Explainable AI for Bioinformatics: Methods, Tools and Applications.

Artificial intelligence (AI) systems utilizing deep neural networks and machine learning (ML) algorithms are widely used for solving critical problems in bioinformatics, biomedical informatics and precision medicine. However, complex ML models that are often perceived as opaque and black-box methods make it difficult to understand the reasoning behind their decisions. This lack of transparency can be a challenge for both end-users and decision-makers, as well as AI developers. In sensitive areas such as healthcare, explainability and accountability are not only desirable properties but also legally required for AI systems that can have a significant impact on human lives. Fairness is another growing concern, as algorithmic decisions should not show bias or discrimination towards certain groups or individuals based on sensitive attributes. Explainable AI (XAI) aims to overcome the opaqueness of black-box models and to provide transparency in how AI systems make decisions. Interpretable ML models can explain how they make predictions and identify factors that influence their outcomes. However, the majority of the state-of-the-art interpretable ML methods are domain-agnostic and have evolved from fields such as computer vision, automated reasoning or statistics, making direct application to bioinformatics problems challenging without customization and domain adaptation. In this paper, we discuss the importance of explainability and algorithmic transparency in the context of bioinformatics. We provide an overview of model-specific and model-agnostic interpretable ML methods and tools and outline their potential limitations. We discuss how existing interpretable ML methods can be customized and fit to bioinformatics research problems. Further, through case studies in bioimaging, cancer genomics and text mining, we demonstrate how XAI methods can improve transparency and decision fairness. Our review aims at providing valuable insights and serving as a starting point for researchers wanting to enhance explainability and decision transparency while solving bioinformatics problems. GitHub: https://github.com/rezacsedu/XAI-for-bioinformatics.

Bioconversion, Pharmacokinetics, and Therapeutic Mechanisms of Ginsenoside Compound K and Its Analogues for Treating Metabolic Diseases.

Rare ginsenoside compound K (CK) is an intestinal microbial metabolite with a low natural abundance that is primarily produced by physicochemical processing, side chain modification, or metabolic transformation in the gut. Moreover, CK exhibits potent biological activity compared to primary ginsenosides, which has raised concerns in the field of ginseng research and development, as well as ginsenoside-related dietary supplements and natural products. Ginsenosides Rb1, Rb2, and Rc are generally used as a substrate to generate CK via several bioconversion processes. Current research shows that CK has a wide range of pharmacological actions, including boosting osteogenesis, lipid and glucose metabolism, lipid oxidation, insulin resistance, and anti-inflammatory and anti-apoptosis properties. Further research on the bioavailability and toxicology of CK can advance its medicinal application. The purpose of this review is to lay the groundwork for future clinical studies and the development of CK as a therapy for metabolic disorders. Furthermore, the toxicology and pharmacology of CK are investigated as well in this review. The findings indicate that CK primarily modulates signaling pathways associated with AMPK, SIRT1, PPARs, WNTs, and NF-kB. It also demonstrates a positive therapeutic effect of CK on non-alcoholic fatty liver disease (NAFLD), obesity, hyperlipidemia, diabetes, and its complications, as well as osteoporosis. Additionally, the analogues of CK showed more bioavailability, less toxicity, and more efficacy against disease states. Enhancing bioavailability and regulating hazardous variables are crucial for its use in clinical trials.

Multiomics Analysis of the PHLDA Gene Family in Different Cancers and Their Clinical Prognostic Value.

The PHLDA (pleckstrin homology-like domain family) gene family is popularly known as a potential biomarker for cancer identification, and members of the PHLDA family have become considered potentially viable targets for cancer treatments. The PHLDA gene family consists of PHLDA1, PHLDA2, and PHLDA3. The predictive significance of PHLDA genes in cancer remains unclear. To determine the role of pleckstrin as a prognostic biomarker in human cancers, we conducted a systematic multiomics investigation. Through various survival analyses, pleckstrin expression was evaluated, and their predictive significance in human tumors was discovered using a variety of online platforms. By analyzing the protein-protein interactions, we also chose a collection of well-known functional protein partners for pleckstrin. Investigations were also carried out on the relationship between pleckstrins and other cancers regarding mutations and copy number alterations. The cumulative impact of pleckstrin and their associated genes on various cancers, Gene Ontology (GO), and pathway analyses were used for their evaluation. Thus, the expression profiles of PHLDA family members and their prognosis in various cancers may be revealed by this study. During this multiomics analysis, we found that among the PHLDA family, PHLDA1 may be a therapeutic target for several cancers, including kidney, colon, and brain cancer, while PHLDA2 can be a therapeutic target for cancers of the colon, esophagus, and pancreas. Additionally, PHLDA3 may be a useful therapeutic target for ovarian, renal, and gastric cancer.

