Association studies between COVID-19 and SSc-ILD

Severe COVID-19 patients may develop pulmonary fibrosis, similar to SSc-ILD disease, suggesting a potential link between the two diseases. However, there are limited treatment options for SSc-ILD-type diseases. Therefore, investigating pathological markers of the two diseases can provide valuable insights for treating related conditions. RNA sequencing technology offers high throughput and precision. However, the bimodal nature of RNA-Seq data cannot be accurately captured by commonly used algorithms such as DESeq2. To address this issue, the Beta-Poisson model has been developed to identify differentially expressed genes. Unlike the classical DESeq2 algorithm, the Beta-Poisson model introduces a Beta distribution to construct a new hybrid distribution in place of the Gamma distribution of the Gamma-Poisson distribution, effectively characterizing the bimodal features of RNA-Seq data. The transcriptomes of SARS-CoV infection and SSc-ILD disease in the lung epithelial cell dataset were analyzed to identify common differentially expressed genes of SARS-CoV and SSc-ILD disease. Gene function and signaling pathway enrichment analysis and protein-protein interaction (PPI) network were used to identify common pathways and drug targets for SSc-ILD with COVID-19 infection. The results show that there are 50 differentially expressed genes in common between COVID-19 and SSC-ILD. The functions of these genes are mainly enriched in immune system response, interferon signaling pathway and other related signaling pathways, and enriched in biological processes such as cell defense response to virus and interferon regulation. Based on the detection of hub genes based on PPIs network, it is predicted that STAT1, ISG15, IRF7, MX1, EIF2AK2, DDX58, OAS1, OAS2, IFIT1 and IFIT3 are the key genes involved in the pathological phenotype of the two diseases. Based on the key genes, the interaction of transcription factor (TF) and miRNA with common differentially expressed genes is also identified. The possible pathological markers of the two diseases and related molecular regulatory mechanisms of disease treatment are revealed to provide theoretical basis for the treatment of the two diseases. © 2023 Editorial Office of Journal of Shenzhen University. All rights reserved.

High-sensitivity profiling of SARS-CoV-2 noncoding region-host protein interactome reveals the potential regulatory role of negative-sense viral RNA.

A deep understanding of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-host interactions is crucial to developing effective therapeutics and addressing the threat of emerging coronaviruses. The role of noncoding regions of viral RNA (ncrRNAs) has yet to be systematically scrutinized. We developed a method using MS2 affinity purification coupled with liquid chromatography-mass spectrometry and designed a diverse set of bait ncrRNAs to systematically map the interactome of SARS-CoV-2 ncrRNA in Calu-3, Huh7, and HEK293T cells. Integration of the results defined the core ncrRNA-host protein interactomes among cell lines. The 5' UTR interactome is enriched with proteins in the small nuclear ribonucleoproteins family and is a target for the regulation of viral replication and transcription. The 3' UTR interactome is enriched with proteins involved in the stress granules and heterogeneous nuclear ribonucleoproteins family. Intriguingly, compared with the positive-sense ncrRNAs, the negative-sense ncrRNAs, especially the negative-sense of 3' UTR, interacted with a large array of host proteins across all cell lines. These proteins are involved in the regulation of the viral production process, host cell apoptosis, and immune response. Taken together, our study depicts the comprehensive landscape of the SARS-CoV-2 ncrRNA-host protein interactome and unveils the potential regulatory role of the negative-sense ncrRNAs, providing a new perspective on virus-host interactions and the design of future therapeutics. Given the highly conserved nature of UTRs in positive-strand viruses, the regulatory role of negative-sense ncrRNAs should not be exclusive to SARS-CoV-2. IMPORTANCE Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes COVID-19, a pandemic affecting millions of lives. During replication and transcription, noncoding regions of the viral RNA (ncrRNAs) may play an important role in the virus-host interactions. Understanding which and how these ncrRNAs interact with host proteins is crucial for understanding the mechanism of SARS-CoV-2 pathogenesis. We developed the MS2 affinity purification coupled with liquid chromatography-mass spectrometry method and designed a diverse set of ncrRNAs to identify the SARS-CoV-2 ncrRNA interactome comprehensively in different cell lines and found that the 5' UTR binds to proteins involved in U1 small nuclear ribonucleoprotein, while the 3' UTR interacts with proteins involved in stress granules and the heterogeneous nuclear ribonucleoprotein family. Interestingly, negative-sense ncrRNAs showed interactions with a large number of diverse host proteins, indicating a crucial role in infection. The results demonstrate that ncrRNAs could serve diverse regulatory functions.

