KAUST Research Conference
Advances in Metagenomics & its Applications
May 23 - 25, 2022 Building 19, Level 3
Session 1 will be chaired by Distinguished Professor Takashi Gojobori. This session will feature four talks related to computational tools and resources in the field of Metagenomics. Session starts at 8:45 am with Nikos Krydes talk.
Abstract: Microbiome research is rapidly transitioning into Data Science. The unprecedented volume of microbiome data being generated pose significant challenges with respect to standards and management strategies, but also bear great new opportunities that can fuel discovery. Computational analysis of microbiome samples involving previously uncultured organisms, is currently advancing our understanding of the structure and function of entire microbial communities and expanding our knowledge of genetic and functional diversity of individual micro-organisms. I will describe some of our computational approaches and will emphasize the value of data processing integration in enabling the exploration of large metagenomic datasets and the discovery of novelty. I will present current approaches and will discuss a few science vignettes in the exploration of microbial, viral, and functional diversity.
Bio: Dr. Kyrpides joined the DOE Joint Genome Institute in 2004 to lead the Genome Biology Program and the development of the comparative analysis platforms for microbial genomes and metagenomes (IMG). He became the Metagenomics Program head in 2010 and has the combined Microbial Genomes and Metagenomes Program since 2011. Prior to joining the DOE Joint Genome Institute, Dr. Kyrpides led the development of the genome analysis and Bioinformatics core at Integrated Genomics Inc. in Chicago, IL. He did his postdoctoral studies with Carl Woese at the University of Illinois at Urbana-Champaign and at Argonne National Laboratory. Dr. Kyrpides’ research focus on Microbiome Research with an emphasis on Microbiome Data Science. His group is developing novel methods for enabling large-scale comparative analysis, as well as mining and visualization of big data.
Abstract: Shotgun metagenomic sequencing allows us to directly observe the functional genes present in a microbial community. Although the cost of metagenomic sequencing has declined dramatically in recent years, it is still impractical to apply shotgun metagenomics to routine studies involving hundreds to thousands of samples. Metagenomic analysis is also limited by sequencing depth, with genes associated with rare members of the community easily missed. Because microbial functions are often correlated with taxonomy it is possible to supplement metagenomic studies with functional predictions based on taxonomic marker genes such as the 16S rRNA gene. In 2015 we introduced paprica, a tool to analyze microbial community structure and predict the associated metabolic potential. Based on the completed genomes available in the RefSeq database, paprica represents a model of gene distribution in a phylogenetic context. Phylogenetic placement techniques allow us to place amplicon sequence reads on a high-quality reference tree constructed from completed genomes and assign genes and genomic characteristics (e.g., GC content, genome length, 16S rRNA gene copy number). Continued development of paprica has improved the fidelity of these predictions and includes the use of multi-gene alignments for the reference tree and nested reference trees to reduce the number of taxa in each tree. Here I’ll describe the paprica pipeline in greater detail, including extensions for the annotation of metagenomic and metatranscriptomic data. I’ll present new results comparing paprica predictions to completed genomes not contained in RefSeq, and case studies illustrating how we use paprica to improve our ecological interpretations.
Bio: Jeff Bowman is a biological oceanographer and microbial ecologist at Scripps Institution of Oceanography at UC San Diego. His lab is broadly interested in the links between microbial community structure and function and biogeochemical cycles. Current focus areas include mangrove-microbe symbiosis, coastal time-series analysis, astrobiology, and high latitude ecosystem processes.
Abstract: Shotgun metagenomics has uncovered a substantial amount of diversity in the human microbiome, but a large fraction of the sequences in a metagenome remains uncharacterized. In my talk I will show how new computational methods can (i) improve the resolution of metagenomic analysis to profile single microbial strains, (ii) uncover the still hidden diversity of the human microbiome, (iii) perform microbial transmission inference, and (iv) extend the analysis to tens of thousands of metagenomics. I will also discuss some biomedical applications for these analyses including oral, infectious, and non-communicable diseases.
