Identification Of Drug Targets Research Articles

Role of Artificial Intelligence in Drug Discovery and Target Identification in Cancer.

Drug discovery and development (DDD) is a highly complex process that necessitates precise monitoring and extensive data analysis at each stage. Furthermore, the DDD process is both timeconsuming and costly. To tackle these concerns, artificial intelligence (AI) technology can be used, which facilitates rapid and precise analysis of extensive datasets within a limited timeframe. The pathophysiology of cancer disease is complicated and requires extensive research for novel drug discovery and development. The first stage in the process of drug discovery and development involves identifying targets. Cell structure and molecular functioning are complex due to the vast number of molecules that function constantly, performing various roles. Furthermore, scientists are continually discovering novel cellular mechanisms and molecules, expanding the range of potential targets. Accurately identifying the correct target is a crucial step in the preparation of a treatment strategy. Various forms of AI, such as machine learning, neural-based learning, deep learning, and network-based learning, are currently being utilised in applications, online services, and databases. These technologies facilitate the identification and validation of targets, ultimately contributing to the success of projects. This review focuses on the different types and subcategories of AI databases utilised in the field of drug discovery and target identification for cancer.

Siah2- and LRSAM1-mediated K63-linked ubiquitination of snakehead vesiculovirus nucleoprotein facilitates viral replication.

Ubiquitination of viral protein plays an important role in viral replication. However, the ubiquitination of the nucleoprotein (N) of negative-strand RNA viruses has rarely been investigated. This study aimed at investigating the ubiquitination of the N protein of a fish rhabdovirus SHVV (snakehead vesiculovirus), identifying the related host E3 ubiquitin ligases, and determining the role of SHVV N ubiquitination and host E3 ubiquitin ligases in viral replication. We found that SHVV N was ubiquitinated mainly via K63-linked ubiquitination, which was mediated by host E3 ubiquitin ligases Siah2 (Siah E3 ubiquitin protein ligase 2) and LRSAM1 (leucine-rich repeat and sterile alpha motif containing 1). The data suggested that Siah2 and LRSAM1 were hijacked by SHVV to ubiquitinate the N protein for viral replication, which exhibited novel anti-SHVV targets for drug design.

Hereditary Transthyretin Amyloidosis (hATTR) with Polyneuropathy Clusters Are Located in Ancient Mining Districts: A Possible Geochemical Origin of the Disease.

Hereditary transthyretin amyloidosis (hATTR) with polyneuropathy (formerly known as Familial Amyloid Polyneuropathy (FAP)) is an endemic amyloidosis involving the harmful aggregation of proteins, most commonly transthyretin (TTR) but sometimes also apolipoprotein A-1 or gelsolin. hATTR appears to be transmitted as an autosomal dominant trait. Over 100 point mutations have been identified, with the Val30Met substitution being the most common. Yet, the mechanism of pathogenesis and the overall origin of hATTR remain unclear. Here, we argue that hATTR could be related to harmful metal exposure. hATTR incidence is unevenly distributed globally, and the three largest defined clusters exist in Japan, Portugal, and Sweden. All three disease regions are also ancient mining districts with associated metal contamination of the local environment. There are two main mechanisms for how harmful metals, after uptake into tissues and body fluids, could induce hATTR. First, the metals could directly influence the expression, function, and/or aggregation of the proteins involved in hATTR pathology. Such metal-protein interactions might constitute molecular targets for anti-hATTR drug design. Second, metal exposure could induce hATTR -associated genetic mutations, which may have happened several generations ago. These two mechanisms can occur in parallel. In conclusion, the possibility that hATTR could be related to metal exposure in geochemically defined regions deserves further attention.

