The output of state-of-the-art reverse-engineering methods for biological networks is often based on the fitting of a mathematical model to the data. Typically, different datasets do not give single consistent network predictions but rather an ensemble of inconsistent networks inferred under the same reverse-engineering method that are only consistent with the specific experimentally measured data. Here, we focus on an alternative approach for combining the information contained within such an ensemble of inconsistent gene networks called meta-analysis, to make more accurate predictions and to estimate the reliability of these predictions. We review two existing meta-analysis approaches; the Fisher transformation combined coefficient test (FTCCT) and Fisher's inverse combined probability test (FICPT); and compare their performance with five well-known methods, ARACNe, Context Likelihood or Relatedness network (CLR), Maximum Relevance Minimum Redundancy (MRNET), Relevance Network (RN) and Bayesian Network (BN). We conducted in-depth numerical ensemble simulations and demonstrated for biological expression data that the meta-analysis approaches consistently outperformed the best gene regulatory network inference (GRNI) methods in the literature. Furthermore, the meta-analysis approaches have a low computational complexity. We conclude that the meta-analysis approaches are a powerful tool for integrating different datasets to give more accurate and reliable predictions for biological networks.
The flat-headed cat (Prionailurus planiceps) is one of the world's least known, highly threatened felids with a distribution restricted to tropical lowland rainforests in Peninsular Thailand/Malaysia, Borneo and Sumatra. Throughout its geographic range large-scale anthropogenic transformation processes, including the pollution of fresh-water river systems and landscape fragmentation, raise concerns regarding its conservation status. Despite an increasing number of camera-trapping field surveys for carnivores in South-East Asia during the past two decades, few of these studies recorded the flat-headed cat.
Parkinson's disease (PD) is an irreversible and incurable multigenic neurodegenerative disorder. It involves progressive loss of mid brain dopaminergic neurons in the substantia nigra pars compacta (SN). We compared brain gene expression profiles with those from the peripheral blood cells of a separate sample of PD patients to identify disease-associated genes. Here, we demonstrate the use of gene expression profiling of brain and blood for detecting valid targets and identifying early PD biomarkers. Implementing this systematic approach, we discovered putative PD risk genes in brain, delineated biological processes and molecular functions that may be particularly disrupted in PD and also identified several putative PD biomarkers in blood. 20 of the differentially expressed genes in SN were also found to be differentially expressed in the blood. Further application of this methodology to other brain regions and neurological disorders should facilitate the discovery of highly reliable and reproducible candidate risk genes and biomarkers for PD. The identification of valid peripheral biomarkers for PD may ultimately facilitate early identification, intervention, and prevention efforts as well.
Bioinformatics analyses of caleosin/peroxygenases (CLO/PXG) demonstrated that these genes are present in the vast majority of Viridiplantae taxa for which sequence data are available. Functionally active CLO/PXG proteins with roles in abiotic stress tolerance and lipid droplet storage are present in some Trebouxiophycean and Chlorophycean green algae but are absent from the small number of sequenced Prasinophyceaen genomes. CLO/PXG-like genes are expressed during dehydration stress in Charophyte algae, a sister clade of the land plants (Embryophyta). CLO/PXG-like sequences are also present in all of the >300 sequenced Embryophyte genomes, where some species contain as many as 10-12 genes that have arisen via selective gene duplication. Angiosperm genomes harbour at least one copy each of two distinct CLO/PX isoforms, termed H (high) and L (low), where H-forms contain an additional C-terminal motif of about 30-50 residues that is absent from L-forms. In contrast, species in other Viridiplantae taxa, including green algae, non-vascular plants, ferns and gymnosperms, contain only one (or occasionally both) of these isoforms per genome. Transcriptome and biochemical data show that CLO/PXG-like genes have complex patterns of developmental and tissue-specific expression. CLO/PXG proteins can associate with cytosolic lipid droplets and/or bilayer membranes. Many of the analysed isoforms also have peroxygenase activity and are involved in oxylipin metabolism. The distribution of CLO/PXG-like genes is consistent with an origin >1 billion years ago in at least two of the earliest diverging groups of the Viridiplantae, namely the Chlorophyta and the Streptophyta, after the Viridiplantae had already diverged from other Archaeplastidal groups such as the Rhodophyta and Glaucophyta. While algal CLO/PXGs have roles in lipid packaging and stress responses, the Embryophyte proteins have a much wider spectrum of roles and may have been instrumental in the colonisation of terrestrial habitats and the subsequent diversification as the major land flora.