Bio: Nicola Segata, Ph.D., is Professor and Principal Investigator in the CIBIO Department at the University of Trento (Italy) and Principal Investigator at the European Institute of Oncology in Milan (Italy). His lab (http://segatalab.cibio.unitn.it/) comprises more than 20 researchers and employs experimental metagenomic tools and novel computational approaches to study the diversity of the microbiome across conditions and populations and its role in human diseases. The projects in the lab bring together computer scientists, microbiologists, statisticians, and clinicians and are generally focused on profiling microbiomes with strain-level resolution and on the meta-analysis of very large sets of metagenomes with novel computational tools.
Lunch will be served in Building 19, Hall 2
Session 2 will be chaired by Professor Alexandre Rosado. This session will feature five talks related to Metagenomics and Infectious diseases. Session starts at 1:30pm with Peiying Hongs's talk.
Abstract: Wastewater contains a myriad of different biological contaminants, including bacteria, viruses and various types of mobile genetic elements which can encode for antibiotic resistance genes. With the increasing awareness of the One Health concept, surveying our shared environment, including the wastewater, for antimicrobial resistance threats would be useful to devise appropriate intervention measures. In this presentation, I illustrate how the field of environmental science and engineering can make use of metagenomics to elucidate the presence of emerging microbial contaminants in wastewater and to assess the effect of wastewater treatment processes on these microbial contaminants.
Bio: Dr. Peiying Hong is an Associate Professor in Division of Biological and Environmental Science and Engineering, KAUST. She obtained her Ph.D. degree in Environmental Science and Engineering from National University of Singapore, and her postdoctoral training in University of Illinois at Urbana-Champaign. At KAUST, her Environmental Microbial Safety and Biotechnology Lab research group seeks to provide the fundamental science and goal-oriented research underpinning improvements in water health and water management globally. The group identifies critical knowledge gaps and exploit new approaches to deliver novel insights that advance water reuse programs safely and sustainably. The goal of Dr Hong’s research is to continue to push for the reuse of high-quality treated wastewater in water-scarce countries like Saudi Arabia and in a global environment that is increasingly impacted by climate change.
Abstract: Metagenomics is the comprehensive genomic study of the collective microbial communities retrieved directly from diverse sample types with the purpose of understanding their genetic diversity, population structure, and ecological importance. Over the last two decades, clinical metagenomics has emerged as a new frontier powerful tool that is rapidly transferring from research to clinical laboratories. This emerging approach is widely applied in the field of microbiological diagnostics for etiological diagnosis, antimicrobial resistance, transcriptome, and the microbiome analysis. Here we are presenting national local data highlighting the importance of metagenomic analysis in health/disease and health care environments providing insight into their microbial communities. Thus, clinical microbiologists, infectious disease experts, and epidemiologists can benefit from such studies in the field of medicine by understanding host and microbes interactions and exploring new approaches in managing diseases.
Abstract: Middle East respiratory syndrome coronavirus (MERS-CoV) is a zoonotic infection that emerged in the Middle East in 2012. Symptoms range from mild to severe and include both respiratory and gastrointestinal illnesses. The virus is mainly present in camel populations with occasional zoonotic spill over into humans. The severity of infection in humans is inﬂuenced by numerous factors, and similar to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), underlying health complications can play a major role. Currently, MERS-CoV and SARS-CoV-2 are coincident in the Middle East and thus a rapid way of sequencing MERS-CoV to derive genotype information for molecular epidemiology is needed. Additionally, complicating factors in MERS-CoV infections are coinfections that require clinical management. The ability to rapidly characterize these infections would be advantageous. To rapidly sequence MERS-CoV, an amplicon-based approach was developed and coupled to Oxford Nanopore long read length sequencing. This and a metagenomic approach were evaluated with clinical samples from patients with MERS. The data illustrated that whole-genome or near-whole-genome information on MERS-CoV could be rapidly obtained. This approach provided data on both consensus genomes and the presence of minor variants, including deletion mutants. The metagenomic analysis provided information of the background microbiome. The advantage of this approach is that insertions and deletions can be identiﬁed, which are the major drivers of genotype change in coronaviruses.