Identification of therapeutic drug target of Shigella Flexneri serotype X through subtractive genomic approach and in-silico screening based on drug repurposing

Shigellosis, induced by Shigella flexneri, constitutes a significant health burden in developing nations, particularly impacting socioeconomically disadvantaged communities. Designated as the second most prevalent cause of diarrheal illness by the World Health Organization (WHO), it precipitates an estimated 212,000 fatalities annually. Within the spectrum of S. flexneri strains, serotype X is notably pervasive and resilient, yet its comprehensive characterization remains deficient. The present investigation endeavors to discern potential pharmacological targets and repurpose existing drug compounds against S. flexneri serotype X. Employing the framework of subtractive genomics, the study interrogates the reference genome of S. flexneri Serotype X (strain 2,002,017; UP000001884) to delineate its proteome into categories of non-homologous, non-paralogous, essential, virulent, and resistant constituents, thereby facilitating the identification of therapeutic targets. Subsequently, a screening of approximately 9000 compounds from the FDA library against the identified drug target aims to delineate efficacious agents for combating S. flexneri serotype X infections. The application of subtractive genomics methodology yields prognostic insights, unveiling non-paralogous proteins (n = 4122), non-homologues (n = 1803), essential (n = 1246), drug-like (n = 389), resistant (n = 167), alongside 42 virulent proteins within the reference proteome. This iterative process culminates in the identification of Serine O-acetyltransferase as a viable drug target. Subsequent virtual screening endeavors to unearth FDA-approved medicinal compounds capable of inhibiting Serine O-acetyltransferase. Noteworthy candidates such as DB12983, DB15085, DB16098, DB16185, and DB16262 emerge, exhibiting potential for mitigating S. flexneri Serotype X. Despite the auspicious findings, diligent scrutiny is imperative to ascertain the efficacy and safety profile of the proposed drug candidates vis-à-vis S. flexneri.

Integrating genome-wide CRISPR screens and in silico drug profiling for targeted antidote development.

Numerous toxins threaten humans, but specific antidotes are unavailable for most of them. Although CRISPR screening has aided the discovery of the mechanisms of some toxins, developing targeted antidotes remains a significant challenge. Recently, we established a systematic framework to develop antidotes by combining the identification of novel drug targets by using a genome-wide CRISPR screen with a virtual screen of drugs approved by the US Food and Drug Administration. This approach allows for a comprehensive understanding of toxin mechanisms at the whole-genome level and facilitates the identification of promising antidote drugs targeting specific molecules. Here, we present step-by-step instructions for executing genome-scale CRISPR-Cas9 knockout screens of toxins in HAP1 cells. We also provide detailed guidance for conducting an in silico drug screen and an in vivo drug validation. By using this protocol, it takes ~4 weeks to perform the genome-scale screen, 4 weeks for sequencing and data analysis, 4 weeks to validate candidate genes, 1 week for the virtual screen and 2 weeks for in vitro drug validation. This framework has the potential to accelerate the development of antidotes for a wide range of toxins and can rapidly identify promising drug candidates that are already known to be safe and effective. This could lead to the development of new antidotes much more quickly than traditional methods, protecting lives from diverse toxins and advancing human health.

High-resolution genome-wide mapping of chromosome-arm-scale truncations induced by CRISPR-Cas9 editing.

Clustered regularly interspaced short palindromic repeats (CRISPR)-CRISPR-associated protein 9 (Cas9) is a powerful tool for introducing targeted mutations in DNA, but recent studies have shown that it can have unintended effects such as structural changes. However, these studies have not yet looked genome wide or across data types. Here we performed a phenotypic CRISPR-Cas9 scan targeting 17,065 genes in primary human cells, revealing a 'proximity bias' in which CRISPR knockouts show unexpected similarities to unrelated genes on the same chromosome arm. This bias was found to be consistent across cell types, laboratories, Cas9 delivery methods and assay modalities, and the data suggest that it is caused by telomeric truncations of chromosome arms, with cell cycle and apoptotic pathways playing a mediating role. Additionally, a simple correction is demonstrated to mitigate this pervasive bias while preserving biological relationships. This previously uncharacterized effect has implications for functional genomic studies using CRISPR-Cas9, with applications in discovery biology, drug-target identification, cell therapies and genetic therapeutics.

Well-differentiated G1 and G2 pancreatic neuroendocrine tumors: a meta-analysis of published expanded DNA sequencing data.

