An ensemble of bioinformatics and machine learning approaches to identify shared breast cancer biomarkers among diverse populations.

Breast cancer continues to be a prominent cause for substantial loss of life among women globally. Despite established treatment approaches, the rising prevalence of breast cancer is a concerning trend regardless of geographical location. This highlights the need to identify common key genes and explore their biological significance across diverse populations. Our research centered on establishing a correlation between common key genes identified in breast cancer patients. While previous studies have reported many of the genes independently, our study delved into the unexplored realm of their mutual interactions, that may establish a foundational network contributing to breast cancer development. Machine learning algorithms were employed for sample classification and key gene selection. The best performance model further selected the candidate genes through expression pattern recognition. Subsequently, the genes common in all the breast cancer patients from India, China, Czech Republic, Germany, Malaysia and Saudi Arabia were selected for further study. We found that among ten classifiers, Catboost exhibited superior performance with an average accuracy of 92%. Functional enrichment analysis and pathway analysis revealed that calcium signaling pathway, regulation of actin cytoskeleton pathway and other cancer-associated pathways were highly enriched with our identified genes. Notably, we observed that these genes regulate each other, forming a complex network. Additionally, we identified PALMD gene as a novel potential biomarker for breast cancer progression. Our study revealed key gene modules forming a complex network that were consistently expressed in different populations, affirming their critical role and biological significance in breast cancer. The identified genes hold promise as prospective biomarkers of breast cancer prognosis irrespective of country of origin or ethnicity. Future investigations will expand upon these genes in a larger population and validate their biological functions through in vivo analysis.

A Comprehensive Mini-Review on Lignin-Based Nanomaterials for Food Applications: Systemic Advancement and Future Trends.

The shift to an environmentally friendly material economy requires renewable resource exploration. This shift may depend on lignin valorization. Lignin is an aromatic polymer that makes up one-third of total lingo-cellulosic biomass and is separated into large amounts for biofuel and paper manufacture. This renewable polymer is readily available at a very low cost as nearly all the lignin that is produced each year (90-100 million tons) is simply burned as a low-value fuel. Lignin offers potential qualities for many applications, and yet it is underutilized. This Perspective highlights lignin-based material prospects and problems in food packaging, antimicrobial, and agricultural applications. The first half will discuss the present and future studies on exploiting lignin as an addition to improve food packaging's mechanical, gas, UV, bioactive molecules, polyphenols, and antioxidant qualities. Second, lignin's antibacterial activity against bacteria, fungi, and viruses will be discussed. In conclusion, lignin agriculture will be discussed in the food industries.

An effective universal protocol for the extraction of fructooligosaccharide from the different agricultural byproducts.

Alternative bio-refinery technologies are required to promote the commercial utilization of plant biomass components. The fructooligosaccharide (FOS) obtained after hydrolysis of the hemicellulose fractions was mainly applied in the pharmaceutical and food industries. Agricultural bi-product is a rich constituent in dietary fibres, which have prebiotic effects on the intestinal microbiota and the host. Herein we explored the impact of FOS on microbiota modulation and the gut homeostasis effect. High fructooligosaccharide recovery was obtained using alkaline extraction techniques. The enzymatic method produced fructooligosaccharides with minor contamination from fructan and glucan components, although it had a low yield. But combining the alkaline and enzymatic process provides a higher yield ratio and purity of fructooligosaccharides. The structure of the fructooligosaccharide was confirmed, according to FTIR, 13C NMR, 1H NMR and 2D-NMR data. Our results could be applied to the development of efficient extraction of valuable products from agricultural materials using enzyme-mediated methods, which were found to be a cost-effective way to boost bio-refining value. Fructooligosaccharides with varying yields, purity, and structure can be obtained.

Antioxidant activities of novel peptides from Limosilactobacillus reuteri fermented brown rice: A combined in vitro and in silico study.