Bio: Dr. Al-Jabr is a Consultant Clinical Research-Molecular Medical Virologist and the Chairperson of the Biorepository Department at the Research Center- King Fahad Medical City, Riyadh. He has an Honorary Research Fellow with the Institute of Infection & Global Health, University of Liverpool, UK. He earned his BSc from the College of Applied Medical Sciences, King Saud University in 2002. In 2009 he went to complete his Master’s degree in Medical Microbiology, Biomedical Sciences from the University of Ulster, UK. In 2016 he was awarded his PhD in Virology from the University of Liverpool, UK. His work focuses on respiratory and emerging viruses; SARS-CoV-2, MERS-CoV, HRSV, using OMICS approaches; metagenomics, transcriptomics, proteomics, and Oxford Nanopore sequence. He runs a research group composed of 1 PDRA, 2 Master students, co-supervised 4 PhD students. Dr. Al-Jabr has good experience in Clinical Virology since he served as Lab Specialist at King Khalid University Hospital, Riyadh for over 9 years, and for one year as a Consultant Clinical Lab Scientist at KFMC. Dr. Al-Jabr is working at the cutting edge of scientific and clinical research. And he has established beneficial research network relationships with many research groups at the national and international levels. He has research funding from the US-FDA to work on SARS-CoV-2 and MERS-CoV in human and animal models and medical countermeasures.
Abstract: Conventional diagnostic techniques used in a clinical microbiology laboratory include culturing of microorganisms, pathogen-specific antibodies/antigen detection and applications of various techniques for the detection of target nucleic acids. Thousands of microorganisms are known to cause human diseases, but only a fraction are readily identifiable using traditional microbiology-based diagnostic methods and the success of identification also depends on a priory knowledge of the possible aetiology. Most clinically relevant pathogens grow within 24 to 48 h in vitro, but several species require a much longer time and some can not be cultured in vitro and thus often avoid timely detection. Rapid and accurate identification of the etiological agents and prediction of other clinically relevant phenotypes such as antimicrobial resistance is of paramount importance for determining the clinical course of treatment. Clinical metagenomics provides the “one for all” approach, which can characterize all DNA or RNA molecules present in a sample, enabling an analysis of the entire meta-organism that aid in precise diagnosis and clinical decision making. Our aim is to make KAUST a major hub for providing training to healthcare professionals in conducting research on applications of Clinical Metagenomics in healthcare settings within KSA under the KAUST Smart Health Initiative (KSHI). We will present a few selected case studies on the applications of Clinical Metagenomics involving KAUST and Infectious Disease Physicians at the partner hospitals in Saudi Arabia.
Bio: Prof. Pain received his PhD from the University of Cambridge, U.K., working on microbial pathogenesis in a bacterial pathogen of cultivated mushrooms. He had his postdoctoral training at the Weatherall Institute of Molecular Medicine (WIMM) in Oxford, U.K. where he focused on cell biology and virulence-associated phenotypes in the human malaria parasite Plasmodium falciparum. After spending five years in Oxford, he moved back to Cambridge and joined the Pathogen Sequencing Unit (PSU) at the Wellcome Trust Sanger Institute (WTSI) as a Senior Scientist. In WTSI, he took a leading role in coordinating several genome and transcriptome projects of several major species of malaria and other related apicomplexan parasites of humans and animals. In 2010, he moved to Saudi Arabia to join the Biological and Environmental Sciences and Engineering (BESE) Division at King Abdullah University of Science and Technology (KAUST) as a member of the founding faculty to establish and lead a research program on Pathogen Genomics – primarily focusing on pathogens of regional and global relevance. Since 2015, he has had an appointment as a Distinguished Professor at the international Institute for Zoonosis Control, Hokkaido University in Sapporo, Japan. Currently, he also serves as an Honorary Professor at the Liverpool School of Tropical Medicine (LSTM) in the U.K.
Prof. Pain’s research group uses a combination of high-throughput sequencing, comparative and functional genomics protocols and bioinformatic analysis tools to study pathogenicity determinants in selected parasitic protists, bacteria and more recently in SARS-CoV-2.
Session 3 will be chaired by Distinguished Professor Carlos Duarte. This session will feature five talks related to Marine and Environmental Metagenomics. Session starts at 8:30am with Keynote speaker Jill Banfield's talk.
