A comprehensive analysis of genetic risk for metabolic syndrome in the Egyptian population via allele frequency investigation and Missense3D predictions

Abstract Diabetes mellitus (DM) represents a major health problem in Egypt and worldwide, with increasing numbers of patients with prediabetes every year. Numerous factors, such as obesity, hyperlipidemia, and hypertension, which have recently become serious concerns, affect the complex pathophysiology of diabetes. These metabolic syndrome diseases are highly linked to genetic variability that drives certain populations, such as Egypt, to be more susceptible to developing DM. Here we conduct a comprehensive analysis to pinpoint the similarities and uniqueness among the Egyptian genome reference and the 1000-genome subpopulations (Europeans, Ad-Mixed Americans, South Asians, East Asians, and Africans), aiming at defining the potential genetic risk of metabolic syndromes. Selected approaches incorporated the analysis of the allele frequency of the different populations’ variations, supported by genotypes’ principal component analysis. Results show that the Egyptian’s reference metabolic genes were clustered together with the Europeans’, Ad-Mixed Americans’, and South-Asians’. Additionally, 8563 variants were uniquely identified in the Egyptian cohort, from those, two were predicted to cause structural damage, namely, CDKAL1: 6_21065070 (A > T) and PPARG: 3_12351660 (C > T) utilizing the Missense3D database. The former is a protein coding gene associated with Type 2 DM while the latter is a key regulator of adipocyte differentiation and glucose homeostasis. Both variants were detected heterozygous in two different Egyptian individuals from overall 110 sample. This analysis sheds light on the unique genetic traits of the Egyptian population that play a role in the DM high prevalence in Egypt. The proposed analysis pipeline -available through GitHub- could be used to conduct similar analysis for other diseases across populations

Identification of Differentially Expressed Genes in Human Colorectal Cancer Using RNASeq Data Validated on the Molecular Level with Real-Time PCR

Colorectal cancer (CRC) is a prevalent cancer with high morbidity and mortality rates worldwide. Late diagnosis is a significant contributor to low survival rates in a minority of cases. The study aimed to perform a robust pipeline using integrated bioinformatics tools that will enable us to identify potential diagnostic and prognostic biomarkers for early detection of CRC by exploring differentially expressed genes (DEGs). In addition to, testing the capability of replacing chemotherapy with plant extract in CRC treatment by validating it using real-time PCR. RNA-seq data from cancerous and adjacent normal tissues were pre-processed and analyzed using various tools such as FastQC, Kallisto, DESeq@ R package, g:Profiler, GNEMANIA-CytoScape and CytoHubba, resulting in the identification of 1641 DEGs enriched in various signaling routes. MMP7, TCF21, and VEGFD were found to be promising diagnostic biomarkers for CRC. An in vitro experiment was conducted to examine the potential anticancer properties of 5-fluorouracile, Withania somnifera extract, and their combination. The extract was found to exhibit a positive trend in gene expression and potential therapeutic value by targeting the three genes; however, further trials are required to regulate the methylation promoter. Molecular docking tests supported the findings by revealing a stable ligand-receptor complex. In conclusion, the study’s analysis workflow is precise and robust in identifying DEGs in CRC that may serve as biomarkers for diagnosis and treatment. Additionally, the identified DEGs can be used in future research with larger sample sizes to analyze CRC survival

Genomic landscape of hepatocellular carcinoma in Egyptian patients by whole exome sequencing

