Nutritional profiling and in silico analysis of pharmacological activities from local rice Pulu Mandoti fermented with Pleurotus spp.

Pulu Mandoti, a local red rice (Oryza sativa L.) variety popular among Sulawesi residents, has gained recognition for its perceived health benefits, especially as a preferred dietary option for individuals with diabetes or those seeking to prevent obesity. Given the increasing consumption of mushrooms, particularly Pleurotus species, renowned for their nutritional and medicinal attributes, this study delves into the transformative effects of Pleurotus spp. fermentation on Pulu Mandoti, the indigenous rice variety. Proximate analysis disclosed elevated dry matter (91.99 ± 0.61%), crude protein (8.55 ± 0.15%), and crude fat (1.34 ± 0.05%) in Pleurotus cystidiosus fermentation compared to Pleurotus ostreatus and Pleurotus djamor. Concurrently, antioxidant and antidiabetic activities were notably improved in all Pleurotus fermentations. Pulu Mandoti fermented with P. cystidiosus outperformed other treatments, aligning with molecular docking results pinpointing 11-Eicosenoic acid, methyl ester, and butylated hydroxytoluene as optimal interactors with antioxidant receptors 5O0x and 2CKJ. Butylated hydroxytoluene demonstrated interactions with the antidiabetic receptor 2QV4, along with 9-Octadecenoic acid, methyl ester. These compounds, previously unreported in Pleurotus, displayed promising attributes as antioxidants and antidiabetic agents. Furthermore, the investigation delved into the fatty acid profiles, emphasizing the diverse range of potential bioactive compounds in fermented Pulu Mandoti. The findings of this research present a potential functional food rich in natural antioxidants and antidiabetic compounds, highlighting the yet undiscovered capabilities of Pleurotus spp. fermentation in augmenting the nutritional composition and bioactivity of indigenous rice varieties, specifically Pulu Mandoti.

Investigations of Ni(II)Cysteine-Tyrosine Dithiocarbamate Complex: Synthesis, Characterization, Molecular Docking, Molecular Dynamic, and Anticancer Activity on MCF-7 Breast Cancer Cell Line.

OBJECTIVE: Breast cancer ranks second in terms of the highest number of cancer deaths for women worldwide and is one of the leading causes of death from cancer in women. The drug that is often used for chemotherapy is cisplatin. However, cisplatin drugs have a number of problems, including lack of selectivity, unwanted side effects, resistance, and toxicity in the body. In this work, we investigated Ni(II) cysteine-tyrosine dithiocarbamate complex against breast cancer. METHODS: Research on the new complex compound Ni(II) cysteine-tyrosine dithiocarbamate have several stages including synthesis, characterization, in-silico and in-vitro testing of MCF-7 cells for anticancer drugs. The synthesis involved reacting cysteine, CS2, KOH and tyrosine with Mn metal. The new complex compound Ni(II) cysteine-tyrosine dithiocarbamate has been synthesized, characterized, and tested in vitro MCF-7 cells for anticancer drugs. Characterization tests such as melting point, conductivity, SEM-EDS, UV Vis, XRD, and FT-IR spectroscopy have been carried out. RESULT: The synthesis yielded a 60,16%, conversion with a melting point of 216-218 oC and a conductivity value of 0.4 mS/cm. In vitro test results showed morphological changes (apoptosis) in MCF-7 cancer cells starting at a sample concentration of 250 µg/mL and an IC50 value of 618.40 µg/mL. Molecular docking study of Ni(II) cysteine-tyrosine dithiocarbamate complex identified with 4,4',4''-[(2R)-butane-1,1,2-triyl]triphenol - Estrogen α showing active site with acidic residue amino E323, M388, L387, G390 and I389. Hydrophobic and hydrophobic bonds are seen in Ni(II) cysteine-tyrosine dithiocarbamate - Estrogen α has a binding energy of -80.9429 kJ /mol. CONCLUSION: there were 5 residues responsible for maintaining the ligand binding stable. The compound had significant Hbond contact intensity, however, it was not strong enough to make a significant anticancer effect. Though the synthesized compound shows low bioactivity, this research is expected to give valuable insight into the effect of molecular structure on anticancer activity.

Skin Microbial Composition and Genetic Mutation Analysis in Precision Medicine for Epidermolysis Bullosa.