Abstract: Insect symbionts can be classified into obligate and secondary facultative symbionts. The primary symbionts are nutritional symbionts which reside within a specialized organ, the bacteriome. These primary symbionts (like Wigglesworthia, Buchnera etc) produce important nutrients, mainly amino acids and vitamins, to their diet-dependent hosts such as aphids, tsetse flies, ants, stinkbugs, weevils, psyllids, lice and sharpshooters. The secondary facultative symbionts can be detected in diverse cell types and organs. These symbionts can be beneficial to their hosts by providing protection against heat, parasitoids, viruses and insecticide resistance. Gut-associated bacteria are members of this category and recent studies increasingly suggest their importance in several aspects of insect host biology and physiology including nutrition, immunity and behavior. The insect gut microbiota can also engage in opportunistically harmful interactions with the host. During the last years, and with the advent of molecular biology and next generation sequencing techniques, research on symbiosis has been rekindled with an emphasis on untangling the diversity and the functional role symbionts have on all aspects of host biology.
Bio: Assoc. Prof. George Tsiamis teaches Microbiology, Molecular Biology, and Biotechnology at the University of Patras in the Department of Environmental Engineering. He has a 17-year of research experience in environmental microbiology. He has more than 100 peer-reviewed publications in international journals, with an h-index of 27, and more than 4,000 citations. He has participated in several FP6, FP7, Horizon EU projects and national projects. He is the Head of the Lab of Systems Microbiology and Applied Genomics.
Abstract: The largest number of deep anoxic brine pools known so far on Earth are in the Red Sea. Such brine pools are very saline water bodies originating from the dissolution of ancient evaporites and laying at the bottom of the sea. They do not mix with the overlaying water column because of the high density of the brines. The conditions of these deep lakes are anoxic, very saline, with a relatively high hydrostatic pressure and in some cases very hot, far above the already high temperature of the Red Sea. Under such conditions and due to the physical separation from the normal seawater column, unique microbial assemblages have been selected over time. The most productive compartment of the brine pools is the transition zone between the normal seawater column and the brine, because gradients of redox couples exist that feed specialized microbial taxa finely stratified across thin seawater-brine interfaces. In this talk the unique features of such microbial assemblages are discussed with reference to the stratification of the microorganisms within the water column of the lakes. The data indicate that the deep anoxic brine pools in the Red Sea are home of unique microbial types specifically adapted to such polyextreme environments.
Abstract: Plant associated microbial communities play key roles in biotic and abiotic stress tolerance as well as nutrient acquisition. The rhizosphere (the nearest soil area to the roots) hosts a rich microbial plant community which provides a pivotal series of beneficial outcomes related to plant growth. Plant roots recruit their rhizosphere microbiome from bulk soil and a small number of the microbes from the rhizosphere enter the plant colonising the root endosphere and some then move to other plant organs. The phylogenetic conservation of rhizosphere microbiomes infers an organized assembly of microbiomes which is directed by mechanisms which are at large unknown. These most likely involve cell-cell interactions amongst microbes, plant-microbe signalling and root exudate effects. Microbial cell-cell communication is a way to dynamically regulate a variety of metabolic and physiological activities in response to the host, environment and microbial neighbors. Plant microbiomes contain a very large number of diverse bacterially produced molecules such as quorum sensing signals, volatiles and secondary metabolites which can play cell-cell signaling roles amongst members of the microbiome. Our present understanding of the numerous different signal molecules which are produced in a microbial community, on how the many different bacteria signal each other and what functions are regulated, is very much in its infancy. Understanding the chemical languages shape the plant microbiome will be very informative on how these communities contribute to plant health and physiology. And will also lead to the development of prebiotic compounds as well as microbial probiotic competence for a more sustainable agriculture of economically important crops.
Bio: Vittorio Venturi (graduated from Edinburgh University, UK in 1988, and received his Ph.D. degree in Microbiology from the University of Utrecht, The Netherlands in 1994. During his PhD research he focused in the regulation of iron-transport processes of beneficial plant associated bacteria which promote plant growth; the monopolization of iron nearby plant roots is an important trait which keeps microbial pathogens away. He then moved as a postdoctoral fellow to the International Centre for Genetic Engineering & Biotechnology (ICGEB), Trieste, Italy, where he started investigating intercellular signaling among bacteria. He then went on to become Group Leader at ICGEB in 1998 continuing his studies on intercellular signaling. He is now particularly interested in (i) how plant associated bacteria undergo interspecies communication and interkingdom signaling with plants and (ii) plant microbiomes and the development of microbial products for a more sustainable agriculture. Since October 2019 he is also acting as the Scientific Coordinator of ICGEB. He has published over 150 articles in peer-reviewed international journals, supervised 17 PhD students and over 10 postdocs.
