Identification of Tumor Budding-Associated Genes in Breast Cancer through Transcriptomic Profiling and Network Diffusion Analysis.

Breast cancer has the highest diagnosis rate among all cancers. Tumor budding (TB) is recognized as a recent prognostic marker. Identifying genes specific to high-TB samples is crucial for hindering tumor progression and metastasis. In this study, we utilized an RNA sequencing technique, called TempO-Seq, to profile transcriptomic data from breast cancer samples, aiming to identify biomarkers for high-TB cases. Through differential expression analysis and mutual information, we identified seven genes (NOL4, STAR, C8G, NEIL1, SLC46A3, FRMD6, and SCARF2) that are potential biomarkers in breast cancer. To gain more relevant proteins, further investigation based on a protein-protein interaction network and the network diffusion technique revealed enrichment in the Hippo signaling and Wnt signaling pathways, promoting tumor initiation, invasion, and metastasis in several cancer types. In conclusion, these novel genes, recognized as overexpressed in high-TB samples, along with their associated pathways, offer promising therapeutic targets, thus advancing treatment and diagnosis for breast cancer.

A k-mer-based bulked segregant analysis approach to map seed traits in unphased heterozygous potato genomes.

High-throughput sequencing-based methods for bulked segregant analysis (BSA) allow for the rapid identification of genetic markers associated with traits of interest. BSA studies have successfully identified qualitative (binary) and quantitative trait loci (QTLs) using QTL mapping. However, most require population structures that fit the models available and a reference genome. Instead, high-throughput short-read sequencing can be combined with BSA of k-mers (BSA-k-mer) to map traits that appear refractory to standard approaches. This method can be applied to any organism and is particularly useful for species with genomes diverged from the closest sequenced genome. It is also instrumental when dealing with highly heterozygous and potentially polyploid genomes without phased haplotype assemblies and for which a single haplotype can control a trait. Finally, it is flexible in terms of population structure. Here, we apply the BSA-k-mer method for the rapid identification of candidate regions related to seed spot and seed size in diploid potato. Using a mixture of F1 and F2 individuals from a cross between 2 highly heterozygous parents, candidate sequences were identified for each trait using the BSA-k-mer approach. Using parental reads, we were able to determine the parental origin of the loci. Finally, we mapped the identified k-mers to a closely related potato genome to validate the method and determine the genomic loci underlying these sequences. The location identified for the seed spot matches with previously identified loci associated with pigmentation in potato. The loci associated with seed size are novel. Both loci are relevant in future breeding toward true seeds in potato.

Immunopathogenesis and Immunogenetic Variants in COVID-19.

Coronavirus disease 2019 (COVID-19) continues to spread globally despite the discovery of vaccines. Many people die due to COVID-19 as a result of catastrophic consequences, such as acute respiratory distress syndrome, pulmonary embolism, and disseminated intravascular coagulation caused by a cytokine storm. Immunopathology and immunogenetic research may assist in diagnosing, predicting, and treating severe COVID-19 and the cytokine storm associated with COVID-19. This paper reviews the immunopathogenesis and immunogenetic variants that play a role in COVID-19. Although various immune-related genetic variants have been investigated in relation to severe COVID-19, the NOD-like receptor protein 3 (NLRP3) and interleukin 18 (IL-18) have not been assessed for their potential significance in the clinical outcome. Here, we a) summarize the current understanding of the immunogenetic etiology and pathophysiology of COVID-19 and the associated cytokine storm; and b) construct and analyze protein-protein interaction (PPI) networks (using enrichment and annotation analysis) based on the NLRP3 and IL18 variants and all genes, which were established in severe COVID-19. Our PPI network and enrichment analyses predict a) useful drug targets to prevent the onset of severe COVID-19, including key antiviral pathways such as Toll-Like-Receptor cascades, NOD-like receptor signaling, RIG-induction of interferon (IFN) α/ß, and interleukin (IL)-1, IL-6, IL-12, IL-18, and tumor necrosis factor signaling; and b) SARS-CoV-2 innate immune evasion and the participation of MYD88 and MAVS in the pathophysiology of severe COVID-19. The PPI network genetic variants may be used to predict more severe COVID-19 outcomes, thereby opening the door for targeted preventive treatments.

Network-based association analysis to infer new disease-gene relationships using large-scale protein interactions.

Protein-protein interactions integrated with disease-gene associations represent important information for revealing protein functions under disease conditions to improve the prevention, diagnosis, and treatment of complex diseases. Although several studies have attempted to identify disease-gene associations, the number of possible disease-gene associations is very small. High-throughput technologies have been established experimentally to identify the association between genes and diseases. However, these techniques are still quite expensive, time consuming, and even difficult to perform. Thus, based on currently available data and knowledge, computational methods have served as alternatives to provide more possible associations to increase our understanding of disease mechanisms. Here, a new network-based algorithm, namely, Disease-Gene Association (DGA), was developed to calculate the association score of a query gene to a new possible set of diseases. First, a large-scale protein interaction network was constructed, and the relationship between two interacting proteins was calculated with regard to the disease relationship. Novel plausible disease-gene pairs were identified and statistically scored by our algorithm using neighboring protein information. The results yielded high performance for disease-gene prediction, with an F-measure of 0.78 and an AUC of 0.86. To identify promising candidates of disease-gene associations, the association coverage of genes and diseases were calculated and used with the association score to perform gene and disease selection. Based on gene selection, we identified promising pairs that exhibited evidence related to several important diseases, e.g., inflammation, lipid metabolism, inborn errors, xanthomatosis, cerebellar ataxia, cognitive deterioration, malignant neoplasms of the skin and malignant tumors of the cervix. Focusing on disease selection, we identified target genes that were important to blistering skin diseases and muscular dystrophy. In summary, our developed algorithm is simple, efficiently identifies disease-gene associations in the protein-protein interaction network and provides additional knowledge regarding disease-gene associations. This method can be generalized to other association studies to further advance biomedical science.