Well-differentiated pancreatic neuroendocrine tumors (PNETs) can be non-functional or functional, e.g. insulinoma and glucagonoma. The majority of PNETs are sporadic, but PNETs also occur in hereditary syndromes, primarily multiple endocrine neoplasia type 1 (MEN1). The Knudson hypothesis stated a second, somatic hit in MEN1 as the cause of PNETs of MEN1 syndrome. In the recent years, reports on genetic somatic events in both sporadic and hereditary PNETs have emerged, providing a basis for a more detailed molecular understanding of the pathophysiology. In this systematic review and meta-analysis, we made a collation and statistical analysis of aggregated frequent genetic alterations and potential driver events in human grade G1/G2 PNETs. A systematic search was performed in concordance with the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) reporting guidelines of 2020.A search in Pubmed for published studies using whole exome, whole genome, or targeted gene panel (+400 genes) sequencing of human G1/G2 PNETs was conducted at the 25th of September 2023. Fourteen datasets from published studies were included with data on 221 patients and 225 G1/G2 PNETs, which were divided into sporadic tumors, and hereditary tumors with pre-disposing germline variants, and tumors with unknown germline status. Further, non-functioning and functioning PNETs were distinguished into two groups for pathway evaluation. The collated genetical analyses were conducted using the 'maftools' R-package. Sporadic PNETs accounted 72.0% (162/225), hereditary PNETs 13.3% (30/225), unknown germline status 14.7% (33/225). The most frequently altered gene was MEN1, with somatic variants and copy number variations in overall 42% (95/225); hereditary PNETs (germline variations in MEN1, VHL, CHEK2, BRCA2, PTEN, CDKN1B, and/or MUTYH) 57% (16/30); sporadic PNETs 36% (58/162); unknown germline status 64% (21/33). The MEN1 point mutations/indels were distributed throughout MEN1. Overall, DAXX (16%, 37/225) and ATRX-variants (12%, 27/225) were also abundant with missense mutations clustered in mutational hotspots associated with histone binding, and translocase activity, respectively. DAXX mutations occurred more frequently in PNETs with MEN1 mutations, p<0.05. While functioning PNETs shared few variated genes, non-functioning PNETs had more recurrent variations in genes associated with the Phosphoinositide 3-kinase, Wnt, NOTCH, and Receptor Tyrosine Kinase-Ras signaling onco-pathways. The somatic genetic alterations in G1/G2 PNETs are diverse, but with distinct differences between sporadic vs. hereditary, and functional vs. non-functional PNETs. Increased understanding of the genetic alterations may lead to identification of more drivers and driver hotspots in the tumorigenesis in well-differentiated PNETs, potentially giving a basis for the identification of new drug targets. (Funded by Novo Nordisk Foundation, grant number NNF19OC0057915).

Core proteome mediated subtractive approach for the identification of potential therapeutic drug target against the honeybee pathogen Paenibacillus larvae.

American foulbrood (AFB), caused by the highly virulent, spore-forming bacterium Paenibacillus larvae, poses a significant threat to honey bee brood. The widespread use of antibiotics not only fails to effectively combat the disease but also raises concerns regarding honey safety. The current computational study was attempted to identify a novel therapeutic drug target against P. larvae, a causative agent of American foulbrood disease in honey bee. We investigated effective novel drug targets through a comprehensive in silico pan-proteome and hierarchal subtractive sequence analysis. In total, 14 strains of P.larvae genomes were used to identify core genes. Subsequently, the core proteome was systematically narrowed down to a single protein predicted as the potential drug target. Alphafold software was then employed to predict the 3D structure of the potential drug target. Structural docking was carried out between a library of phytochemicals derived from traditional Chinese flora (n>36,000) and the potential receptor using Autodock tool 1.5.6. Finally, molecular dynamics (MD) simulation study was conducted using GROMACS to assess the stability of the best-docked ligand. Proteome mining led to the identification of Ketoacyl-ACP synthase III as a highly promising therapeutic target, making it a prime candidate for inhibitor screening. The subsequent virtual screening and MD simulation analyses further affirmed the selection of ZINC95910054 as a potent inhibitor, with the lowest binding energy. This finding presents significant promise in the battle against P. larvae. Computer aided drug design provides a novel approach for managing American foulbrood in honey bee populations, potentially mitigating its detrimental effects on both bee colonies and the honey industry.