In Silico and In Vitro Study of Isoquercitrin against Kidney Cancer and Inflammation by Triggering Potential Gene Targets.

Kidney cancer has emerged as a major medical problem in recent times. Multiple compounds are used to treat kidney cancer by triggering cancer-causing gene targets. For instance, isoquercitrin (quercetin-3-O-ß-d-glucopyranoside) is frequently present in fruits, vegetables, medicinal herbs, and foods and drinks made from plants. Our previous study predicted using protein-protein interaction (PPI) and molecular docking analysis that the isoquercitrin compound can control kidney cancer and inflammation by triggering potential gene targets of IGF1R, PIK3CA, IL6, and PTGS2. So, the present study is about further in silico and in vitro validation. We performed molecular dynamic (MD) simulation, gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, cytotoxicity assay, and RT-PCR and qRT-PCR validation. According to the MD simulation (250 ns), we found that IGF1R, PIK3CA, and PTGS2, except for IL6 gene targets, show stable binding energy with a stable complex with isoquercitrin. We also performed gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of the final targets to determine their regulatory functions and signaling pathways. Furthermore, we checked the cytotoxicity effect of isoquercitrin (IQ) and found that 5 µg/mL and 10 µg/mL doses showed higher cell viability in a normal kidney cell line (HEK 293) and also inversely showed an inhibition of cell growth at 35% and 45%, respectively, in the kidney cancer cell line (A498). Lastly, the RT-PCR and qRT-PCR findings showed a significant decrease in PTGS2, PIK3CA, and IGF1R gene expression, except for IL6 expression, following dose-dependent treatments with IQ. Thus, we can conclude that isoquercitrin inhibits the expression of PTGS2, PIK3CA, and IGF1R gene targets, which in turn controls kidney cancer and inflammation.

Butyrate's (a short-chain fatty acid) microbial synthesis, absorption, and preventive roles against colorectal and lung cancer.

Butyrate, a short-chain fatty acid (SCFA) produced by bacterial fermentation of fiber in the colon, is a source of energy for colonocytes. Butyrate is essential for improving gastrointestinal (GI) health since it helps colonocyte function, reduces inflammation, preserves the gut barrier, and fosters a balanced microbiome. Human colonic butyrate producers are Gram-positive firmicutes, which are phylogenetically varied. The two most prevalent subgroups are associated with Eubacterium rectale/Roseburia spp. and Faecalibacterium prausnitzii. Now, the mechanism for the production of butyrate from microbes is a very vital topic to know. In the present study, we discuss the genes encoding the core of the butyrate synthesis pathway and also discuss the butyryl-CoA:acetate CoA-transferase, instead of butyrate kinase, which usually appears to be the enzyme that completes the process. Recently, butyrate-producing microbes have been genetically modified by researchers to increase butyrate synthesis from microbes. The activity of butyrate as a histone deacetylase inhibitor (HDACi) has led to several clinical trials to assess its effectiveness as a potential cancer treatment. Among various significant roles, butyrate is the main energy source for intestinal epithelial cells, which helps maintain colonic homeostasis. Moreover, people with non-small-cell lung cancer (NSCLC) have distinct gut microbiota from healthy adults and frequently have dysbiosis of the butyrate-producing bacteria in their guts. So, with an emphasis on colon and lung cancer, this review also discusses how the microbiome is crucial in preventing the progression of certain cancers through butyrate production. Further studies should be performed to investigate the underlying mechanisms of how these specific butyrate-producing bacteria can control both colon and lung cancer progression and prognosis.

Potential of Gut Microbial Metabolites in Treating Osteoporosis and Obesity: A Network Pharmacology and Bioinformatics Approach.