An efficient computational protocol for template-based design of peptides that inhibit interactions involving SARS-CoV-2 proteins.

The RNA-dependent RNA polymerase (RdRp) complex of SARS-CoV-2 lies at the core of its replication and transcription processes. The interfaces between holo-RdRp subunits are highly conserved, facilitating the design of inhibitors with high affinity for the interaction interface hotspots. We, therefore, take this as a model protein complex for the application of a structural bioinformatics protocol to design peptides that inhibit RdRp complexation by preferential binding at the interface of its core subunit nonstructural protein, nsp12, with accessory factor nsp7. Here, the interaction hotspots of the nsp7-nsp12 subunit of RdRp, determined from a long molecular dynamics trajectory, are used as a template. A large library of peptide sequences constructed from multiple hotspot motifs of nsp12 is screened in-silico to determine sequences with high geometric complementarity and interaction specificity for the binding interface of nsp7 (target) in the complex. Two lead designed peptides are extensively characterized using orthogonal bioanalytical methods to determine their suitability for inhibition of RdRp complexation. Binding affinity of these peptides to accessory factor nsp7, determined using a surface plasmon resonance (SPR) assay, is slightly better than that of nsp12: dissociation constant of 133nM and 167nM, respectively, compared to 473nM for nsp12. A competitive ELISA is used to quantify inhibition of nsp7-nsp12 complexation, with one of the lead peptides giving an IC50 of 25µM . Cell penetrability and cytotoxicity are characterized using a cargo delivery assay and MTT cytotoxicity assay, respectively. Overall, this work presents a proof-of-concept of an approach for rational discovery of peptide inhibitors of SARS-CoV-2 protein-protein interactions.

The Non-Covalent Interactions index: From biology to chemical reactivity and solid-state

The Non-Covalent Interaction (NCI) index has become a versatile tool for analyzing the presence and strength of localized and delocalized interactions in systems of interest to diverse areas such as biochemistry, reactivity, and solid state. Furthermore, it can be applied to promolecular, ab initio or even experimental electron densities, making it an even more flexible method. In this chapter, we demonstrate some of these NCI capacities by employing it in three representative cases: (a) Interaction between the spike protein of some early variants of the SARS-CoV-2 virus with selected potential inhibitors. (b) The ring-opening metathesis polymerization of two norbornene derivatives, mediated by a ruthenium-alkylidene catalyst. (c) Effect of π-electron delocalization in the structural and electronic changes on the noncovalent interactions in two quinone crystals. These cases are illustrative of the application of NCI to study ligand-protein interactions, the interplay of inter and intramolecular forces in reaction pathways, and crystal packing, respectively. Moreover, we show the suitability of using different methods for obtaining the electron density in each example. © 2023 Elsevier Inc. All rights reserved.

Structure and Functions of HMGB2 Protein.

High-Mobility Group (HMG) chromosomal proteins are the most numerous nuclear non-histone proteins. HMGB domain proteins are the most abundant and well-studied HMG proteins. They are involved in variety of biological processes. HMGB1 and HMGB2 were the first members of HMGB-family to be discovered and are found in all studied eukaryotes. Despite the high degree of homology, HMGB1 and HMGB2 proteins differ from each other both in structure and functions. In contrast to HMGB2, there is a large pool of works devoted to the HMGB1 protein whose structure-function properties have been described in detail in our previous review in 2020. In this review, we attempted to bring together diverse data about the structure and functions of the HMGB2 protein. The review also describes post-translational modifications of the HMGB2 protein and its role in the development of a number of diseases. Particular attention is paid to its interaction with various targets, including DNA and protein partners. The influence of the level of HMGB2 expression on various processes associated with cell differentiation and aging and its ability to mediate the differentiation of embryonic and adult stem cells are also discussed.

Proteomic understanding of SARS-CoV-2 infection and COVID-19: Biological, diagnostic, and therapeutic perspectives

The research community responded rapidly to the SARS-CoV-2 pandemic with a burst of proteomic studies to understand this new virus. We focus in this chapter on proteomic approaches and advances in the areas of the viral proteome and the viral-host protein-protein interactions exploited to facilitate the pathogenic life cycle of SARS-CoV2. We also outline the proteome and posttranslational protein modifications of infected cells and blood proteomics of COVID-19 patients in relation to pathogenesis, host response, and disease severity. Finally, tools and application for COVID-19 proteomics and implementations in diagnostics and therapies are surveyed before summarizing the present achievements and future perspectives. © 2023 Elsevier Inc. All rights reserved.