Background: Hepatocellular carcinoma (HCC) is the most common primary liver cancer. Chronic hepatitis and liver cirrhosis lead to accumulation of genetic alterations driving HCC pathogenesis. This study is designed to explore genomic landscape of HCC in Egyptian patients by whole exome sequencing. Methods: Whole exome sequencing using Ion Torrent was done on 13 HCC patients, who underwent surgical intervention (7 patients underwent living donor liver transplantation (LDLT) and 6 patients had surgical resection}. Results: Mutational signature was mostly S1, S5, S6, and S12 in HCC. Analysis of highly mutated genes in both HCC and Non-HCC revealed the presence of highly mutated genes in HCC (AHNAK2, MUC6, MUC16, TTN, ZNF17, FLG, MUC12, OBSCN, PDE4DIP, MUC5b, and HYDIN). Among the 26 significantly mutated HCC genes—identified across 10 genome sequencing studies—in addition to TCGA, APOB and RP1L1 showed the highest number of mutations in both HCC and Non-HCC tissues. Tier 1, Tier 2 variants in TCGA SMGs in HCC and Non-HCC (TP53, PIK3CA, CDKN2A, and BAP1). Cancer Genome Landscape analysis revealed Tier 1 and Tier 2 variants in HCC (MSH2) and in Non-HCC (KMT2D and ATM). For KEGG analysis, the significantly annotated clusters in HCC were Notch signaling, Wnt signaling, PI3K-AKT pathway, Hippo signaling, Apelin signaling, Hedgehog (Hh) signaling, and MAPK signaling, in addition to ECM-receptor interaction, focal adhesion, and calcium signaling. Tier 1 and Tier 2 variants KIT, KMT2D, NOTCH1, KMT2C, PIK3CA, KIT, SMARCA4, ATM, PTEN, MSH2, and PTCH1 were low frequency variants in both HCC and Non-HCC. Conclusion: Our results are in accordance with previous studies in HCC regarding highly mutated genes, TCGA and specifically enriched pathways in HCC. Analysis for clinical interpretation of variants revealed the presence of Tier 1 and Tier 2 variants that represent potential clinically actionable targets. The use of sequencing techniques to detect structural variants and novel techniques as single cell sequencing together with multiomics transcriptomics, metagenomics will integrate the molecular pathogenesis of HCC in Egyptian patients

A simple statistical test of taxonomic or functional homogeneity using replicated microbiome sequencing samples

One important question in microbiome analysis is how to assess the homogeneity of the microbial composition in a given environment, with respect to a given analysis method. Do different microbial samples taken from the same environment follow the same taxonomic distribution of organisms, or the same distribution of functions? Here we provide a non-parametric statistical “triangulation test” to address this type of question. The test requires that multiple replicates are available for each of the biological samples, and it is based on three-way computational comparisons of samples. To illustrate the application of the test, we collected three biological samples taken from different locations in one piece of human stool, each represented by three replicates, and analyzed them using MEGAN. (Despite its name, the triangulation test does not require that the number of biological samples or replicates be three.) The triangulation test rejects the null hypothesis that the three biological samples exhibit the same distribution of taxa or function (error probability ≤0.05), indicating that the microbial composition of the investigated human stool is not homogenous on a macroscopic scale, suggesting that pooling material from multiple locations is a reasonable practice. We provide an implementation of the test in our open source program MEGAN Community Edition

The use of RNA-seq data for re-annotation of transcriptomes

Recently, demands for whole genome sequencing have been greatly increased for many applications, including the study of SNPs and their role in phenotypic diversity in nature. However, whole genome sequencing using high throughput sequencing methods remains an expensive task, only suitable to large consortium of researchers funded by strong agencies. As an alternative, RNA-seq seems to be an appropriate alternative for many reasons. First, while genome sizes can differ by as much as 5 orders of magnitude, transcriptome sizes differ by less than 2 orders of magnitude even between yeast and polyploid plants. Second, coding sequences are more conserved and have less repetitive elements than non-coding sequences. Finally, RNAseq allows not only the identification of coding polymorphism but also characterization of expression differences, both of which have been shown to underlie phenotypic diversity. In this study, we have developed a pipeline for annotating transcriptomes for species without an available direct reference genome, based mainly on RNA-seq data and a closely related reference genome. Benchmarking studies were performed to decide software components of the pipeline. Among three programs; AUGUTSUS gene prediction tool incorporated with RNA-seq data, Cufflinks transcriptome reconstruction tool and Trinity denovo transcriptome assembler, AUGUSTUS proved to be the most accurate software in terms of sensitivity and specificity. We have used published gene models of Col accession of Arabidopsis thaliana as a reference annotation and compared it with the software generated gene models. The performance of the pipeline in the absence of an available direct reference genome was tested. In such case, a pseudo-reference genome was constructed by incorporating accession-specific SNPs into the closest reference genome. RNA-seq reads were mapped against both the published A. thaliana (Ler accession) and the Ler pseudo-reference genome, which is constructed by incorporating Ler SNPs into Col accession of A. thaliana. The two gene models gave highly similar results when compared with the published Ler gene models. Finally, the pipeline was applied on four different tomato species; S. lycopersicum var. m82, S. pennellii, S. pimpinellifolium and S. habrochaites. Among the four species, only S. lycopersicum var. m82 has a reference genome of S. lycopersicum var. Heinz from which we have constructed pseudoreference genomes for the four species using the available RNA-seq data. AUGUSTUS with RNA-seq guidance was applied to predict genes models from the four constructed pseudoreference genomes. In order to monitor the effect of incorporating species-specific SNPs on annotation, we compared each of the four generated annotations with the published ITAG S. Iycopersicum var. Heinz annotation. Results showed variation in the values of sensitivity and specificity between pairs of compared gene models. We illustrated that evolutionary distances between the four tomato species and the values of sensitivity and specificity are inversely correlated with each others