BACKGROUND The gut microbial metabolites demonstrate significant activity against metabolic diseases including osteoporosis (OP) and obesity, but active compounds, targets, and mechanisms have not been fully identified. Hence, the current investigation explored the mechanisms of active metabolites and targets against OP and obesity by using network pharmacology approaches. MATERIAL AND METHODS The gutMGene database was used to collect gut microbial targets-associated metabolites; DisGeNET and OMIM databases were used to identify targets relevant to OP and obesity. A total of 63 and 89 overlapped targets were considered the final OP and obesity targets after creating a Venn diagram of metabolites-related targets and disease-related targets. Furthermore, the top 20% of degrees, betweenness, and closeness were used to form the sub-network of protein-protein interaction of these targets. Finally, the biotransformation-increased receptors and biological mechanisms were identified and validated using ADMET properties analysis, molecular docking, and molecular dynamic simulation. RESULTS GO, KEGG pathway analysis, and protein-protein interactions were performed to establish metabolites and target networks. According to the enrichment analysis, OP and obesity are highly linked to the lipid and atherosclerosis pathways. Moreover, ADMET analysis depicts that the major metabolites have drug-likeliness activity and no or less toxicity. Following that, the molecular docking studies showed that compound K and TP53 target have a remarkable negative affinity (-8.0 kcal/mol) among all metabolites and targets for both diseases. Finally, the conformity of compound K against the targeted protein TP53 was validated by 250ns MD simulation. CONCLUSIONS Therefore, we summarized that compound K can regulate TP53 and could be developed as a therapy option for OP and obesity.

Plant-assisted green preparation of silver nanoparticles using leaf extract of Dalbergia sissoo and their antioxidant, antibacterial and catalytic applications.

Plant-mediated preparation of silver nanoparticles (AgNPs) is thought to be a more economical and environmentally benign process in comparison to physical and chemical synthesis methods. In the present study, the aqueous leaf extract of Dalbergia sissoo was prepared and utilized to reduce silver ion (Ag+) during the green synthesis of silver nanoparticles (DL-AgNPs). The formation of DL-AgNPs was verified using UV-Vis spectra, exhibiting the surface plasmon resonance (SPR) band at around 450 nm. FT-IR analysis revealed the kinds of phytochemicals that serve as reducing and capping agents while DL-AgNPs are being synthesized. Analysis of scanning electron microscope (SEM) and high-resolution transmission electron microscopy (HR-TEM) images verified the development of spherical and oval-shaped DL-AgNPs, with sizes ranging from 10 to 25 nm. The stability and particle size distribution of synthesized DL-AgNPs were ensured by zeta potential and DLS (dynamic light scattering) investigations. Additionally, X-ray diffraction (XRD) analysis confirmed the crystalline nature of DL-AgNPs. In antioxidant experiments, DL-AgNPs demonstrated significant scavenging capacities of DPPH and ABTS radicals with EC50 values of 51.32 and 33.32 µg/mL, respectively. The antibacterial activity of DL-AgNPs was shown to be significant against harmful bacteria, with a maximum zone of inhibition (21.5 ± 0.86 mm) against Staphylococcus aureus. Furthermore, DL-AgNPs exhibited effective catalytic activity to degrade environment-polluting dyes (methylene blue, methyl orange, and Congo red) and toxic chemicals (p-nitrophenol). The results of all these studies suggested that DL-AgNPs made from the leaf extract of Dalbergia sissoo have merit for application in the environmental and biomedical fields.

Comprehensive study on the selection and performance of the best electrode pair for electrocoagulation of textile wastewater using multi-criteria decision-making methods (TOPSIS, VIKOR and PROMETHEE II).