Automating biomedical literature review for rapid drug discovery: Leveraging GPT-4 to expedite pandemic response

ObjectiveThe rapid expansion of the biomedical literature challenges traditional review methods, especially during outbreaks of emerging infectious diseases when quick action is critical. Our study aims to explore the potential of ChatGPT to automate the biomedical literature review for rapid drug discovery. Materials and MethodsWe introduce a novel automated pipeline helping to identify drugs for a given virus in response to a potential future global health threat. Our approach can be used to select PubMed articles identifying a drug target for the given virus. We tested our approach on two known pathogens: SARS-CoV-2, where the literature is vast, and Nipah, where the literature is sparse. Specifically, a panel of three experts reviewed a set of PubMed articles and labeled them as either describing a drug target for the given virus or not. The same task was given to the automated pipeline and its performance was based on whether it labeled the articles similarly to the human experts. We applied a number of prompt engineering techniques to improve the performance of ChatGPT. ResultsOur best configuration used GPT-4 by OpenAI and achieved an out-of-sample validation performance with accuracy/F1-score/sensitivity/specificity of 92.87%/88.43%/83.38%/97.82% for SARS-CoV-2 and 87.40%/73.90%/74.72%/91.36% for Nipah. ConclusionThese results highlight the utility of ChatGPT in drug discovery and development and reveal their potential to enable rapid drug target identification during a pandemic-level health emergency.

#1138 Urine proteomic characterisation of DahlS rat model shows robust translatability of molecular profile with human hypertensive kidney damage

Abstract Background and Aims The role of hypertension in CKD is complex. Hypertensive nephropathy or arterionephrosclerosis is caused by prolonged periods of poorly controlled high blood pressure and is characterized by gradual loss of kidney function with various grades of proteinuria over a period of many years. This complexity requires novel analysis strategies for a successful understanding of pathomechanisms, drug target and biomarker identification including well characterized translatable models where the DalhS rats could be relevant. Method We analysed the urinary proteome of hypertensive Dahl S rats at baseline and 2, 4, and 7-weeks of high salt diet (4%, n = 5 in each cohort). A TMT MS/LC method was performed to obtain proteomics profile of the urine, to then filter, normalize and apply a differential analysis based on linear models and empirical Bayes statistics (limma) in R language. Additionally, we did a pathway enrichment analysis using IPA. Finally, to assess the translatability of the findings, we compared the rat urinary proteome data with publicly available urinary proteome from patients with CKD. Results The DalhS rats developed hypertension, proteinuria, and glomerulosclerosis. In the urine, we identified a total of 36 differentially expressed proteins comparing 4 weeks of high salt diet (HSD) versus the baseline, and 85 differentially expressed proteins at 7 weeks of HSD. Pathway analysis of the differentially abundant urinary proteome showed robust enrichment of pathways related to hypertensive kidney damage including ‘acute phase response signaling’, a pathway that activates rapidly in inflammatory settings and can sustain chronically, and liver / retinoid-x-receptor (LXR/RXR) activation​, which was also activated. Additionally, the coagulation system and complement cascade also showed positive z-scores in high-salt-exposed rats (after 7 weeks). Moreover, we observed an inactivation of several immune related pathways such as IL-12 signaling and STAT3 pathways. Finally, analysis of publicly available data of the urinary proteome of CKD patients (PMID 34963468, N = 40) exhibited enrichment of more than 20 pathways in common with the DalhS cohort, suggesting a high translatability between the urinary proteome of the animal model and patients. Specifically, we observe activation of acute phase response signalling, LXR/RXR activation, and complement cascade in DalhS and human datasets, as well as inhibition of IL-12 and STAT3 signaling pathways. Conclusion Molecules targeting the complement pathway are under intensive development. Prior development of LXR agonists have been limited by Ph1 findings. The findings with acute-phase response, IL-12 and STAT3 pathways suggests the importance of immune-driven damage mechanisms in hypertensive CKD. Identified proteins and enriched pathways in this work provide a rich resource for future investigation. In conclusion, the Dahl S rat model urinary proteome captures multiple underlying mechanisms of the human hypertensive CKD phenotype.

Identification of Potential Drug Targets of Calix[4]arene by Reverse Docking.