Epidermolysis bullosa (EB) is an inherited skin disease representing a spectrum of rare genetic disorders. These conditions share the common trait that causes fragile skin, resulting in the development of blisters and erosions. The inheritance follows an autosomal pattern, and the array of clinical presentations leads to significant physical suffering, considerable morbidity, and mortality. Despite EB having no cure, effectively managing EB remains an exceptional challenge due to its rarity and complexity, occasionally casting a profound impact on the lives of affected individuals. Considering that EB management requires a multidisciplinary approach, this sometimes worsens the condition of patients with EB due to inappropriate handling. Thus, more appropriate and precise treatment management of EB is essentially needed. Advanced technology in medicine and health comes into the bioinformatics era. Including treatment for skin diseases, omics-based approaches aim to evaluate and handle better disease management and treatment. In this work, we review several approaches regarding the implementation of omics-based technology, including genetics, pathogenic mutation, skin microbiomics, and metagenomics analysis for EB. In addition, we highlight recent updates on the potential of metagenomics analysis in precision medicine for EB.

Novel Complex of Zinc (II) Dichloroethylenediamine: Synthesis, Characterization, In-silico, and In-vitro Evaluation against Cervical Cancer Cells.

OBJECTIVE: Cervical cancer is a malignancy originating from the cervix and often caused by oncogenic Human Papilloma Virus (HPV), specifically subtypes 16 and 18. Anticancer drugs are chemotherapeutic compounds used for cancer treatment. Therefore, this research aims to synthesize and characterize Zinc (II) dichloroethylenediamine (Zn(en)Cl2) complex, as well as determine its antiproliferative activity against HeLa cells. The Zn(en)Cl2 complex was successfully synthesized, and the antiproliferative activity was tested. METHODS: The synthesis involved reacting ethylenediamine and KCl with Zn metal. The complex formed was characterized using a conductometer, UV-Vis spectroscopy, FT-IR spectroscopy, and XRD, while the activity was measured against HeLa cells. RESULT: The synthesis yielded a 56.12% conversion with a melting point of 198-200 oC and a conductivity value of 2.02 mS/cm. The Zn(en)Cl2 complex showed potential activity against HeLa cells with an IC50 value of 898.35 µg/mL, which was evidenced by changes in the morphological structure of HeLa cells. Its interaction with DNA targets was investigated by employing molecular docking. CONCLUSION: The observed data indicated that the Zn(en)Cl2 complex bound to DNA at the nitrogenous base Guanine (DG) by coordinate covalent bonds. Interestingly, DG maintained interaction with the complex until the end of the docking simulation. Additionally, molecular dynamics (MD) simulation was conducted, and the results showed that Zn(en)Cl2 remained bound to the DNA binding pocket all through the process.

A genomic and bioinformatic-based approach to identify genetic variants for liver cancer across multiple continents.

Liver cancer is the fourth leading cause of death worldwide. Well-known risk factors include hepatitis B virus and hepatitis C virus, along with exposure to aflatoxins, excessive alcohol consumption, obesity, and type 2 diabetes. Genomic variants play a crucial role in mediating the associations between these risk factors and liver cancer. However, the specific variants involved in this process remain under-explored. This study utilized a bioinformatics approach to identify genetic variants associated with liver cancer from various continents. Single-nucleotide polymorphisms associated with liver cancer were retrieved from the genome-wide association studies catalog. Prioritization was then performed using functional annotation with HaploReg v4.1 and the Ensembl database. The prevalence and allele frequencies of each variant were evaluated using Pearson correlation coefficients. Two variants, rs2294915 and rs2896019, encoded by the PNPLA3 gene, were found to be highly expressed in the liver tissue, as well as in the skin, cell-cultured fibroblasts, and adipose-subcutaneous tissue, all of which contribute to the risk of liver cancer. We further found that these two SNPs (rs2294915 and rs2896019) were positively correlated with the prevalence rate. Positive associations with the prevalence rate were more frequent in East Asian and African populations. We highlight the utility of this population-specific PNPLA3 genetic variant for genetic association studies and for the early prognosis and treatment of liver cancer. This study highlights the potential of integrating genomic databases with bioinformatic analysis to identify genetic variations involved in the pathogenesis of liver cancer. The genetic variants investigated in this study are likely to predispose to liver cancer and could affect its progression and aggressiveness. We recommend future research prioritizing the validation of these variations in clinical settings.

In Silico Pleiotropy Analysis in KEGG Signaling Networks Using a Boolean Network Model.