The accelerating environmental impact of the textile industry, especially in water management, requires efficient wastewater treatment strategies. This study examines the effectiveness of various electrode pairs in the Electrocoagulation (EC) process for treating textile wastewater, focusing on removing of Total Suspended Solids (TSS), turbidity, Chemical Oxygen Demand (COD), and Total Organic Carbon (TOC). A comprehensive analysis was conducted using thirty-six electrode pair combinations, consisting of six materials: Aluminium (Al), Zinc (Zn), Carbon (C), Copper (Cu), Mild Steel (MS), and Stainless Steel (SS). The results demonstrated that different electrode pairs yielded varying removal efficiencies for various pollutants, with the highest efficiencies being 92.09% for COD (Al-C pair), 99.66% for TSS (Al-Cu pair), 99.17% for turbidity (Al-MS pair), and 70.99% for TOC (SS-SS pair). However, no single electrode pair excelled in removing all pollutant categories. To address this, three Multi-Criteria Decision Making (MCDM) methods such as TOPSIS, VIKOR, and PROMETHEE II were used to assess the most effective electrode pair. The results indicated that the Al-Zn combination was the most efficient, exhibiting high removal efficiencies for various pollutants (99.32% for TSS, 98.88% for turbidity, 68.62% for COD, and 57.96% for TOC). This study demonstrates that the EC process can effectively treat textile effluent and emphasizes the importance of selecting suitable electrode materials. Furthermore, pollutant removal was optimal with the Al-Zn electrode pair, offering a balanced and efficient approach to textile wastewater treatment. Thus, MCDM methods offer a robust framework for assessing and optimizing electrode selection, providing valuable insights for sustainable environmental management practices.

Microplastic pollution: a review of techniques to identify microplastics and their threats to the aquatic ecosystem.

Microplastics (MPs), small synthetic particles, have emerged as perilous chemical pollutants in aquatic habitats, causing grave concerns about their disruptive effects on ecosystems. The fauna and flora inhabiting these specific environments consume these MPs, unwittingly introducing them into the intricate web of the food chain. In this comprehensive evaluation, the current methods of identifying MPs are amalgamated and their profound impacts on marine and freshwater ecosystems are discussed. There are many potential risks associated with MPs, including the dangers of ingestion and entanglement, as well as internal injuries and digestive obstructions, both marine and freshwater organisms. In this review, the merits and limitations of diverse identification techniques are discussed, including spanning chemical analysis, thermal identification, and spectroscopic imaging such as Fourier-transform infrared spectroscopy (FTIR), Raman spectroscopy, and fluorescent microscopy. Additionally, it discusses the prevalence of MPs, the factors that affect their release into aquatic ecosystems, as well as their plausible impact on various aquatic ecosystems. Considering these disconcerting findings, it is imperative that appropriate measures should be taken to assess the potential risks of MP pollution, protect aquatic life and human health, and foster sustainable development.

Deep learning-based clustering approaches for bioinformatics.

Clustering is central to many data-driven bioinformatics research and serves a powerful computational method. In particular, clustering helps at analyzing unstructured and high-dimensional data in the form of sequences, expressions, texts and images. Further, clustering is used to gain insights into biological processes in the genomics level, e.g. clustering of gene expressions provides insights on the natural structure inherent in the data, understanding gene functions, cellular processes, subtypes of cells and understanding gene regulations. Subsequently, clustering approaches, including hierarchical, centroid-based, distribution-based, density-based and self-organizing maps, have long been studied and used in classical machine learning settings. In contrast, deep learning (DL)-based representation and feature learning for clustering have not been reviewed and employed extensively. Since the quality of clustering is not only dependent on the distribution of data points but also on the learned representation, deep neural networks can be effective means to transform mappings from a high-dimensional data space into a lower-dimensional feature space, leading to improved clustering results. In this paper, we review state-of-the-art DL-based approaches for cluster analysis that are based on representation learning, which we hope to be useful, particularly for bioinformatics research. Further, we explore in detail the training procedures of DL-based clustering algorithms, point out different clustering quality metrics and evaluate several DL-based approaches on three bioinformatics use cases, including bioimaging, cancer genomics and biomedical text mining. We believe this review and the evaluation results will provide valuable insights and serve a starting point for researchers wanting to apply DL-based unsupervised methods to solve emerging bioinformatics research problems.

Factors affecting depression among married adults: a gender-based household cross-sectional study.

