Solving genomic puzzles: computational methods for metagenomic binning.

Metagenomics involves the study of genetic material obtained directly from communities of microorganisms living in natural environments. The field of metagenomics has provided valuable insights into the structure, diversity and ecology of microbial communities. Once an environmental sample is sequenced and processed, metagenomic binning clusters the sequences into bins representing different taxonomic groups such as species, genera, or higher levels. Several computational tools have been developed to automate the process of metagenomic binning. These tools have enabled the recovery of novel draft genomes of microorganisms allowing us to study their behaviors and functions within microbial communities. This review classifies and analyzes different approaches of metagenomic binning and different refinement, visualization, and evaluation techniques used by these methods. Furthermore, the review highlights the current challenges and areas of improvement present within the field of research.

Future-proofing genomic data and consent management: a comprehensive review of technology innovations.

Genomic information is increasingly used to inform medical treatments and manage future disease risks. However, any personal and societal gains must be carefully balanced against the risk to individuals contributing their genomic data. Expanding our understanding of actionable genomic insights requires researchers to access large global datasets to capture the complexity of genomic contribution to diseases. Similarly, clinicians need efficient access to a patient's genome as well as population-representative historical records for evidence-based decisions. Both researchers and clinicians hence rely on participants to consent to the use of their genomic data, which in turn requires trust in the professional and ethical handling of this information. Here, we review existing and emerging solutions for secure and effective genomic information management, including storage, encryption, consent, and authorization that are needed to build participant trust. We discuss recent innovations in cloud computing, quantum-computing-proof encryption, and self-sovereign identity. These innovations can augment key developments from within the genomics community, notably GA4GH Passports and the Crypt4GH file container standard. We also explore how decentralized storage as well as the digital consenting process can offer culturally acceptable processes to encourage data contributions from ethnic minorities. We conclude that the individual and their right for self-determination needs to be put at the center of any genomics framework, because only on an individual level can the received benefits be accurately balanced against the risk of exposing private information.

SNP-based heritability and selection analyses: Improved models and new results.

Complex-trait genetics has advanced dramatically through methods to estimate the heritability tagged by SNPs, both genome-wide and in genomic regions of interest such as those defined by functional annotations. The models underlying many of these analyses are inadequate, and consequently many SNP-heritability results published to date are inaccurate. Here, we review the modelling issues, both for analyses based on individual genotype data and association test statistics, highlighting the role of a low-dimensional model for the heritability of each SNP. We use state-of-art models to present updated results about how heritability is distributed with respect to functional annotations in the human genome, and how it varies with allele frequency, which can reflect purifying selection. Our results give finer detail to the picture that has emerged in recent years of complex trait heritability widely dispersed across the genome. Confounding due to population structure remains a problem that summary statistic analyses cannot reliably overcome. Also see the video abstract here: https://youtu.be/WC2u03V65MQ.

Tejaas: reverse regression increases power for detecting trans-eQTLs.

Trans-acting expression quantitative trait loci (trans-eQTLs) account for ≥70% expression heritability and could therefore facilitate uncovering mechanisms underlying the origination of complex diseases. Identifying trans-eQTLs is challenging because of small effect sizes, tissue specificity, and a severe multiple-testing burden. Tejaas predicts trans-eQTLs by performing L2-regularized "reverse" multiple regression of each SNP on all genes, aggregating evidence from many small trans-effects while being unaffected by the strong expression correlations. Combined with a novel unsupervised k-nearest neighbor method to remove confounders, Tejaas predicts 18851 unique trans-eQTLs across 49 tissues from GTEx. They are enriched in open chromatin, enhancers, and other regulatory regions. Many overlap with disease-associated SNPs, pointing to tissue-specific transcriptional regulation mechanisms.

Current trends and challenges in cancer management and therapy using designer nanomaterials.

Nanotechnology has the potential to circumvent several drawbacks of conventional therapeutic formulations. In fact, significant strides have been made towards the application of engineered nanomaterials for the treatment of cancer with high specificity, sensitivity and efficacy. Tailor-made nanomaterials functionalized with specific ligands can target cancer cells in a predictable manner and deliver encapsulated payloads effectively. Moreover, nanomaterials can also be designed for increased drug loading, improved half-life in the body, controlled release, and selective distribution by modifying their composition, size, morphology, and surface chemistry. To date, polymeric nanomaterials, metallic nanoparticles, carbon-based materials, liposomes, and dendrimers have been developed as smart drug delivery systems for cancer treatment, demonstrating enhanced pharmacokinetic and pharmacodynamic profiles over conventional formulations due to their nanoscale size and unique physicochemical characteristics. The data present in the literature suggest that nanotechnology will provide next-generation platforms for cancer management and anticancer therapy. Therefore, in this critical review, we summarize a range of nanomaterials which are currently being employed for anticancer therapies and discuss the fundamental role of their physicochemical properties in cancer management. We further elaborate on the topical progress made to date toward nanomaterial engineering for cancer therapy, including current strategies for drug targeting and release for efficient cancer administration. We also discuss issues of nanotoxicity, which is an often-neglected feature of nanotechnology. Finally, we attempt to summarize the current challenges in nanotherapeutics and provide an outlook on the future of this important field.