In silico multiple-targets identification for heme detoxification in the human malaria parasite Plasmodium falciparum.

Detoxification of hemoglobin byproducts or free heme is an essential step and considered potential targets for anti-malaria drug development. However, most of anti-malaria drugs are no longer effective due to the emergence and spread of the drug resistant malaria parasites. Therefore, it is an urgent need to identify potential new targets and even for target combinations for effective malaria drug design. In this work, we reconstructed the metabolic networks of Plasmodium falciparum and human red blood cells for the simulation of steady mass and flux flows of the parasite's metabolites under the blood environment by flux balance analysis (FBA). The integrated model, namely iPF-RBC-713, was then adjusted into two stage-specific metabolic models, which first was for the pathological stage metabolic model of the parasite when invaded the red blood cell without any treatment and second was for the treatment stage of the parasite when a drug acted by inhibiting the hemozoin formation and caused high production rate of heme toxicity. The process of identifying target combinations consisted of two main steps. Firstly, the optimal fluxes of reactions in both the pathological and treatment stages were computed and compared to determine the change of fluxes. Corresponding enzymes of the reactions with zero fluxes in the treatment stage but non-zero fluxes in the pathological stage were predicted as a preliminary list of potential targets in inhibiting heme detoxification. Secondly, the combinations of all possible targets listed in the first step were examined to search for the best promising target combinations resulting in more effective inhibition of the detoxification to kill the malaria parasites. Finally, twenty-three enzymes were identified as a preliminary list of candidate targets which mostly were in pyruvate metabolism and citrate cycle. The optimal set of multiple targets for blocking the detoxification was a set of heme ligase, adenosine transporter, myo-inositol 1-phosphate synthase, ferrodoxim reductase-like protein and guanine transporter. In conclusion, the method has shown an effective and efficient way to identify target combinations which are obviously useful in the development of novel antimalarial drug combinations.

Novel p16 binding peptide development for p16-overexpressing cancer cell detection using phage display.

Protein p(16INK4a) (p16) is a well-known biomarker for diagnosis of human papillomavirus (HPV) related cancers. In this work, we identify novel p16 binding peptides by using phage display selection method. A random heptamer phage display library was screened on purified recombinant p16 protein-coated plates to elute only the bound phages from p16 surfaces. Binding affinity of the bound phages was compared with each other by enzyme-linked immunosorbent assay (ELISA), fluorescence imaging technique, and bioinformatic computations. Binding specificity and binding selectivity of the best candidate phage-displayed p16 binding peptide were evaluated by peptide blocking experiment in competition with p16 monoclonal antibody and fluorescence imaging technique, respectively. Five candidate phage-displayed peptides were isolated from the phage display selection method. All candidate p16 binding phages show better binding affinity than wild-type phage in ELISA test, but only three of them can discriminate p16-overexpressing cancer cell, CaSki, from normal uterine fibroblast cell, HUF, with relative fluorescence intensities from 2.6 to 4.2-fold greater than those of wild-type phage. Bioinformatic results indicate that peptide 'Ser-His-Ser-Leu-Leu-Ser-Ser' binds to p16 molecule with the best binding score and does not interfere with the common protein functions of p16. Peptide blocking experiment shows that the phage-displayed peptide 'Ser-His-Ser-Leu-Leu-Ser-Ser' can conceal p16 from monoclonal antibody interaction. This phage clone also selectively interacts with the p16 positive cell lines, and thus, it can be applied for p16-overexpressing cell detection.

Estimating novel potential drug targets of Plasmodium falciparum by analysing the metabolic network of knock-out strains in silico.

Malaria is one of the world's most common and serious diseases causing death of about 3 million people each year. Its most severe occurrence is caused by the protozoan Plasmodium falciparum. Biomedical research could enable treating the disease by effectively and specifically targeting essential enzymes of this parasite. However, the parasite has developed resistance to existing drugs making it indispensable to discover new drugs. We have established a simple computational tool which analyses the topology of the metabolic network of P. falciparum to identify essential enzymes as possible drug targets. We investigated the essentiality of a reaction in the metabolic network by deleting (knocking-out) such a reaction in silico. The algorithm selected neighbouring compounds of the investigated reaction that had to be produced by alternative biochemical pathways. Using breadth first searches, we tested qualitatively if these products could be generated by reactions that serve as potential deviations of the metabolic flux. With this we identified 70 essential reactions. Our results were compared with a comprehensive list of 38 targets of approved malaria drugs. When combining our approach with an in silico analysis performed recently [Yeh, I., Hanekamp, T., Tsoka, S., Karp, P.D., Altman, R.B., 2004. Computational analysis of Plasmodium falciparum metabolism: organizing genomic information to facilitate drug discovery. Genome Res. 14, 917-924] we could improve the precision of the prediction results. Finally we present a refined list of 22 new potential candidate targets for P. falciparum, half of which have reasonable evidence to be valid targets against micro-organisms and cancer.