BACKGROUND: Depression is a common mental health problem all over the world including Bangladesh. World Health Organization included it in the Mental Health Gap Action Programme as one of its priority conditions. Research on this issue is scanty in Bangladesh. Therefore, we designed to a gender-based household study on associated factors of depression among married adults in Rajshahi City of Bangladesh. METHODS: We conducted this household cross-sectional study from August 01 to October 31, 2019. A total of 708 married adults currently living together in Rajshahi City were recruited for this study. We applied a multi-stage random sampling technique for selecting samples and used a semi-structured questionnaire to collect necessary information from them. The Patient Health Questionnaire-9 was used for measuring depression and frequency distribution and binary logistic regression model were used for data analysis. RESULTS: The prevalence of depression (moderate to severe) was 14.4% (95% CI: 11.9-16.9) among married adults, and females (21.2%, 95% CI: 17.2-25.4) suffered more than males (7.6%, 95% CI: 4.8-10.5). A multiple binary logistic regression model established four main factors of depression among married females: (i) multiple marriage [AOR = 19.982; 95% CI: 10.081-39.610; p < 0.01]; (ii) poor relationship with spouse [AOR = 2.175; 95%CI: 1.068-4.428; p < 0.05]; (iii) chronic medical comorbidity [AOR = 1.876; 95%CI: 1.009-2.626; p < 0.05]; and (iv) 7-12 years duration of conjugal life [AOR = 2.091, 1.009-4.334; p < 0.05]. Two main factors of depression among married males were (i) multiple marriage [AOR = 24.605; 95% CI: 20.228-40.402; p < 0.01] and hard work [AOR = 4.358; 95%CI: 1.109-7.132; p < 0.05]. CONCLUSION: The prevalence of depression was significantly high among the study population, and females were the most vulnerable group. The concerned authorities and stakeholders should take appropriate measures to manage the problem with special focus on the risk factors and the vulnerable groups.

Genetic association in CYP3A4 and CYP3A5 genes elevate the risk of prostate cancer.

BACKGROUND: CYP3A4 and CYP3A5 are biologically potential genes responsible for prostate cancer. AIM: We aimed to analyse the expression and association of CYP3A4 and CYP3A5 genes in prostate cancer. SUBJECTS AND METHODS: Web-based bioinformatics tools were used to assess the association of CYP3A4 and CYP3A5 genes with prostate cancer risks. A case-control study of 210 prostate cancer cases and 207 controls was also approved to determine the allelic variants of the CYP3A4 gene- rs2740574 (CYP3A4*1B) and the variant of CYP3A5 gene-rs776746 (CYP3A5*3) using Polymerase Chain Reaction-Restriction Fragment Length Polymorphism (PCR-RFLP). The risk of prostate cancer was estimated as odds ratio (OR) and 95% confidence interval (CI) using unrestricted logistic regression models. RESULTS: Our in silico data confirmed that both CYP3A4 and CYP3A5 genes are significantly associated with higher prostate cancer risks. In the case of CYP3A4*1B polymorphism, the heterozygote (*1 A/*1B), mutant (*1B/*1B), and combined heterozygote plus mutant (*1A/*1B+*1B/*1B) genotypes showed 3.52-fold, 3.90-fold, and 3.67-fold increased risk of prostate cancer, respectively. In the case of CYP3A5*3 polymorphism, the heterozygote (*1/*3), mutant (*3/*3), and combined (*1/*3+*3/*3) genotypes were found to be significantly associated with 5.11-, 5.49-, and 5.28-fold greater risk of prostate cancer, respectively. CONCLUSION: Our results indicate that CYP3A4*1B and CYP3A5*3 are significantly associated with increased prostate cancer risk.KEY MESSAGESBioinformatics tools were used and concluded that the CYP3A4 and CYP3A5 genes were significantly associated with the development and progression of prostate cancer.CYP3A4 and CYP3A5 polymorphisms were significantly associated with an increased risk of prostate cancer.Polymerase Chain Reaction (PCR)-Restriction Fragment Length Polymorphism (RFLP) was used to estimate polymorphisms of prostate cancer progression in the Bangladeshi population.

The role of protected areas co-management in enhancing resistance and resilience of deciduous forest ecosystem to extreme climatic events in Bangladesh.