Epigenetic features, methods, and implementations associated with COVID-19

The infection and life cycle of severe acute respiratory syndrome coronavirus 2 are widely studied, yet multiple gaps exist in the knowledge that affects therapeutic developments against coronavirus disease 2019 (COVID-19). Predominantly caused by a respiratory virus, COVID-19 is not restricted to the respiratory tract but affects multiple organs of the body including the cardiovascular, neurological, immunological, and renal systems. COVID-19 affects all age groups, although the elderly population inherently presenting with multiple comorbidities are disproportionately affected. The majority of the patients experience mild symptoms, although moderate, severe, and critical symptoms occur in a smaller group of patients. Interestingly, the effects of the disease can be acute or chronic and present an ongoing health care challenge. Epigenetic mechanisms of COVID-19 (DNA methylation, histone posttranslational modifications, histone citrullination, etc.) are an emerging field and present enormous potential toward the medical management of COVID-19. Angiotensin converting enzyme 2, an important protein in the cardiovascular system, is a receptor for viral entry into cells, and the epigenetic processes that regulate this protein have been widely studied. Identification of the epitranscriptomic profile has led to the identification of putative biomarkers for disease diagnosis and trials of novel epidrugs for targeted therapy. © 2023 Elsevier Inc. All rights reserved.

Myxobacterial depsipeptide chondramides interrupt SARS-CoV-2 entry by targeting its broad, cell tropic spike protein.

The severity of the COVID-19 pandemic has necessitated the search for drugs against SARS-CoV-2. In this study, we explored via in silico approaches myxobacterial secondary metabolites against various receptor-binding regions of SARS-CoV-2 spike which are responsible in recognition and attachment to host cell receptors mechanisms, namely ACE2, GRP78, and NRP1. In general, cyclic depsipeptide chondramides conferred high affinities toward the spike RBD, showing strong binding to the known viral hot spots Arg403, Gln493 and Gln498 and better selectivity compared to most host cell receptors studied. Among them, chondramide C3 (1) exhibited a binding energy which remained relatively constant when docked against most of the spike variants. Chondramide C (2) on the other hand exhibited strong affinity against spike variants identified in the United Kingdom (N501Y), South Africa (N501Y, E484K, K417N) and Brazil (N501Y, E484K, K417T). Chondramide C6 (9) showed highest BE towards GRP78 RBD. Molecular dynamics simulations were also performed for chondramides 1 and 2 against SARS-CoV-2 spike RBD of the Wuhan wild-type and the South African variant, respectively, where resulting complexes demonstrated dynamic stability within a 120-ns simulation time. Protein-protein binding experiments using HADDOCK illustrated weaker binding affinity for complexed chondramide ligands in the RBD against the studied host cell receptors. The chondramide derivatives in general possessed favorable pharmacokinetic properties, highlighting their potential as prototypic anti-COVID-19 drugs limiting viral attachment and possibly minimizing viral infection.Communicated by Ramaswamy H. Sarma.

SARS-CoV-2 SUD2 and Nsp5 Conspire to Boost Apoptosis of Respiratory Epithelial Cells via an Augmented Interaction with the G-Quadruplex of BclII.