Evaluating Andrographolide as a Potent Inhibitor of NS3-4A Protease and Its Drug-Resistant Mutants Using In Silico Approaches.

Current combination therapy of PEG-INF and ribavirin against the Hepatitis C Virus (HCV) genotype-1 infections is ineffective in maintaining sustained viral response in 50% of the infection cases. New compounds in the form of protease inhibitors can complement the combination therapy. Asunaprevir is new to the drug regiment as the NS3-4A protease inhibitor, but it is susceptible to two mutations, namely, R155K and D168A in the protein. Thus, in our study, we sought to evaluate Andrographolide, a labdane-diterpenoid from the Andrographis paniculata plant as an effective compound for inhibiting the NS3-4A protease as well as its concomitant drug-resistant mutants by using molecular docking and dynamic simulations. Our study shows that Andrographolide has best docking scores of -15.0862, -15.2322, and -13.9072 compared to those of Asunaprevir -3.7159, -2.6431, and -5.4149 with wild-type R155K and D168A mutants, respectively. Also, as shown in the MD simulations, the compound was good in binding the target proteins and maintains strong bonds causing very less to negligible perturbation in the protein backbone structures. Our results validate the susceptibility of Asunaprevir to protein variants as seen from our docking studies and trajectory period analysis. Therefore, from our study, we hope to add one more option in the drug regiment to tackle drug resistance in HCV infections.

Taxifolin curbs NF-κB-mediated Wnt/ß-catenin signaling via up-regulating Nrf2 pathway in experimental colon carcinogenesis.

Aberrations in homeostasis mechanisms including Nrf2, inflammatory, and Wnt/ß-catenin signaling are the major causative factors implicated in colon cancer development. Hence blocking these pathways through natural interventions pave a new channel for colon cancer prevention. Earlier, we reported the chemopreventive effect of taxifolin (TAX) against colon carcinogenesis. In this study, we aimed to understand the ability of TAX, to modulate the Nrf2, inflammatory and Wnt/ß-catenin cascades on 1, 2-dimethyl hydrazine (DMH)-induced mouse colon carcinogenesis. In addition, in silico molecular docking studies were performed to evaluate the binding affinity between TAX and target proteins (Nrf2, ß-catenin, and TNF-α). We perceived that the increase of serum marker enzyme levels (CEA and LDH) and mast cell infiltration that occurs in the presence of DMH is inverted after TAX treatment. Immunoblot expression and docking analysis revealed that TAX could induce antioxidant response pathway, confirming the enhanced level of Nrf2 protein. It also inhibited NF-κB and Wnt signaling by down-regulating the levels of regulatory metabolites such as TNF-α, COX-2, ß-catenin, and Cyclin-D1. Collectively, results of our hypothesis shown that TAX is an effective chemopreventive agent capable of modulating inflammatory, Wnt and antioxidant response pathway proteins in tumor microenvironment which explicating its anticancer property.

Identification of Deleterious SNPs and Their Effects on Structural Level in CHRNA3 Gene.

The aim of our study is to identify probable deleterious genetic variations that can alter the expression and the function of the CHRNA3 gene using in silico methods. Of the 2305 SNPs identified in the CHRNA3 gene, 115 were found to be non-synonymous and 12 and 15 nsSNPs were found to be in the 5' and 3' UTRs, respectively. Further, out of the 115 nsSNPs investigated, eight were predicted to be deleterious by both SIFT and PredictSNP servers. The major mutations predicted to affect the structure of the protein are phenylalanine to valine (Y43V) and lysine to asparagine (K216N) as shown by the trajectory run in molecular dynamics studies. The random transition of the protein structures over the simulation period caused by these mutations hints at how the native state is distorted which could lead to the loss of structural stability and functionality of the nicotinic acetylcholine receptors subunit α-3 protein. Based on this work, we propose that the nsSNP with SNP id of rs75495285 and rs76821682 will have comparatively more deleterious effects than the other predicted mutations in destabilizing the protein structure.