Due to ongoing and projected climate change as well as increasing anthropogenic disturbances, the tropical deciduous forest has been experiencing a decline in its biomass and productivity. To mitigate this adverse effect, many tropical countries have adopted forest co-management engaging local communities. However, the effects of co-management on the resistance and resilience of forest ecosystems to extreme climatic events have rarely been tested. The present study investigates the effects of co-management on resistance and resilience to extreme climatic events in two major tropical deciduous forest protected areas of Bangladesh, namely Madhupur National Park (MNP) and Bhawal National Park (BNP), through remotely sensed satellite data. We used the Google Earth Engine platform to access the Landsat images from 1990 to 2020 for a comprehensive assessment of the forest cover condition under two major management regimes (i.e., traditional and co-management). We find that co-management slows down the rate of forest destruction, where the rate of forest destruction was 108 ha year-1 in MNP and 121 ha year-1 in BNP during the year 1990-2008 under traditional forest management system. Under the co-management regime, forest cover increased by 19 ha year-1 and 41 ha year-1 from 2009 to 2020 respectively in MNP and BNP. Our study finds a highly significant correlation between rainfall (p < 0.001) and forest health, although co-management had poor impacts on forest resistance and resilience in case of extreme climatic events, such as drought and heavy rainfall. We find, no significant impacts of co-management on resistance and resilience to drought in MNP, and on resistance and resilience to heavy rainfall in MNP and BNP. In BNP, the impacts of co-management on resistance (p < 0.05) and resilience (p < 0.01) of forest to drought were highly significant. Forest co-management although have the potentials to reduce the deforestation rate by mitigating anthropogenic disturbances, its capacity to tackle the adverse impact of climate change was limited in our study. An adaptive co-management model, therefore, is crucial for mainstreaming the adverse effect of climate change on the tropical deciduous forest to harness the maximum potential of community participation in forest resources management.

Risk Areas for Influenza A(H5) Environmental Contamination in Live Bird Markets, Dhaka, Bangladesh.

We evaluated the presence of influenza A(H5) virus environmental contamination in live bird markets (LBMs) in Dhaka, Bangladesh. By using Bernoulli generalized linear models and multinomial logistic regression models, we quantified LBM-level factors associated with market work zone-specific influenza A(H5) virus contamination patterns. Results showed higher environmental contamination in LBMs that have wholesale and retail operations compared with retail-only markets (relative risk 0.69, 95% 0.51-0.93; p = 0.012) and in March compared with January (relative risk 2.07, 95% CI 1.44-2.96; p<0.001). Influenza A(H5) environmental contamination remains a public health problem in most LBMs in Dhaka, which underscores the need to implement enhanced biosecurity interventions in LBMs in Bangladesh.

Identification of molecular signatures and pathways to identify novel therapeutic targets in Alzheimer's disease: Insights from a systems biomedicine perspective.

Alzheimer's disease (AD) is a progressive neurodegenerative disease characterized by the accumulation of amyloid plaques and neurofibrillary tangles in the brain. However, there are no peripheral biomarkers available that can detect AD onset. This study aimed to identify the molecular signatures in AD through an integrative analysis of blood gene expression data. We used two microarray datasets (GSE4226 and GSE4229) comparing peripheral blood transcriptomes of AD patients and controls to identify differentially expressed genes (DEGs). Gene set and protein overrepresentation analysis, protein-protein interaction (PPI), DEGs-Transcription Factors (TFs) interactions, DEGs-microRNAs (miRNAs) interactions, protein-drug interactions, and protein subcellular localizations analyses were performed on DEGs common to the datasets. We identified 25 common DEGs between the two datasets. Integration of genome scale transcriptome datasets with biomolecular networks revealed hub genes (NOL6, ATF3, TUBB, UQCRC1, CASP2, SND1, VCAM1, BTF3, VPS37B), common transcription factors (FOXC1, GATA2, NFIC, PPARG, USF2, YY1) and miRNAs (mir-20a-5p, mir-93-5p, mir-16-5p, let-7b-5p, mir-708-5p, mir-24-3p, mir-26b-5p, mir-17-5p, mir-193-3p, mir-186-5p). Evaluation of histone modifications revealed that hub genes possess several histone modification sites associated with AD. Protein-drug interactions revealed 10 compounds that affect the identified AD candidate biomolecules, including anti-neoplastic agents (Vinorelbine, Vincristine, Vinblastine, Epothilone D, Epothilone B, CYT997, and ZEN-012), a dermatological (Podofilox) and an immunosuppressive agent (Colchicine). The subcellular localization of molecular signatures varied, including nuclear, plasma membrane and cytosolic proteins. In the present study, it was identified blood-cell derived molecular signatures that might be useful as candidate peripheral biomarkers in AD. It was also identified potential drugs and epigenetic data associated with these molecules that may be useful in designing therapeutic approaches to ameliorate AD.