The molecular mechanisms underlying how SUD2 recruits other proteins of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) to exert its G-quadruplex (G4)-dependent pathogenic function is unknown. Herein, Nsp5 was singled out as a binding partner of the SUD2-N+M domains (SUD2core) with high affinity, through the surface located crossing these two domains. Biochemical and fluorescent assays demonstrated that this complex also formed in the nucleus of living host cells. Moreover, the SUD2core-Nsp5 complex displayed significantly enhanced selective binding affinity for the G4 structure in the BclII promoter than did SUD2core alone. This increased stability exhibited by the tertiary complex was rationalized by AlphaFold2 and molecular dynamics analysis. In line with these molecular interactions, downregulation of BclII and subsequent augmented apoptosis of respiratory cells were both observed. These results provide novel information and a new avenue to explore therapeutic strategies targeting SARS-CoV-2. IMPORTANCE SUD2, a unique protein domain closely related to the pathogenesis of SARS-CoV-2, has been reported to bind with the G-quadruplex (G4), a special noncanonical DNA structure endowed with important functions in regulating gene expression. However, the interacting partner of SUD2, among other SARS-CoV-2 Nsps, and the resulting functional consequences remain unknown. Here, a stable complex formed between SUD2 and Nsp5 was fully characterized both in vitro and in host cells. Moreover, this complex had a significantly enhanced binding affinity specifically targeting the Bcl2G4 in the promoter region of the antiapoptotic gene BclII, compared with SUD2 alone. In respiratory epithelial cells, the SUD2-Nsp5 complex promoted BclII-mediated apoptosis in a G4-dependent manner. These results reveal fresh information about matched multicomponent interactions, which can be parlayed to develop new therapeutics for future relevant viral disease.

Modeling of SARS-CoV-2 Virus Proteins: Implications on Its Proteome.

COronaVIrus Disease 19 (COVID-19) is a severe acute respiratory syndrome (SARS) caused by a group of beta coronaviruses, SARS-CoV-2. The SARS-CoV-2 virus is similar to previous SARS- and MERS-causing strains and has infected nearly six hundred and fifty million people all over the globe, while the death toll has crossed the six million mark (as of December, 2022). In this chapter, we look at how computational modeling approaches of the viral proteins could help us understand the various processes in the viral life cycle inside the host, an understanding of which might provide key insights in mitigating this and future threats. This understanding helps us identify key targets for the purpose of drug discovery and vaccine development.

Pathogen-driven cancers from a structural perspective: Targeting host-pathogen protein-protein interactions.

Host-pathogen interactions (HPIs) affect and involve multiple mechanisms in both the pathogen and the host. Pathogen interactions disrupt homeostasis in host cells, with their toxins interfering with host mechanisms, resulting in infections, diseases, and disorders, extending from AIDS and COVID-19, to cancer. Studies of the three-dimensional (3D) structures of host-pathogen complexes aim to understand how pathogens interact with their hosts. They also aim to contribute to the development of rational therapeutics, as well as preventive measures. However, structural studies are fraught with challenges toward these aims. This review describes the state-of-the-art in protein-protein interactions (PPIs) between the host and pathogens from the structural standpoint. It discusses computational aspects of predicting these PPIs, including machine learning (ML) and artificial intelligence (AI)-driven, and overviews available computational methods and their challenges. It concludes with examples of how theoretical computational approaches can result in a therapeutic agent with a potential of being used in the clinics, as well as future directions.

SARS-COV-2 Coronavirus Papain-like Protease PLpro as an Antiviral Target for Inhibitors of Active Site and Protein-Protein Interactions.

The papain-like protease PLpro of the SARS-CoV-2 coronavirus is a multifunctional enzyme that catalyzes the proteolytic processing of two viral polyproteins, pp1a and pp1ab. PLpro also cleaves peptide bonds between host cell proteins and ubiquitin (or ubiquitin-like proteins), which is associated with a violation of immune processes. Nine structures of the most effective inhibitors of the PLpro active center were prioritized according to the parameters of biochemical (IC 50) and cellular tests to assess the suppression of viral replication (EC 50) and cytotoxicity (CC 50). A literature search has shown that PLpro can interact with at least 60 potential protein partners in cells, 23 of which are targets for other viral proteins (human papillomavirus and Epstein-Barr virus). The analysis of protein-protein interactions showed that the proteins USP3, UBE2J1, RCHY1, and FAF2 involved in deubiquitinylation and ubiquitinylation processes contain the largest number of bonds with other proteins; the interaction of viral proteins with them can affect the architecture of the entire network of protein-protein interactions. Using the example of a spatial model of the PLpro/ubiquitin complex and a set of 154 naturally occurring compounds with known antiviral activity, 13 compounds (molecular masses in the range of 454-954 Da) were predicted as potential PLpro inhibitors. These compounds bind to the "hot" amino acid residues of the protease at the positions Gly163, Asp164, Arg166, Glu167, and Tyr264 involved in the interaction with ubiquitin. Thus, pharmacological effects on peripheral PLpro sites, which play important roles in binding protein substrates, may be an additional target-oriented antiviral strategy.

Investigating the competition between ACE2 natural molecular interactors and SARS-CoV-2 candidate inhibitors.