Poster competition will take place in Building 19, Hall 2 & 3. Lunch will also be served.
Session 3 will be chaired by Professor Daniele Daffonchio. This session will feature five talks related to Metagenomics and Biotechnology. Session starts at 2:00pm with Manuel Ferrer's talk.
Abstract: Our understanding of enzymes has demonstrated that they help circular economy and keeping climate change issues from rising. Biotechnologically speaking, the solution is simple: to obtain an enzyme that can be added directly to, or at one of the stages of the production process of, products to make them more sustainable and environmentally friendly. However, the challenges of replacing chemical counterparts with enzymes are manifold, and constant innovation is demanded. In his presentation, M. Ferrer will briefly describe innovations in metagenomics, a technology that provide a bridge between biodiversity, circular economy and climate change mitigation. Specific examples on how metagenomics, with the help of high-throughput robotic screening platforms, supercomputers, bioinformatics, computational, accurate protein structure prediction and engineering tools, will help access and generate enzymatic diversity, will be presented.
Bio: Dr. Manuel Ferrer is affiliated to Department of Biochemistry, Consejo Superior de Investigaciones Científicas, where Dr. Manuel Ferrer is currently working as Assistant Professor . Dr. Manuel Ferrer has authored and co-authored several national and international publications and also working as a reviewer for reputed professional journals. Dr. Manuel Ferrer is having an active association with different societies and academies around the world. Dr. Manuel Ferrer made his mark in the scientific community with the contributions and widely recognition from honorable subject experts around the world. Dr. Manuel Ferrer has received several awards for the contributions to the scientific community. Dr. Manuel Ferrer major research interest involves Biochemistry. His research interest is in Biochemistry.
Abstract: The applications of biotechnology have gained popularity in the petroleum industry in recent times as an excellent alternative approach to some of the conventional oilfield operations due to its environment friendly impact and sustainability. The approaches mainly depend on the use of micro-biological systems, living organisms or parts of it to develop or create group of essential products for the petroleum industry. For example, fracturing and drilling fluid breakers, bio- surfactants, and Bio-mineralization for different field applications. In addition, the recent advancements in molecular technology have made high throughput sequencing more accessible and affordable. The petroleum industry is studying renewable and clean sources of energy like the biofuels, genetically modified organisms capable of producing bio-polymers and other chemicals, cost-effective chemical bio-processes as an alternative to the traditional routes. This has revolutionized our understanding of microbial diversity within oilfield samples. This paper will give a comprehensive overview of where biotechnology stand on petroleum industry and highlights the potential biotechnological solutions for different challenges in this industry, and the opportunities offered by microbes as positive use or as bio-catalyst that increase the revenues of oil and gas industry operation, and bacteria that enhanced oil recovery (MEOR). Or, bacteria that has possibilities of encouraging competitive microbes to grow, to suppress the activities of troublesome bacteria to control the microbial souring.
Bio: Dr. Abdulmohsen holds a PhD with specialty in Petroleum applied Biotechnology and he has served Saudi Aramco for the last 27 years. Abdulmohsen is leading the biotechnology program with in the Saudi Aramco EXPEC Advance Research Center. In his career, Abdulmohsen has published a total of 55 papers as 1st author, and his research work has resulted in developing 15 granted patents. In addition, he has led international and national academic collaborations and led several international joint technology projects (JIP) to develop new technologies. He supervised and was selected to be an external examiner for national and international universities for PhD and MSc students. His research interests include mitigation of microbial influence corrosion, biofilm development and control, Biocide development and screening, Drilling mud microbial contamination and mitigation, reservoir souring and plugging.
Abstract: Microbiomes are essential for the health and performance of higher organisms (hosts) and metagenomics has helped to unravel the interactions between them. Here I will present new approaches to predict functionality of microbiomes using 16S rRNA gene amplicon data and improve the information content of metagenome-assembled genomes (MAGs). I will also show how MAGs can be used to model metabolic interactions within microbiomes and gain new insight into the input and outputs of host-associated systems.