Improving data workflow systems with cloud services and use of open data for bioinformatics research.

Data workflow systems (DWFSs) enable bioinformatics researchers to combine components for data access and data analytics, and to share the final data analytics approach with their collaborators. Increasingly, such systems have to cope with large-scale data, such as full genomes (about 200 GB each), public fact repositories (about 100 TB of data) and 3D imaging data at even larger scales. As moving the data becomes cumbersome, the DWFS needs to embed its processes into a cloud infrastructure, where the data are already hosted. As the standardized public data play an increasingly important role, the DWFS needs to comply with Semantic Web technologies. This advancement to DWFS would reduce overhead costs and accelerate the progress in bioinformatics research based on large-scale data and public resources, as researchers would require less specialized IT knowledge for the implementation. Furthermore, the high data growth rates in bioinformatics research drive the demand for parallel and distributed computing, which then imposes a need for scalability and high-throughput capabilities onto the DWFS. As a result, requirements for data sharing and access to public knowledge bases suggest that compliance of the DWFS with Semantic Web standards is necessary. In this article, we will analyze the existing DWFS with regard to their capabilities toward public open data use as well as large-scale computational and human interface requirements. We untangle the parameters for selecting a preferable solution for bioinformatics research with particular consideration to using cloud services and Semantic Web technologies. Our analysis leads to research guidelines and recommendations toward the development of future DWFS for the bioinformatics research community.

Establishment of the Embryonic Shoot Meristem Involves Activation of Two Classes of Genes with Opposing Functions for Meristem Activities.

The shoot meristem, a stem-cell-containing tissue initiated during plant embryogenesis, is responsible for continuous shoot organ production in postembryonic development. Although key regulatory factors including KNOX genes are responsible for stem cell maintenance in the shoot meristem, how the onset of such factors is regulated during embryogenesis is elusive. Here, we present evidence that the two KNOX genes STM and KNAT6 together with the two other regulatory genes BLR and LAS are functionally important downstream genes of CUC1 and CUC2, which are a redundant pair of genes that specify the embryonic shoot organ boundary. Combined expression of STM with any of KNAT6, BLR, and LAS can efficiently rescue the defects of shoot meristem formation and/or separation of cotyledons in cuc1cuc2 double mutants. In addition, CUC1 and CUC2 are also required for the activation of KLU, a cytochrome P450-encoding gene known to restrict organ production, and KLU counteracts STM in the promotion of meristem activity, providing a possible balancing mechanism for shoot meristem maintenance. Together, these results establish the roles for CUC1 and CUC2 in coordinating the activation of two classes of genes with opposite effects on shoot meristem activity.

Ligand-mediated protein degradation reveals functional conservation among sequence variants of the CUL4-type E3 ligase substrate receptor cereblon.

Upon binding to thalidomide and other immunomodulatory drugs, the E3 ligase substrate receptor cereblon (CRBN) promotes proteosomal destruction by engaging the DDB1-CUL4A-Roc1-RBX1 E3 ubiquitin ligase in human cells but not in mouse cells, suggesting that sequence variations in CRBN may cause its inactivation. Therapeutically, CRBN engagers have the potential for broad applications in cancer and immune therapy by specifically reducing protein expression through targeted ubiquitin-mediated degradation. To examine the effects of defined sequence changes on CRBN's activity, we performed a comprehensive study using complementary theoretical, biophysical, and biological assays aimed at understanding CRBN's nonprimate sequence variations. With a series of recombinant thalidomide-binding domain (TBD) proteins, we show that CRBN sequence variants retain their drug-binding properties to both classical immunomodulatory drugs and dBET1, a chemical compound and targeting ligand designed to degrade bromodomain-containing 4 (BRD4) via a CRBN-dependent mechanism. We further show that dBET1 stimulates CRBN's E3 ubiquitin-conjugating function and degrades BRD4 in both mouse and human cells. This insight paves the way for studies of CRBN-dependent proteasome-targeting molecules in nonprimate models and provides a new understanding of CRBN's substrate-recruiting function.