Comparative genomics and transcriptomic analyses were performed on two agronomically important groups of genes from oil palm versus other major crop species and the model organism, Arabidopsis thaliana. The first analysis was of two gene families with key roles in regulation of oil quality and in particular the accumulation of oleic acid, namely stearoyl ACP desaturases (SAD) and acyl-acyl carrier protein (ACP) thioesterases (FAT). In both cases, these were found to be large gene families with complex expression profiles across a wide range of tissue types and developmental stages. The detailed classification of the oil palm SAD and FAT genes has enabled the updating of the latest version of the oil palm gene model. The second analysis focused on disease resistance (R) genes in order to elucidate possible candidates for breeding of pathogen tolerance/resistance. Ortholog analysis showed that 141 out of the 210 putative oil palm R genes had homologs in banana and rice. These genes formed 37 clusters with 634 orthologous genes. Classification of the 141 oil palm R genes showed that the genes belong to the Kinase (7), CNL (95), MLO-like (8), RLK (3) and Others (28) categories. The CNL R genes formed eight clusters. Expression data for selected R genes also identified potential candidates for breeding of disease resistance traits. Furthermore, these findings can provide information about the species evolution as well as the identification of agronomically important genes in oil palm and other major crops.
Transporters mediate the movement of compounds across the membranes that separate the cell from its environment and across the inner membranes surrounding cellular compartments. It is estimated that one third of a proteome consists of membrane proteins, and many of these are transport proteins. Given the increase in the number of genomes being sequenced, there is a need for computational tools that predict the substrates that are transported by the transmembrane transport proteins. In this paper, we present TranCEP, a predictor of the type of substrate transported by a transmembrane transport protein. TranCEP combines the traditional use of the amino acid composition of the protein, with evolutionary information captured in a multiple sequence alignment (MSA), and restriction to important positions of the alignment that play a role in determining the specificity of the protein. Our experimental results show that TranCEP significantly outperforms the state-of-the-art predictors. The results quantify the contribution made by each type of information used.
One of the key aspects of computational systems biology is the investigation on the dynamic biological processes within cells. Computational models are often required to elucidate the mechanisms and principles driving the processes because of the nonlinearity and complexity. The models usually incorporate a set of parameters that signify the physical properties of the actual biological systems. In most cases, these parameters are estimated by fitting the model outputs with the corresponding experimental data. However, this is a challenging task because the available experimental data are frequently noisy and incomplete. In this paper, a new hybrid optimization method is proposed to estimate these parameters from the noisy and incomplete experimental data. The proposed method, called Swarm-based Chemical Reaction Optimization, integrates the evolutionary searching strategy employed by the Chemical Reaction Optimization, into the neighbouring searching strategy of the Firefly Algorithm method. The effectiveness of the method was evaluated using a simulated nonlinear model and two biological models: synthetic transcriptional oscillators, and extracellular protease production models. The results showed that the accuracy and computational speed of the proposed method were better than the existing Differential Evolution, Firefly Algorithm and Chemical Reaction Optimization methods. The reliability of the estimated parameters was statistically validated, which suggests that the model outputs produced by these parameters were valid even when noisy and incomplete experimental data were used. Additionally, Akaike Information Criterion was employed to evaluate the model selection, which highlighted the capability of the proposed method in choosing a plausible model based on the experimental data. In conclusion, this paper presents the effectiveness of the proposed method for parameter estimation and model selection problems using noisy and incomplete experimental data. This study is hoped to provide a new insight in developing more accurate and reliable biological models based on limited and low quality experimental data.
Despite the effectiveness of Pap-smear test in reducing the mortality rate due to cervical cancer, the criteria of the reporting standard of the Pap-smear test are mostly qualitative in nature. This study addresses the issue on how to define the criteria in a more quantitative and definite term. A negative Pap-smear test result, i.e. negative for intraepithelial lesion or malignancy (NILM), is qualitatively defined to have evenly distributed, finely granular chromatin in the nuclei of cervical squamous cells. To quantify this chromatin pattern, this study employed Fuzzy C-Means clustering as the segmentation technique, enabling different degrees of chromatin segmentation to be performed on sample images of non-neoplastic squamous cells. From the simulation results, a model representing the chromatin distribution of non-neoplastic cervical squamous cell is constructed with the following quantitative characteristics: at the best representative sensitivity level 4 based on statistical analysis and human experts' feedbacks, a nucleus of non-neoplastic squamous cell has an average of 67 chromatins with a total area of 10.827 μm2; the average distance between the nearest chromatin pair is 0.508 μm and the average eccentricity of the chromatin is 0.47.