Calixarenes are displaying great potential for the development of new drug delivery systems, diagnostic imaging, biosensing devices and inhibitors of biological processes. In particular, calixarene derivatives are able to interact with many different enzymes and function as inhibitors. By screening of the potential drug target database (PDTD) with a reverse docking procedure, we identify and discuss a selection of 100 proteins that interact strongly with calix[4]arene. We also discover that leucine (23.5%), isoleucine (11.3%), phenylalanines (11.3%) and valine (9.5%) are the most frequent binding residues followed by hydrophobic cysteines and methionines and aromatic histidines, tyrosines and tryptophanes. Top binders are peroxisome proliferator-activated receptors that alreadyaretargeted by commercial drugs, demonstrating the practical interest in calix[4]arene. Nuclear receptors, potassium channel, several carrier proteins, a variety of cancer-related proteins and viral proteins are prominent in the list. It is concluded that calix[4]arene, which is characterized by facile access, well-defined conformational characteristics, and ease of functionalization at both the lower and higher rims, could be a potential lead compound for the development of enzyme inhibitors and theranostic platforms.

Subtractive Proteomics and Reverse-Vaccinology Approaches for Novel Drug Target Identification and Chimeric Vaccine Development against Bartonella henselae Strain Houston-1.

Bartonella henselae is a Gram-negative bacterium causing a variety of clinical symptoms, ranging from cat-scratch disease to severe systemic infections, and it is primarily transmitted by infected fleas. Its status as an emerging zoonotic pathogen and its capacity to persist within host erythrocytes and endothelial cells emphasize its clinical significance. Despite progress in understanding its pathogenesis, limited knowledge exists about the virulence factors and regulatory mechanisms specific to the B. henselae strain Houston-1. Exploring these aspects is crucial for targeted therapeutic strategies against this versatile pathogen. Using reverse-vaccinology-based subtractive proteomics, this research aimed to identify the most antigenic proteins for formulating a multi-epitope vaccine against the B. henselae strain Houston-1. One crucial virulent and antigenic protein, the PAS domain-containing sensor histidine kinase protein, was identified. Subsequently, the identification of B-cell and T-cell epitopes for the specified protein was carried out and the evaluated epitopes were checked for their antigenicity, allergenicity, solubility, MHC binding capability, and toxicity. The filtered epitopes were merged using linkers and an adjuvant to create a multi-epitope vaccine construct. The structure was then refined, with 92.3% of amino acids falling within the allowed regions. Docking of the human receptor (TLR4) with the vaccine construct was performed and demonstrated a binding energy of -1047.2 Kcal/mol with more interactions. Molecular dynamic simulations confirmed the stability of this docked complex, emphasizing the conformation and interactions between the molecules. Further experimental validation is necessary to evaluate its effectiveness against B. henselae.

An integrated in-silico approach for drug target identification in human pathogen Shigella dysenteriae.

Shigella dysenteriae, is a Gram-negative bacterium that emerged as the second most significant cause of bacillary dysentery. Antibiotic treatment is vital in lowering Shigella infection rates, yet the growing global resistance to broad-spectrum antibiotics poses a significant challenge. The persistent multidrug resistance of S. dysenteriae complicates its management and control. Hence, there is an urgent requirement to discover novel therapeutic targets and potent medications to prevent and treat this disease. Therefore, the integration of bioinformatics methods such as subtractive and comparative analysis provides a pathway to compute the pan-genome of S. dysenteriae. In our study, we analysed a dataset comprising 27 whole genomes. The S. dysenteriae strain SD197 was used as the reference for determining the core genome. Initially, our focus was directed towards the identification of the proteome of the core genome. Moreover, several filters were applied to the core genome, including assessments for non-host homology, protein essentiality, and virulence, in order to prioritize potential drug targets. Among these targets were Integration host factor subunit alpha and Tyrosine recombinase XerC. Furthermore, four drug-like compounds showing potential inhibitory effects against both target proteins were identified. Subsequently, molecular docking analysis was conducted involving these targets and the compounds. This initial study provides the list of novel targets against S. dysenteriae. Conclusively, future in vitro investigations could validate our in-silico findings and uncover potential therapeutic drugs for combating bacillary dysentery infection.

MISP Is Overexpressed in Intestinal Metaplasia and Gastric Cancer.