Associations of the PON1 rs854560 polymorphism with plasma lipid levels: a meta-analysis.

BACKGROUND: Previous studies have investigated the associations of paraoxonase 1 (PON1) rs854560 polymorphism with plasma lipid levels, but the results are inconclusive. This meta-analysis aimed to clarify the associations of the rs854560 polymorphism with plasma lipid levels. METHODS: A comprehensive search of the literature was carried out by using the databases which include Medline, Google Scholar, Web of Science, Embase, Cochrane Library, China National Knowledge Infrastructure (CNKI), Wanfang and VIP database up till August 2018. The pooled standardized mean difference (SMD) with 95% confidence interval (CI) was used to assess the differences in lipid levels between the genotypes. Begg's funnel plots and Egger's test were used to examine the publication bias. RESULTS: A total of 41 studies (22,844 subjects) were identified for the associations of rs854560 polymorphism with plasma lipid levels. The M carriers had lower levels of high-density lipoprotein Cholesterol (HDL-C) (SMD = - 0.15, 95% CI = - 0.23--0.07, P < 0.01) and apolipoprotein A-I (APOA1) (SMD = - 0.67, 95% CI = - 0.93--0.41, P < 0.01) than the non-carriers. Subgroup analysis by ethnicity revealed that the effect on HDL level was significant in Caucasians and the subjects of other ethnic origins. No publication bias was detected in this meta-analysis. CONCLUSIONS: The meta-analysis suggests that the PON1 rs854560 polymorphism is associated with a lower HDL-C level in Caucasians and subjects of other ethnic origins.

Salinization and arsenic contamination of surface water in southwest Bangladesh.

To identify the causes of salinization and arsenic contamination of surface water on an embanked island (i.e., polder) in the tidal delta plain of SW Bangladesh we collected and analyzed water samples in the dry (May) and wet (October) seasons in 2012-2013. Samples were collected from rice paddies (wet season), saltwater ponds used for brine shrimp aquaculture (dry season), freshwater ponds and tidal channels (both wet and dry season), and rainwater collectors. Continuous measurements of salinity from March 2012 to February 2013 show that tidal channel water increases from ~0.15 ppt in the wet season up to ~20 ppt in the dry season. On the polder, surface water exceeds the World Health Organization drinking water guideline of 10 µg As/L in 78% of shrimp ponds and 27% of rice paddies, raising concerns that produced shrimp and rice could have unsafe levels of As. Drinking water sources also often have unsafe As levels, with 83% of tubewell and 43% of freshwater pond samples having >10 µg As/L. Water compositions and field observations are consistent with shrimp pond water being sourced from tidal channels during the dry season, rather than the locally saline groundwater from tubewells. Irrigation water for rice paddies is also obtained from the tidal channels, but during the wet season when surface waters are fresh. Salts become concentrated in irrigation water through evaporation, with average salinity increasing from 0.43 ppt in the tidal channel source to 0.91 ppt in the rice paddies. Our observations suggest that the practice of seasonally alternating rice and shrimp farming in a field has a negligible effect on rice paddy water salinity. Also, shrimp ponds do not significantly affect the salinity of adjacent surface water bodies or subjacent groundwater because impermeable shallow surface deposits of silt and clay mostly isolate surface water bodies from each other and from the shallow groundwater aquifer. Bivariate plots of conservative element concentrations show that all surface water types lie on mixing lines between dry season tidal channel water and rainwater, i.e., all are related by varying degrees of salinization. High As concentrations in dry season tidal channel water and shrimp ponds likely result from groundwater exfiltration and upstream irrigation in the dry season. Arsenic is transferred from tidal channels to rice paddies through irrigation. Including groundwater samples from the same area (Ayers et al. in Geochem Trans 17:1-22, 2016), principal components analysis and correlation analysis reveal that salinization explains most variation in surface water compositions, whereas progressive reduction of buried surface water by dissolved organic carbon is responsible for the nonconservative behavior of S, Fe, and As and changes in Eh and alkalinity of groundwater.