The Cancer Genetic Markers of Susceptibility genome-wide association study (GWAS) originally identified a single nucleotide polymorphism (SNP) rs11249433 at 1p11.2 associated with breast cancer risk. To fine-map this locus, we genotyped 92 SNPs in a 900kb region (120,505,799-121,481,132) flanking rs11249433 in 45,276 breast cancer cases and 48,998 controls of European, Asian and African ancestry from 50 studies in the Breast Cancer Association Consortium. Genotyping was done using iCOGS, a custom-built array. Due to the complicated nature of the region on chr1p11.2: 120,300,000-120,505,798, that lies near the centromere and contains seven duplicated genomic segments, we restricted analyses to 429 SNPs excluding the duplicated regions (42 genotyped and 387 imputed). Per-allelic associations with breast cancer risk were estimated using logistic regression models adjusting for study and ancestry-specific principal components. The strongest association observed was with the original identified index SNP rs11249433 (minor allele frequency (MAF) 0.402; per-allele odds ratio (OR) = 1.10, 95% confidence interval (CI) 1.08-1.13, P = 1.49 x 10-21). The association for rs11249433 was limited to ER-positive breast cancers (test for heterogeneity P≤8.41 x 10-5). Additional analyses by other tumor characteristics showed stronger associations with moderately/well differentiated tumors and tumors of lobular histology. Although no significant eQTL associations were observed, in silico analyses showed that rs11249433 was located in a region that is likely a weak enhancer/promoter. Fine-mapping analysis of the 1p11.2 breast cancer susceptibility locus confirms this region to be limited to risk to cancers that are ER-positive.
Immunoinformatics plays a pivotal role in vaccine design, immunodiagnostic development, and antibody production. In the past, antibody design and vaccine development depended exclusively on immunological experiments which are relatively expensive and time-consuming. However, recent advances in the field of immunological bioinformatics have provided feasible tools which can be used to lessen the time and cost required for vaccine and antibody development. This approach allows the selection of immunogenic regions from the pathogen genomes. The ideal regions could be developed as potential vaccine candidates to trigger protective immune responses in the hosts. At present, epitope-based vaccines are attractive concepts which have been successfully trailed to develop vaccines which target rapidly mutating pathogens. In this article, we provide an overview of the current progress of immunoinformatics and their applications in the vaccine design, immune system modeling and therapeutics.
Numerous cancer studies have combined different datasets for the prognosis of patients. This study incorporated four networks for significant directed random walk (sDRW) to predict cancerous genes and risk pathways. The study investigated the feasibility of cancer prediction via different networks. In this study, multiple micro array data were analysed and used in the experiment. Six gene expression datasets were applied in four networks to study the effectiveness of the networks in sDRW in terms of cancer prediction. The experimental results showed that one of the proposed networks is outstanding compared to other networks. The network is then proposed to be implemented in sDRW as a walker network. This study provides a foundation for further studies and research on other networks. We hope these finding will improve the prognostic methods of cancer patients.
Although the idea of bacteria causing different types of cancer has exploded about century ago, the potential mechanisms of carcinogenesis is still not well established. Many reports showed the involvement of M. hominis in the development of prostate cancer, however, mechanistic approach for growth and development of prostate cancer has been poorly understood. In the current study, we predicted M. hominis proteins targeting in the mitochondria and cytoplasm of host cells and their implication in prostate cancer. A total of 77 and 320 proteins from M. hominis proteome were predicted to target in the mitochondria and cytoplasm of host cells respectively. In particular, various targeted proteins may interfere with normal growth behaviour of host cells, thereby altering the decision of programmed cell death. Furthermore, we investigated possible mechanisms of the mitochondrial and cytoplasmic targeted proteins of M. hominis in etiology of prostate cancer by screening the whole proteome.
Recent advances in machine learning and natural language processing have made it possible to profoundly advance our ability to accurately predict protein structures and their functions. While such improvements are significantly impacting the fields of biology and biotechnology at large, such methods have the downside of high demands in terms of computing power and runtime, hampering their applicability to large datasets. Here, we present NetSurfP-3.0, a tool for predicting solvent accessibility, secondary structure, structural disorder and backbone dihedral angles for each residue of an amino acid sequence. This NetSurfP update exploits recent advances in pre-trained protein language models to drastically improve the runtime of its predecessor by two orders of magnitude, while displaying similar prediction performance. We assessed the accuracy of NetSurfP-3.0 on several independent test datasets and found it to consistently produce state-of-the-art predictions for each of its output features, with a runtime that is up to to 600 times faster than the most commonly available methods performing the same tasks. The tool is freely available as a web server with a user-friendly interface to navigate the results, as well as a standalone downloadable package.