Implementation of a Life Cycle Cost Deep Learning Prediction Model Based on Building Structure Alternatives for Industrial Buildings

Undoubtedly, most industrial buildings have a huge Life Cycle Cost (LCC) throughout their lifespan, and most of these costs occur in structural operation and maintenance costs, environmental impact costs, etc. Hence, it is necessary to think about a fast way to determine the LCC values. Therefore, this article presents an LCC deep learning prediction model to assess structural and envelope-type alternatives for industrial building, and to make a decision for the most suitable structure. The input and output criteria of the prediction model were collected from previous studies. The deep learning network model was developed using a Deep Belief Network (DBN) with Restricted Boltzmann Machine (RBM) hidden layers. Seven investigation cases were studied to validate the prediction model of a 312-item dataset over a period of 30 years, after the training phase of the network to take the suitable hidden layers of the RBM and hidden neurons in each hidden layer that achieved the minimal errors of the model. Another case was studied in the model to compare design structure alternatives, consisting of three main structure frames—a reinforced concrete frame, a precast/pre-stressed concrete frame, and a steel frame—over their life cycle, and make a decision. Precast/pre-stressed concrete frames were the best decision until the end of the life cycle cost, as it is possible to reuse the removed sections in a new industrial building

Implementation of a Life Cycle Cost Deep Learning Prediction Model Based on Building Structure Alternatives for Industrial Buildings

Undoubtedly, most industrial buildings have a huge Life Cycle Cost (LCC) throughout their lifespan, and most of these costs occur in structural operation and maintenance costs, environmental impact costs, etc. Hence, it is necessary to think about a fast way to determine the LCC values. Therefore, this article presents an LCC deep learning prediction model to assess structural and envelope-type alternatives for industrial building, and to make a decision for the most suitable structure. The input and output criteria of the prediction model were collected from previous studies. The deep learning network model was developed using a Deep Belief Network (DBN) with Restricted Boltzmann Machine (RBM) hidden layers. Seven investigation cases were studied to validate the prediction model of a 312-item dataset over a period of 30 years, after the training phase of the network to take the suitable hidden layers of the RBM and hidden neurons in each hidden layer that achieved the minimal errors of the model. Another case was studied in the model to compare design structure alternatives, consisting of three main structure frames—a reinforced concrete frame, a precast/pre-stressed concrete frame, and a steel frame—over their life cycle, and make a decision. Precast/pre-stressed concrete frames were the best decision until the end of the life cycle cost, as it is possible to reuse the removed sections in a new industrial building

Performance Analysis and Estimation of Call Admission Control Parameters in Wireless Integrated Voice and Data Networks

We propose an admission control policy for wireless multimedia networks that is based on the well known threshold-based guard channel method. The new scheme deals with two different types of traffic classes; namely: voice and data. We assume two different thresholds, one for each traffic class. In addition, we propose to buffer the handoff data calls if no free channels are available rather than rejecting them. For handoff voice calls, we propose two methods, namely: a blocking method and a preemptive method. For the blocking method, we reject the handoff voice calls if no channels are available. For the preemptive method, an ongoing data call can be buffered and its channel allocated to the handoff voice call. We study the effect of the thresholds, buffer size and the application of the proposed methods on call blocking probabilities. It is shown that the new call blocking probabilities are only affected by the threshold values. Meanwhile, the data handoff blocking probability exhibited great improvement. For handoff voice calls, when the blocking method is applied, the blocking probability value increases slightly with the increase of buffer size. Meanwhile, for the preemptive method, the handoff voice call blocking probability significantly decreases as the buffer size increases. Based on these results, we develop an algorithm that uses the proposed policy to estimate the appropriate thresholds and buffer size which meet the required call blocking probabilities for each traffic type.