The SARS-CoV-2 pandemic still poses a threat to the global health as the virus continues spreading in most countries. Therefore, the identification of molecules capable of inhibiting the binding between the ACE2 receptor and the SARS-CoV-2 spike protein is of paramount importance. Recently, two DNA aptamers were designed with the aim to inhibit the interaction between the ACE2 receptor and the spike protein of SARS-CoV-2. Indeed, the two molecules interact with the ACE2 receptor in the region around the K353 residue, preventing its binding of the spike protein. If on the one hand this inhibition process hinders the entry of the virus into the host cell, it could lead to a series of side effects, both in physiological and pathological conditions, preventing the correct functioning of the ACE2 receptor. Here, we discuss through a computational study the possible effect of these two very promising DNA aptamers, investigating all possible interactions between ACE2 and its experimentally known molecular partners. Our in silico predictions show that some of the 10 known molecular partners of ACE2 could interact, physiologically or pathologically, in a region adjacent to the K353 residue. Thus, the curative action of the proposed DNA aptamers could recruit ACE2 from its biological functions.

Timeline of changes in spike conformational dynamics in emergent SARS-CoV-2 variants reveal progressive stabilization of trimer stalk with altered NTD dynamics.

SARS-CoV-2 emergent variants are characterized by increased viral fitness and each shows multiple mutations predominantly localized to the spike (S) protein. Here, amide hydrogen/deuterium exchange mass spectrometry has been applied to track changes in S dynamics from multiple SARS-CoV-2 variants. Our results highlight large differences across variants at two loci with impacts on S dynamics and stability. A significant enhancement in stabilization first occurred with the emergence of D614G S followed by smaller, progressive stabilization in subsequent variants. Stabilization preceded altered dynamics in the N-terminal domain, wherein Omicron BA.1 S showed the largest magnitude increases relative to other preceding variants. Changes in stabilization and dynamics resulting from S mutations detail the evolutionary trajectory of S in emerging variants. These carry major implications for SARS-CoV-2 viral fitness and offer new insights into variant-specific therapeutic development.

Liaisons dangereuses: Intrinsic Disorder in Cellular Proteins Recruited to Viral Infection-Related Biocondensates.

Liquid-liquid phase separation (LLPS) is responsible for the formation of so-called membrane-less organelles (MLOs) that are essential for the spatio-temporal organization of the cell. Intrinsically disordered proteins (IDPs) or regions (IDRs), either alone or in conjunction with nucleic acids, are involved in the formation of these intracellular condensates. Notably, viruses exploit LLPS at their own benefit to form viral replication compartments. Beyond giving rise to biomolecular condensates, viral proteins are also known to partition into cellular MLOs, thus raising the question as to whether these cellular phase-separating proteins are drivers of LLPS or behave as clients/regulators. Here, we focus on a set of eukaryotic proteins that are either sequestered in viral factories or colocalize with viral proteins within cellular MLOs, with the primary goal of gathering organized, predicted, and experimental information on these proteins, which constitute promising targets for innovative antiviral strategies. Using various computational approaches, we thoroughly investigated their disorder content and inherent propensity to undergo LLPS, along with their biological functions and interactivity networks. Results show that these proteins are on average, though to varying degrees, enriched in disorder, with their propensity for phase separation being correlated, as expected, with their disorder content. A trend, which awaits further validation, tends to emerge whereby the most disordered proteins serve as drivers, while more ordered cellular proteins tend instead to be clients of viral factories. In light of their high disorder content and their annotated LLPS behavior, most proteins in our data set are drivers or co-drivers of molecular condensation, foreshadowing a key role of these cellular proteins in the scaffolding of viral infection-related MLOs.

SARS-CoV-2 ORF8 dimerization and binding mode analysis with class I MHC: computational approaches to identify COVID-19 inhibitors.