Oxidative stress is known to cause cell apoptosis, tissue damage, and pathological changes in the body, but antioxidant peptides are renowned radical scavengers. This study investigated the antioxidative and protective effect of six novel peptides obtained after microbial fermentation of brown rice. The selected peptides (MW ≤ 8 KDa), namely AVPYPQ (P1), ILTAV (P2), LGDVIGVP (P3), NPIFDYVLLP (P4), VAPFPEV (P5), and VLPVPK (P6) exhibited strong antioxidant potential against in vitro radicals with IC50 values for DPPH (5.12 ± 0.9-12.54 ± 0.6 µg/ml), ABTS (5.97 ± 0.2-14.20 ± 1.5 µg/ml), FRAP (4.98 ± 2.2-12.19 ± 0.8 µg/ml) and PSC (9.71 ± 0.5-17.84 ± 1.3 µg/ml),respectively. Additionally, these peptides reduced ROS concentrations in Caco-2 cells treated with hydrogen peroxide. In silico studies indicated all six peptides had a higher binding score for the Keap1-Kelch domain than TX6, a potential Keap1 reference ligand. These findings suggest peptides derived from fermented brown rice might be functional components in foods.

Bioinformatics strategies for studying the molecular mechanisms of fungal extracellular vesicles with a focus on infection and immune responses.

Fungal extracellular vesicles (EVs) are released during pathogenesis and are found to be an opportunistic infection in most cases. EVs are immunocompetent with their host and have paved the way for new biomedical approaches to drug delivery and the treatment of complex diseases including cancer. With computing and processing advancements, the rise of bioinformatics tools for the evaluation of various parameters involved in fungal EVs has blossomed. In this review, we have complied and explored the bioinformatics tools to analyze the host-pathogen interaction, toxicity, omics and pathogenesis with an array of specific tools that have depicted the ability of EVs as vector/carrier for therapeutic agents and as a potential theme for immunotherapy. We have also discussed the generation and pathways involved in the production, transport, pathogenic action and immunological interactions of EVs in the host system. The incorporation of network pharmacology approaches has been discussed regarding fungal pathogens and their significance in drug discovery. To represent the overview, we have presented and demonstrated an in silico study model to portray the human Cryptococcal interactions.

A review on the application of bioinformatics tools in food microbiome studies.

There is currently a transformed interest toward understanding the impact of fermentation on functional food development due to growing consumer interest on modified health benefits of sustainable foods. In this review, we attempt to summarize recent findings regarding the impact of Next-generation sequencing and other bioinformatics methods in the food microbiome and use prediction software to understand the critical role of microbes in producing fermented foods. Traditionally, fermentation methods and starter culture development were considered conventional methods needing optimization to eliminate errors in technique and were influenced by technical knowledge of fermentation. Recent advances in high-output omics innovations permit the implementation of additional logical tactics for developing fermentation methods. Further, the review describes the multiple functions of the predictions based on docking studies and the correlation of genomic and metabolomic analysis to develop trends to understand the potential food microbiome interactions and associated products to become a part of a healthy diet.

The Role of Bioactive Peptides in Diabetes and Obesity.

Bioactive peptides are present in most soy products and eggs and have essential protective functions. Infection is a core feature of innate immunity that affects blood pressure and the glucose level, and ageing can be delayed by killing senescent cells. Food also encrypts bioactive peptides and protein sequences produced through proteolysis or food processing. Unique food protein fragments can improve human health and avoid metabolic diseases, inflammation, hypertension, obesity, and diabetes mellitus. This review focuses on drug targets and fundamental mechanisms of bioactive peptides on metabolic syndromes, namely obesity and type 2 diabetes, to provide new ideas and knowledge on the ability of bioactive peptide to control metabolic syndromes.

Identification of marker genes in Alzheimer's disease using a machine-learning model.