Gastric cancer is the fifth most common cancer and the fourth cause of global cancer mortality. The identification of new biomarkers and drug targets is crucial to allow the better prognosis and treatment of patients. The mitotic spindle positioning (MISP) protein has the function of correcting mitotic spindle positioning and centrosome clustering and has been implicated in the cytokinesis and migration of cancer cells. The goal of this work was to evaluate the expression and clinical relevance of MISP in gastric cancer. MISP expression was evaluated by immunohistochemistry in a single hospital series (n = 286) of gastric adenocarcinomas and compared with normal gastric mucosa and intestinal metaplasia, a preneoplastic lesion. MISP was detected on the membrane in 83% of the cases, being overexpressed in gastric cancer compared to normal gastric mucosa (n = 10). Its expression was negatively associated with diffuse and poorly cohesive types. On the other hand, it was strongly expressed in intestinal metaplasia where it was associated with MUC2 and CDX2 expression. Furthermore, when we silenced MISP in vitro, a significant decrease in the viability of gastric carcinoma cells was observed. In conclusion, MISP is overexpressed in gastric cancer, being associated with an intestinal phenotype in gastric carcinogenesis and having a role in cellular proliferation.

GDP-mannose pyrophosphorylase is an efficient target in Xanthomonas citri for citrus canker control.

Xcc causes citrus canker, a threat to citrus production, which has been managed with copper, being required a more sustainable alternative for the disease control. XanB was previously found on the surface of Xcc, interacting with the host and displaying PMI and GMP activities. We demonstrated by xanB deletion and complementation that GMP activity plays a critical role in Xcc pathogenicity, particularly in biofilm formation. XanB homology modeling was performed, and in silico virtual screening led to carbohydrate-derived compounds able to inhibit XanB activity and reduce disease symptoms by 95%. XanB emerges as a promising target for drug design for control of citrus canker and other economically important diseases caused by Xanthomonas sp.

Comparative toxicity assessment of fisetin-aided artificial intelligence-assisted drug design targeting epibulbar dermoid through phytochemicals

Abstract Epibulbar dermoid, benign tumors resulting from the improper function of the pathogenic protein Patched 1 (PTCH1), surgical interventions are employed to mitigate the lethal effects. However, these procedures often lead to complications such as scleral weakening and corneal astigmatism. Due to the absence of effective medical treatments, there is a critical need for novel approaches to prevent the spread of this disease. This study not only fills a key gap in medical therapy for epibulbar dermoid, but it also demonstrates AI’s transformational potential in drug development. Employing an AI drug design service, three efficient de novo therapeutic compounds targeting the PTCH1 protein were crafted with Fisetin. The method included obtaining the PTCH1 protein sequence from NCBI, computing physiochemical parameters with Expasy Protparam, and estimating the 3D structure with SWISS-MODEL. Quality assessment, binding site prediction, drug design with WADDAICA, Lipinski’s Rule of 5, toxicity screening, ADMET evaluation, docking analysis with CB-dock and Patch dock, and MD simulations with IMOD all work together to provide an adequate basis for the development and validation of AI-designed phytochemical medicines for epibulbar dermoid. Comprehensive pharmacokinetic and toxicity assessments demonstrated the outstanding absorbency of AI-designed Fisetin in the blood–brain barrier, emphasizing its non-toxic nature. The AI-designed Fisetin exhibits a notable affinity of approximately −9.2 kcal/mol during docking with the PTCH1 protein, suggesting its potential as a drug. While the in silico results indicate efficiency and safety, the necessity for in vivo testing is acknowledged to validate these promising findings. This research significantly advances the frontier of knowledge by proposing a non-invasive solution to epibulbar dermoid, addressing current limitations, and offering potential alternatives to conventional surgical interventions.

Mechanisms of ligand recognition and activation of melanin-concentrating hormone receptors

Melanin-concentrating hormone (MCH) is a cyclic neuropeptide that regulates food intake, energy balance, and other physiological functions by stimulating MCHR1 and MCHR2 receptors, both of which are class A G protein-coupled receptors. MCHR1 predominately couples to inhibitory G protein, Gi/o, and MCHR2 can only couple to Gq/11. Here we present cryo-electron microscopy structures of MCH-activated MCHR1 with Gi and MCH-activated MCHR2 with Gq at the global resolutions of 3.01 Å and 2.40 Å, respectively. These structures reveal that MCH adopts a consistent cysteine-mediated hairpin loop configuration when bound to both receptors. A central arginine from the LGRVY core motif between the two cysteines of MCH penetrates deeply into the transmembrane pocket, triggering receptor activation. Integrated with mutational and functional insights, our findings elucidate the molecular underpinnings of ligand recognition and MCH receptor activation and offer a structural foundation for targeted drug design.