SARS-CoV-2 encodes eight accessory proteins, one of which, ORF8, has a poorly conserved sequence with SARS-CoV and its role in viral pathogenicity has recently been identified. ORF8 in SARS-CoV-2 has a unique functional feature that allows it to form a dimer structure linked by a disulfide bridge between Cys20 and Cys20 (S-S). This study provides structural characterization of natural mutant variants as well as the identification of potential drug candidates capable of binding directly to the interchain disulfide bridge. The lead compounds reported in this work have a tendency to settle in the dimeric interfaces by direct interaction with the disulfide bridge. These molecules may disturb the dimer formation and may have an inhibition impact on its potential functional role in host immune evasion and virulence pathogenicity. This work provides detailed insights on the sequence and structural variability through computational mutational studies, as well as potent drug candidates with the ability to interrupt the intermolecular disulfide bridge formed between Cys20 and Cys20. Furthermore, the interactions of ORF8 peptides complexed with MHC-1 is studied, and the binding mode reveals that certain ORF8 peptides bind to MHC-1 in a manner similar to other viral peptides. Overall, this study is a narrative of various computational approaches used to provide detailed structural insights into SARS-CoV-2 ORF8 interchain disulfide bond disruptors.

Recent computational drug repositioning strategies against SARS-CoV-2.

Since COVID-19 emerged in 2019, significant levels of suffering and disruption have been caused on a global scale. Although vaccines have become widely used, the virus has shown its potential for evading immunities or acquiring other novel characteristics. Whether current drug treatments are still effective for people infected with Omicron remains unclear. Due to the long development cycles and high expense requirements of de novo drug development, many researchers have turned to consider drug repositioning in the search to find effective treatments for COVID-19. Here, we review such drug repositioning and combination efforts towards providing better handling. For potential drugs under consideration, aspects of both structure and function require attention, with specific categories of sequence, expression, structure, and interaction, the key parameters for investigation. For different data types, we show the corresponding differing drug repositioning methods that have been exploited. As incorporating drug combinations can increase therapeutic efficacy and reduce toxicity, we also review computational strategies to reveal drug combination potential. Taken together, we found that graph theory and neural network were the most used strategy with high potential towards drug repositioning for COVID-19. Integrating different levels of data may further improve the success rate of drug repositioning.

ChikvInt: a Chikungunya virus-host protein-protein interaction database.

Chikungunya is a fast-mutating virus causing Chikungunya virus disease (ChikvD) with a significant load of disability-adjusted life years (DALY) around the world. The outbreak of this virus is significantly higher in the tropical countries. Several experiments have identified crucial viral-host protein-protein interactions (PPIs) between Chikungunya Virus (Chikv) and the human host. However, no standard database that catalogs this PPI information exists. Here we develop a Chikv-Human PPI database, ChikvInt, to facilitate understanding ChikvD disease pathogenesis and the progress of vaccine studies. ChikvInt consists of 109 interactions and is available at www.chikvint.com.

Role of cholesterol-recognition motifs in the infectivity of SARS-CoV-2 variants.

The presence of linear amino acid motifs with the capacity to recognize the neutral lipid cholesterol, known as Cholesterol Recognition/interaction Amino acid Consensus sequence (CRAC), and its inverse or mirror image, CARC, has recently been reported in the primary sequence of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike S homotrimeric glycoprotein. These motifs also occur in the two other pathogenic coronaviruses, SARS-CoV, and Middle-East respiratory syndrome CoV (MERS-CoV), most conspicuously in the transmembrane domain, the fusion peptide, the amino-terminal domain, and the receptor binding domain of SARS-CoV-2 S protein. Here we analyze the presence of cholesterol-recognition motifs in these key regions of the spike glycoprotein in the pathogenic CoVs. We disclose the inherent pathophysiological implications of the cholesterol motifs in the virus-host cell interactions and variant infectivity.

Formal Concept Analysis Applications in Bioinformatics

The bioinformatics discipline seeks to solve problems in biology with computational theories and methods. Formal concept analysis (FCA) is one such theoretical model, based on partial orders. FCA allows the user to examine the structural properties of data based on which subsets of the dataset depend on each other. This article surveys the current literature related to the use of FCA for bioinformatics. The survey begins with a discussion of FCA, its hierarchical advantages, several advanced models of FCA, and lattice management strategies. It then examines how FCA has been used in bioinformatics applications, followed by future prospects of FCA in those areas. The applications addressed include gene data analysis (with next-generation sequencing), biomarkers discovery, protein-protein interaction, disease analysis (including COVID-19, cancer, and others), drug design and development, healthcare informatics, biomedical ontologies, and phylogeny. Some of the most promising prospects of FCA are identifying influential nodes in a network representing protein-protein interactions, determining critical concepts to discover biomarkers, integrating machine learning and deep learning for cancer classification, and pattern matching for next-generation sequencing. [ FROM AUTHOR]