Computational Systems Analysis on Polycystic Ovarian Syndrome (PCOS)

Complex diseases are caused by a combination of genetic and environmental factors. Unraveling the molecular pathways from the genetic factors that affect a phenotype is always difficult, but in the case of complex diseases, this is further complicated since genetic factors in affected individuals might be different. Polycystic ovarian syndrome (PCOS) is an example of a complex disease with limited molecular information. Recently, PCOS molecular omics data have increasingly appeared in many publications. We conduct extensive bioinformatics analyses on the data and perform strong integration of experimental and computational biology to understand its complex biological systems in examining multiple interacting genes and their products. PCOS involves networks of genes, and to understand them, those networks must be mapped. This approach has emerged as powerful tools for studying complex diseases and been coined as network biology. Network biology encompasses wide range of network types including those based on physical interactions between and among cellular components and those baised on similarity among patients or diseases. Each of these offers distinct biological clues that may help scientists transform their cellular parts list into insights about complex diseases. This chapter will discuss some computational analysis aspects on the omics studies that have been conducted in PCOS

Applications of next-generation sequencing technologies and computational tools in molecular evolution and aquatic animals conservation studies : a short review

Aquatic ecosystems that form major biodiversity hotspots are critically threatened due to environmental and anthropogenic stressors. We believe that, in this genomic era, computational methods can be applied to promote aquatic biodiversity conservation by addressing questions related to the evolutionary history of aquatic organisms at the molecular level. However, huge amounts of genomics data generated can only be discerned through the use of bioinformatics. Here, we examine the applications of next-generation sequencing technologies and bioinformatics tools to study the molecular evolution of aquatic animals and discuss the current challenges and future perspectives of using bioinformatics toward aquatic animal conservation efforts

Pattern analysis of corona virus disease (COVID-19) - outbreak in Malaysia

The ongoing Corona Virus Disease (COVID-19) outbreak is now declared as the pandemic by World Health Organization (WHO). This disease began in Wuhan, China in late 2019 and is widely spread now all over the world. Progressively, Malaysia has been the leading country in Southeast Asia for this outbreak with cases more than 2000 as on 26th March 2020. This article highlights the analysis of the outbreak pattern which follows the exponential growth regression line. Data is collected daily for 66 days starting from the 1st case defined on 25th January 2020. Regression line is used because it can describe the relationship between predictors and the outcome within the datasets that can be used for prediction purposes. Fitting the real data to the graph, an equation which follows the exponential growth model is obtained. The calculation of the relative error between the exact and the approximate data shows that the pattern follows the exponential growth model as it is compared with the quadratic regression line. This analysis can be particularly beneficial for the health authorities in preparing immediate and effective strategies to flatten the curve. Malaysia government is currently working hard in flattening the curve by implementing Restricted Movement Order (RMO)

Identification of Potential Genes Encoding Protein Transporters in Arabidopsis thaliana Glucosinolate (GSL) Metabolism

Several species in Brassicaceae produce glucosinolates (GSLs) to protect themselves against pests. As demonstrated in A. thaliana, the reallocation of defence compounds, of which GSLs are a major part, is highly dependent on transport processes and serves to protect high-value tissues such as reproductive tissues. This study aimed to identify potential GSL-transporter proteins (TPs) using a network-biology approach. The known A. thaliana GSL genes were retrieved from the literature and pathway databases and searched against several co-expression databases to generate a gene network consisting of 1267 nodes and 14,308 edges. In addition, 1151 co-expressed genes were annotated, integrated, and visualised using relevant bioinformatic tools. Based on three criteria, 21 potential GSL genes encoding TPs were selected. The AST68 and ABCG40 potential GSL TPs were chosen for further investigation because their subcellular localisation is similar to that of known GSL TPs (SULTR1;1 and SULTR1;2) and ABCG36, respectively. However, AST68 was selected for a molecular-docking analysis using AutoDOCK Vina and AutoDOCK 4.2 with the generated 3D model, showing that both domains were well superimposed on the homologs. Both molecular-docking tools calculated good binding-energy values between the sulphate ion and Ser419 and Val172, with the formation of hydrogen bonds and van der Waals interactions, respectively, suggesting that AST68 was one of the sulphate transporters involved in GSL biosynthesis. This finding illustrates the ability to use computational analysis on gene co-expression data to screen and characterise plant TPs on a large scale to comprehensively elucidate GSL metabolism in A. thaliana. Most importantly, newly identified potential GSL transporters can serve as molecular tools in improving the nutritional value of crops

