and is caused by a complex interplay of multiple genetic and environmental factors. Yet, the molecular mechanisms underlying the disorder remain largely elusive. In this study, we used the available omics data to compile a list of TD candidate genes, and we subsequently conducted tissue/cell type specificity and functional enrichment analyses of this list. Using genomic data, we also investigated genetic sharing between TD and blood and cerebrospinal fluid (CSF) metabolite levels. Lastly, we built a molecular landscape of TD through integrating the results from these analyses with an extensive literature search to identify the interactions between the TD candidate genes/proteins and metabolites. We found evidence for an enriched expression of the TD candidate genes in four brain regions and the pituitary. The functional enrichment analyses implicated two pathways ('cAMP-mediated signaling' and 'Endocannabinoid Neuronal Synapse Pathway') and multiple biological functions related to brain development and synaptic transmission in TD etiology. Furthermore, we found genetic sharing between TD and the blood and CSF levels of 39 metabolites. The landscape of TD not only provides insights into the (altered) molecular processes that underlie the disease but, through the identification of potential drug targets (such as FLT3, NAALAD2, CX3CL1-CX3CR1, OPRM1, and HRH2), it also yields clues for developing novel TD treatments.

DeepCellEss: cell line-specific essential protein prediction with attention-based interpretable deep learning

MOTIVATION

Protein essentiality is usually accepted to be a conditional trait and strongly affected by cellular environments. However, existing computational methods often do not take such characteristics into account, preferring to incorporate all available data and train a general model for all cell lines. In addition, the lack of model interpretability limits further exploration and analysis of essential protein predictions.

RESULTS

In this study, we proposed DeepCellEss, a sequence-based interpretable deep learning framework for cell line-specific essential protein predictions. DeepCellEss utilizes a convolutional neural network and bidirectional long short-term memory to learn short- and long-range latent information from protein sequences. Further, a multi-head self-attention mechanism is used to provide residue-level model interpretability. For model construction, we collected extremely large-scale benchmark datasets across 323 cell lines. Extensive computational experiments demonstrate that DeepCellEss yields effective prediction performance for different cell lines and outperforms existing sequence-based methods as well as network-based centrality measures. Finally, we conducted some case studies to illustrate the necessity of considering specific cell lines and the superiority of DeepCellEss. We believe that DeepCellEss can serve as a useful tool for predicting essential proteins across different cell lines.

AVAILABILITY AND IMPLEMENTATION

The DeepCellEss web server is available at http://csuligroup.com:8000/DeepCellEss. The source code and data underlying this study can be obtained from https://github.com/CSUBioGroup/DeepCellEss.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Clinical Significance of TUBGCP4 Expression in Hepatocellular Carcinoma

We aim to investigate the expression and clinical significance of the tubulin gamma complex-associated protein 4 (TUBGCP4) in hepatocellular carcinoma (HCC). The mRNA expression of TUBGCP4 in HCC tissues was analyzed using The Cancer Genome Atlas (TCGA) database. Paired HCC and adjacent nontumor tissues were obtained from HCC patients to measure the protein expression of TUBGCP4 by immunohistochemistry (IHC) and to analyze the relationship between TUBGCP4 protein expression and the clinicopathological characteristics and the prognosis of HCC patients. We found that TUBGCP4 mRNA expression was upregulated in HCC tissues from TCGA database. IHC analysis showed that TUBGCP4 was positively expressed in 61.25% (49/80) of HCC tissues and 77.5% (62/80) of adjacent nontumor tissues. The Chi-square analysis indicated that the positive rate of TUBGCP4 expression between HCC tissues and the adjacent nontumor tissues was statistically different (P < 0.05). Furthermore, we found that TUBGCP4 protein expression was correlated with carbohydrate antigen (CA-199) levels of HCC patients (P < 0.05). Further, survival analysis showed that the overall survival time and tumor-free survival time in the TUBGCP4 positive group were significantly higher than those of the negative group (P < 0.05), indicating that the positive expression of TUBGCP4 was related to a better prognosis of HCC patients. COX model showed that TUBGCP4 was an independent prognostic factor for HCC patients. Our study indicates that TUBGCP4 protein expression is downregulated in HCC tissues and has a relationship with the prognosis of HCC patients.