CATH (https://www.cathdb.info) identifies domains in protein structures from wwPDB and classifies these into evolutionary superfamilies, thereby providing structural and functional annotations. There are two levels: CATH-B, a daily snapshot of the latest domain structures and superfamily assignments, and CATH+, with additional derived data, such as predicted sequence domains, and functionally coherent sequence subsets (Functional Families or FunFams). The latest CATH+ release, version 4.3, significantly increases coverage of structural and sequence data, with an addition of 65,351 fully-classified domains structures (+15%), providing 500 238 structural domains, and 151 million predicted sequence domains (+59%) assigned to 5481 superfamilies. The FunFam generation pipeline has been re-engineered to cope with the increased influx of data. Three times more sequences are captured in FunFams, with a concomitant increase in functional purity, information content and structural coverage. FunFam expansion increases the structural annotations provided for experimental GO terms (+59%). We also present CATH-FunVar web-pages displaying variations in protein sequences and their proximity to known or predicted functional sites. We present two case studies (1) putative cancer drivers and (2) SARS-CoV-2 proteins. Finally, we have improved links to and from CATH including SCOP, InterPro, Aquaria and 2DProt.
This research investigated a UPLC-QTOF/ESI-MS-based phytochemical profiling of Combretum indicum leaf extract (CILEx), and explored its in vitro antioxidant and in vivo antidiabetic effects in a Long-Evans rat model. After a one-week intervention, the animals' blood glucose, lipid profile, and pancreatic architectures were evaluated. UPLC-QTOF/ESI-MS fragmentation of CILEx and its eight docking-guided compounds were further dissected to evaluate their roles using bioinformatics-based network pharmacological tools. Results showed a very promising antioxidative effect of CILEx. Both doses of CILEx were found to significantly (p < 0.05) reduce blood glucose, low-density lipoprotein (LDL), and total cholesterol (TC), and increase high-density lipoprotein (HDL). Pancreatic tissue architectures were much improved compared to the diabetic control group. A computational approach revealed that schizonepetoside E, melianol, leucodelphinidin, and arbutin were highly suitable for further therapeutic assessment. Arbutin, in a Gene Ontology and PPI network study, evolved as the most prospective constituent for 203 target proteins of 48 KEGG pathways regulating immune modulation and insulin secretion to control diabetes. The fragmentation mechanisms of the compounds are consistent with the obtained effects for CILEx. Results show that the natural compounds from CILEx could exert potential antidiabetic effects through in vivo and computational study.
In mass spectrometry (MS)-based metabolomics, missing values (NAs) may be due to different causes, including sample heterogeneity, ion suppression, spectral overlap, inappropriate data processing, and instrumental errors. Although a number of methodologies have been applied to handle NAs, NA imputation remains a challenging problem. Here, we propose a non-negative matrix factorization (NMF)-based method for NA imputation in MS-based metabolomics data, which makes use of both global and local information of the data. The proposed method was compared with three commonly used methods: k-nearest neighbors (kNN), random forest (RF), and outlier-robust (ORI) missing values imputation. These methods were evaluated from the perspectives of accuracy of imputation, retrieval of data structures, and rank of imputation superiority. The experimental results showed that the NMF-based method is well-adapted to various cases of data missingness and the presence of outliers in MS-based metabolic profiles. It outperformed kNN and ORI and showed results comparable with the RF method. Furthermore, the NMF method is more robust and less susceptible to outliers as compared with the RF method. The proposed NMF-based scheme may serve as an alternative NA imputation method which may facilitate biological interpretations of metabolomics data.
Understanding past dispersal and breeding events can provide insight into ecology and evolution and can help inform strategies for conservation and the control of pest species. However, parent-offspring dispersal can be difficult to investigate in rare species and in small pest species such as mosquitoes. Here, we develop a methodology for estimating parent-offspring dispersal from the spatial distribution of close kin, using pairwise kinship estimates derived from genome-wide single nucleotide polymorphisms (SNPs). SNPs were scored in 162 Aedes aegypti (yellow fever mosquito) collected from eight close-set, high-rise apartment buildings in an area of Malaysia with high dengue incidence. We used the SNPs to reconstruct kinship groups across three orders of kinship. We transformed the geographical distances between all kin pairs within each kinship category into axial standard deviations of these distances, then decomposed these into components representing past dispersal events. From these components, we isolated the axial standard deviation of parent-offspring dispersal and estimated neighbourhood area (129 m), median parent-offspring dispersal distance (75 m) and oviposition dispersal radius within a gonotrophic cycle (36 m). We also analysed genetic structure using distance-based redundancy analysis and linear regression, finding isolation by distance both within and between buildings and estimating neighbourhood size at 268 individuals. These findings indicate the scale required to suppress local outbreaks of arboviral disease and to target releases of modified mosquitoes for mosquito and disease control. Our methodology is readily implementable for studies of other species, including pests and species of conservation significance.