Functional Genomics and Insights into the Pathogenesis and Treatment of Psoriasis.

Psoriasis is a lifelong, systemic, immune mediated inflammatory skin condition, affecting 1-3% of the world's population, with an impact on quality of life similar to diseases like cancer or diabetes. Genetics are the single largest risk factor in psoriasis, with Genome-Wide Association (GWAS) studies showing that many psoriasis risk genes lie along the IL-23/Th17 axis. Potential psoriasis risk genes determined through GWAS can be annotated and characterised using functional genomics, allowing the identification of novel drug targets and the repurposing of existing drugs. This review is focused on the IL-23/Th17 axis, providing an insight into key cell types, cytokines, and intracellular signaling pathways involved. This includes examination of currently available biological treatments, time to relapse post drug withdrawal, and rates of primary/secondary drug failure, showing the need for greater understanding of the underlying genetic mechanisms of psoriasis and how they can impact treatment. This could allow for patient stratification towards the treatment most likely to reduce the burden of disease for the longest period possible.

MUC1 promotes cervical squamous cell carcinoma through ERK phosphorylation-mediated regulation of ITGA2/ITGA3

In contrast to the decreasing trends in developed countries, the incidence and mortality rates of cervical squamous cell carcinoma in China have increased significantly. The screening and identification of reliable biomarkers and candidate drug targets for cervical squamous cell carcinoma are urgently needed to improve the survival rate and quality of life of patients. In this study, we demonstrated that the expression of MUC1 was greater in neoplastic tissues than in non-neoplastic tissues of the cervix, and cervical squamous cell carcinoma patients with high MUC1 expression had significantly worse overall survival than did those with low MUC1 expression, indicating its potential for early diagnosis of cervical squamous cell carcinoma. Next, we explored the regulatory mechanism of MUC1 in cervical squamous cell carcinoma. MUC1 could upregulate ITGA2 and ITGA3 expression via ERK phosphorylation, promoting the proliferation and metastasis of cervical cancer cells. Further knockdown of ITGA2 and ITGA3 significantly inhibited the tumorigenesis of cervical cancer cells. Moreover, we designed a combination drug regimen comprising MUC1-siRNA and a novel ERK inhibitor in vivo and found that the combination of these drugs achieved better results in animals with xenografts than did MUC1 alone. Overall, we discovered a novel regulatory pathway, MUC1/ERK/ITGA2/3, in cervical squamous cell carcinoma that may serve as a potential biomarker and therapeutic target in the future.

Assessing Target Specificity of the Small Molecule Inhibitor MARIMASTAT to Snake Venom Toxins: A Novel Application of Thermal Proteome Profiling

New treatments that circumvent the pitfalls of traditional antivenom therapies are critical to address the problem of snakebite globally. Numerous snake venom toxin inhibitors have shown promising cross-species neutralization of medically significant venom toxins in vivo and in vitro. The development of high-throughput approaches for the screening of such inhibitors could accelerate their identification, testing, and implementation and thus holds exciting potential for improving the treatments and outcomes of snakebite envenomation worldwide. Energetics-based proteomic approaches, including thermal proteome profiling and proteome integral solubility alteration (PISA) assays, represent “deep proteomics” methods for high throughput, proteome-wide identification of drug targets and ligands. In the following study, we apply thermal proteome profiling and PISA methods to characterize the interactions between venom toxin proteoforms in Crotalus atrox (Western Diamondback Rattlesnake) and the snake venom metalloprotease (SVMP) inhibitor marimastat. We investigate its venom proteome-wide effects and characterize its interactions with specific SVMP proteoforms, as well as its potential targeting of non-SVMP venom toxin families. We also compare the performance of PISA thermal window and soluble supernatant with insoluble precipitate using two inhibitor concentrations, providing the first demonstration of the utility of a sensitive high-throughput PISA-based approach to assess the direct targets of small molecule inhibitors for snake venom.