Alzheimer's Disease (AD) is one of the most common causes of dementia, mostly affecting the elderly population. Currently, there is no proper diagnostic tool or method available for the detection of AD. The present study used two distinct data sets of AD genes, which could be potential biomarkers in the diagnosis. The differentially expressed genes (DEGs) curated from both datasets were used for machine learning classification, tissue expression annotation and co-expression analysis. Further, CNPY3, GPR84, HIST1H2AB, HIST1H2AE, IFNAR1, LMO3, MYO18A, N4BP2L1, PML, SLC4A4, ST8SIA4, TLE1 and N4BP2L1 were identified as highly significant DEGs and exhibited co-expression with other query genes. Moreover, a tissue expression study found that these genes are also expressed in the brain tissue. In addition to the earlier studies for marker gene identification, we have considered a different set of machine learning classifiers to improve the accuracy rate from the analysis. Amongst all the six classification algorithms, J48 emerged as the best classifier, which could be used for differentiating healthy and diseased samples. SMO/SVM and Logit Boost further followed J48 to achieve the classification accuracy.

In Vitro and In Silico Screening and Characterization of Antimicrobial Napin Bioactive Protein in Brassica juncea and Moringa oleifera.

The study aimed to investigate the antibacterial activity of Mustard (Brassica juncea) and Moringa (Moringa oleifera) leaf extracts and coagulant protein for their potential application in water treatment. Bacterial cell aggregation and growth kinetics studies were employed for thirteen bacterial strains with different concentrations of leaf extracts and coagulant protein. Moringa oleifera leaf extract (MOS) and coagulant protein showed cell aggregation against ten bacterial strains, whereas leaf extract alone showed growth inhibition of five bacterial strains for up to 6 h and five bacterial strains for up to 3 h. Brassica juncea leaf extract (BJS) showed growth inhibition for up to 6 h, and three bacterial strains showed inhibition for up to 3 h. The highest inhibition concentration with 2.5 mg/mL was 19 mm, and furthermore, the minimum inhibitory concentration (MIC) (0.5 mg/mL) and MBC (1.5 mg/mL) were determined to have a higher antibacterial effect for <3 KDa peptides. Based on LCMS analysis, napin was identified in both MOS and BJS; furthermore, the mode of action of napin peptide was determined on lipoprotein X complex (LpxC) and four-chained structured binding protein of bacterial type II topoisomerase (4PLB). The docking analysis has exhibited moderate to potent inhibition with a range of dock score -912.9 Kcal/mol. Thus, it possesses antibacterial-coagulant potential bioactive peptides present in the Moringa oleifera purified protein (MOP) and Brassica juncea purified protein (BJP) that could act as an effective antimicrobial agent to replace currently available antibiotics. The result implies that MOP and Brassica juncea purified coagulant (BJP) proteins may perform a wide degree of antibacterial functions against different pathogens.

Towards the early detection of ductal carcinoma (a common type of breast cancer) using biomarkers linked to the PPAR(γ) signaling pathway.

Breast cancer is a leading cause of morbidity and mortality among women comprising about 12% females worldwide. The underlying alteration in the gene expression, molecular mechanism and metabolic pathways responsible for incidence and progression of breast tumorigenesis are yet not completely understood. In the present study, potential biomarker genes involved in the early progression for early diagnosis of breast cancer has been detailed. Regulation and Gene profiling of Ductal Carcinoma In-situ (DCIS), Invasive Ductal Carcinoma (IDC) and healthy samples have been analyzed to follow their expression pattern employing normalization, statistical calculation, DEGs annotation and Protein-Protein Interaction (PPI) network. We have performed a comparative study on differentially expressed genes among Healthy vs DCIS, Healthy vsIDC and DCIS vs IDC. We found MCM102 and SLC12A8as consistently over-expressed and LEP, SORBS1, SFRP1, PLIN1, FABP4, RBP4, CD300LG, ID4, CRYAB, ECRG4, G0S2, FMO2, ADAMTS5, CAV1, CAV2, ABCA8, MAMDC2, IGFBP6, CLDN11, TGFBR3as under-expressed genes in all the 3 conditions categorized for pre-invasive and invasive ductal breast carcinoma. These genes were further studied for the active pathways where PPAR(γ) signaling pathway was found to be significantly involved. The gene expression profile database can be a potential tool in the early diagnosis of breast cancer.