Pleiotropy, which refers to the ability of different mutations on the same gene to cause different pathological effects in human genetic diseases, is important in understanding system-level biological diseases. Although some biological experiments have been proposed, still little is known about pleiotropy on gene-gene dynamics, since most previous studies have been based on correlation analysis. Therefore, a new perspective is needed to investigate pleiotropy in terms of gene-gene dynamical characteristics. To quantify pleiotropy in terms of network dynamics, we propose a measure called in silico Pleiotropic Scores (sPS), which represents how much a gene is affected against a pair of different types of mutations on a Boolean network model. We found that our model can identify more candidate pleiotropic genes that are not known to be pleiotropic than the experimental database. In addition, we found that many types of functionally important genes tend to have higher sPS values than other genes; in other words, they are more pleiotropic. We investigated the relations of sPS with the structural properties in the signaling network and found that there are highly positive relations to degree, feedback loops, and centrality measures. This implies that the structural characteristics are principles to identify new pleiotropic genes. Finally, we found some biological evidence showing that sPS analysis is relevant to the real pleiotropic data and can be considered a novel candidate for pleiotropic gene research. Taken together, our results can be used to understand the dynamics pleiotropic characteristics in complex biological systems in terms of gene-phenotype relations.

Effects of ordered mutations on dynamics in signaling networks.

BACKGROUND: Many previous clinical studies have found that accumulated sequential mutations are statistically related to tumorigenesis. However, they are limited in fully elucidating the significance of the ordered-mutation because they did not focus on the network dynamics. Therefore, there is a pressing need to investigate the dynamics characteristics induced by ordered-mutations. METHODS: To quantify the ordered-mutation-inducing dynamics, we defined the mutation-sensitivity and the order-specificity that represent if the network is sensitive against a double knockout mutation and if mutation-sensitivity is specific to the mutation order, respectively, using a Boolean network model. RESULTS: Through intensive investigations, we found that a signaling network is more sensitive when a double-mutation occurs in the direction order inducing a longer path and a smaller number of paths than in the reverse order. In addition, feedback loops involving a gene pair decreased both the mutation-sensitivity and the order-specificity. Next, we investigated relationships of functionally important genes with ordered-mutation-inducing dynamics. The network is more sensitive to mutations subject to drug-targets, whereas it is less specific to the mutation order. Both the sensitivity and specificity are increased when different-drug-targeted genes are mutated. Further, we found that tumor suppressors can efficiently suppress the amplification of oncogenes when the former are mutated earlier than the latter. CONCLUSION: Taken together, our results help to understand the importance of the order of mutations with respect to the dynamical effects in complex biological systems.

Construction and analysis of gene-gene dynamics influence networks based on a Boolean model.

BACKGROUND: Identification of novel gene-gene relations is a crucial issue to understand system-level biological phenomena. To this end, many methods based on a correlation analysis of gene expressions or structural analysis of molecular interaction networks have been proposed. They have a limitation in identifying more complicated gene-gene dynamical relations, though. RESULTS: To overcome this limitation, we proposed a measure to quantify a gene-gene dynamical influence (GDI) using a Boolean network model and constructed a GDI network to indicate existence of a dynamical influence for every ordered pair of genes. It represents how much a state trajectory of a target gene is changed by a knockout mutation subject to a source gene in a gene-gene molecular interaction (GMI) network. Through a topological comparison between GDI and GMI networks, we observed that the former network is denser than the latter network, which implies that there exist many gene pairs of dynamically influencing but molecularly non-interacting relations. In addition, a larger number of hub genes were generated in the GDI network. On the other hand, there was a correlation between these networks such that the degree value of a node was positively correlated to each other. We further investigated the relationships of the GDI value with structural properties and found that there are negative and positive correlations with the length of a shortest path and the number of paths, respectively. In addition, a GDI network could predict a set of genes whose steady-state expression is affected in E. coli gene-knockout experiments. More interestingly, we found that the drug-targets with side-effects have a larger number of outgoing links than the other genes in the GDI network, which implies that they are more likely to influence the dynamics of other genes. Finally, we found biological evidences showing that the gene pairs which are not molecularly interacting but dynamically influential can be considered for novel gene-gene relationships. CONCLUSION: Taken together, construction and analysis of the GDI network can be a useful approach to identify novel gene-gene relationships in terms of the dynamical influence.