Protein-protein interaction network analysis on the whiteleg shrimp Penaeus vannamei and Vibrio parahaemolyticus host-pathogen relationship reveals possible proteins and pathways involved during infection

Whiteleg shrimp, Penaeus vannamei, is the prime candidate in shrimp aquaculture that significantly contributes to the economic growth of the global aquaculture sector. However, P. vannamei culture is constantly plagued with pathogenic infection of Vibrio spp., including Vibrio parahaemolyticus, which impairs shrimp production and consequently results in significant financial losses. Despite this persistent problem, the relationship between P. vannamei and V. parahaemolyticus remains unclear. The discovery of protein-protein interactions (PPIs) between host and pathogen might improve the understanding of bacterial infection in shrimp since most pathogenic bacteria infect hosts through multiple mechanisms. Hence, this study aims to identify the candidate effector proteins of V. parahaemolyticus and their targeted host proteins and potential pathways involved in its infection using a host-pathogen PPI approach. The P. vannamei – V. parahaemolyticus PPI (PVPPI) network was constructed using in silico methods, followed by clustering and pathway enrichment analyses. The constructed PVPPI network consisted of 448871 interactions between 4427 P. vannamei and 18119 V. parahaemolyticus proteins. Clustering analysis identified several effector proteins, i.e., secretion systems proteins, and their targeted proteins, including cell signalling proteins. The bacterial secretion system, mTOR signalling, and endocytosis were among the enriched pathways that might be involved during V. parahaemolyticus infection in P. vannamei. This study reports the first host-pathogen PPI network between P. vannamei and V. parahaemolyticus, where several hosts and pathogen proteins with potential pathways are highlighted. These findings offer new insights into the interaction between V. parahaemolyticus and P. vannamei, a vital aspect that could serve as a baseline for future disease prevention and treatment in the shrimp aquaculture industry

Identification of <i>Pv</i>Hsp70-1 and <i>Pv</i>Hsp70-2 by LC-MS/MS.

<p>Mass spectra were analyzed to identify proteins of interest using Mascot sequence matching software with the Ludwig NR database. Peptides were assessed individually with a score greater than 44 considered significant. Data shown for both proteins are the highest scores matched.</p><p>^aProteins identified in excised gel slices.</p><p>^bPeptide sequences derived from gel samples.</p><p>^cAccession numbers for proteins generated by database searching.</p><p>^dIdentified protein name, best match with the Hsp70s identified from the gels.</p><p>^eCoverage (%), Sequence coverage for the most closely matched protein.</p><p>^fCombined scores of all observed mass spectra that matched the amino acid sequences within the protein of interest.</p><p>Identification of <i>Pv</i>Hsp70-1 and <i>Pv</i>Hsp70-2 by LC-MS/MS.</p

Hsp70 was induced in <i>P</i>. <i>viridis</i> by heat shock.

<p>Protein samples from the adductor muscle (A, B), foot (C, D), gill (E, F) and mantel (G, H) were resolved in 7% SDS polyacrylamide gels and either stained with Coomassie blue (A, C, E, G) or blotted to membranes and probed with antibody to Hsp70 (B, D, F, H). Arrows labeled p70 indicate the position of 70 kDa proteins in the gel. M, molecular mass markers in kDa; H-Hsp70, recombinant human Hsp70.</p

NLHS induced the synthesis of Hsp70 in <i>P</i>. <i>viridis</i>.

<p>The amounts of <i>Pv</i>Hsp70-1 and <i>Pv</i>Hsp70-2 in (A) adductor muscle, (B) foot, (C) gill and (D) mantle of <i>P</i>. <i>viridis</i> exposed to heat shock at 30, 32, 34, 36, 38 and 40°C were determined by densitometry analysis of antibody-stained Western blots as described in Materials and Methods. Bars that are not visible denote the absence of <i>Pv</i>Hsp70-2. Asterisk (*) represents statistical difference against the control treatment (<i>P</i><0.05). The experiment was performed in duplicate. 28, mussels not receiving NLHS (control).</p

NLHS enhanced the thermotolerance of <i>P</i>. <i>viridis</i>.

<p><i>P</i>. <i>viridis</i> acclimatized at 28°C were exposed to NLHS and then heated for 30 min at their LT₅₀ (42°C) and LHT (44°C). Survivors were counted 24 h after challenge. Data are presented as mean ± standard errors. Asterisk (*) represents statistical difference against the control (<i>P</i>< 0.05). The experiments were performed in triplicate. Non-induced, mussels not receiving NLHS; Induced, mussels exposed to NLHS.</p