Bio: Professor Torsten Thomas obtained his PhD from the University of New South Wales, Sydney, Australia, where he also established his academic career and led research centers. His research has focused on understanding the central role that microbiomes have in influencing the performance and health of higher host organisms, in particular in the marine environment. For this he has generated and analyzed large data sets and has developed several innovative approaches, including -omic technologies, bioinformatics pipelines and ecological theories.
Dinner will be served at Al Marsa restaurant by invitation only.
Session 5 will be chaired by Professor Peiying Hong. This session will feature five talks related to Metagenomics in Oil & Water Industry. Session starts at 8:30am with a talk by speaker Abdulwahab A. Al-Ghamdi, Director, KAUST Upstream Research Center, Saudi Aramco
Abstract: Biotechnology has immense potential in resolving upstream oil & gas challenges while maintaining a circular carbon economy as it is directed towards generating a wide range of bio-based chemicals that are environmentally compatible and function effectively under harsh reservoir conditions. Such bio-products could be better alternatives than several costly, and environmentally hazardous synthetic chemicals currently used in oilfield operations. Our objective is to leverage biotechnology approaches to generate bio-based materials from different biological resources such as reservoir extremophiles and microalgae for oilfield operations. These bio-products offer better alternatives for synthetic oilfield chemicals in terms of economic and environmental sustainability, besides high efficiency and stability under extreme reservoir conditions. Thus, metagenomics study can help demonstrating the metabolic capabilities of the reservoir microbial consortium, which provides better insights on the promising metabolic capabilities of this consortium and potential green chemicals for upstream applications. A comprehensive Omics analysis via AI and ML capabilities can significantly help in identifying the biological products that can be produced by these biocatalysts and explore their promising potential in the Oil and Gas industry.
Bio: Dr. Abdulwahab A. Al-Ghamdi started his career with Saudi Aramco in 2001 where he completed several assignments in different organizations within EXPEC ARC include Advanced Technical Services Division (ATSD), Production Technology Division (PTD), and Strategic Technology Analysis Division (STAD) as well as Gas Reservoir Management Division (GRMD). He is currently heading KAUST Upstream Research Center. Al-Ghamdi has a Bachelor’s Chemical Engineering degree from KFUPM with honors. A M.Sc. and PhD degree in Petroleum Engineering from Texas A&M University. A M.Sc. degree in Industrial Engineering from Texas A&M University, with a graduate certificate in business from Mays business school at Texas A&M University. Abdulwahab published several technical and journal papers, and led the successful implementation of many analytical tools and business solution to manage organizational performance and strategy framework. Abdulwahab received a number of international honors and recognitions including the “2011 SPE Young Professional Paper Contest” in Denver for the best paper written by a Young Professional in the Well Stimulation discipline, the regional “2012 Oil & Gas awards for Young Engineer of the year”, recipient of the “2012 SPE Regional Outstanding Young Member Award”, and lead his team to win the SPE prestigious International award “The Outstanding Section Young Professionals Committee Award”, San Antonio 2012.
Abstract: Corrosion in pipelines and reservoir tanks in oil plants is a serious problem in the energy industries around the world because it causes a huge economic loss due to not only frequent replacements of the parts of pipelines and tanks but also potential damage of the entire fields of crude oil. Current studies have revealed that corrosions are generated by microbial activities and they are now called as Microbial influences Corrosion (MIC) or simply bio-corrosion. Bacterial species actually causing bio-corrosion is crucial information for the suppression of the corrosion.
To diagnose and give proper treatment to pipelines in industrial plants, it is essential to identify the bacterial species responsible for bio-corrosions. For this aim, I conducted an analysis of the microbial community at the corrosion sites in pipelines of oil plants, using the comparative metagenomic analysis along with bioinformatics and statistics.
Identifying the bacterial species responsible to bio-corrosion, this study provides us with information on bacterial indicators that will be available to classify and diagnose bio-corrosions. Furthermore, these species may be available as biomarkers to detect the events of bio-corrosion at an early stage. Then, any appropriate care such as the appropriate choice of biocides can be taken immediately and appropriately. Thus, my study will provide a platform for obtaining microbial information related to bio-corrosion that enables us to obtain a practical approach to protect them from bio-corrosion.
Bio: Budoor Ali Nasser is a lab scientist in R&DC at Saudi Aramco since 2010 . She got her bachelor degree in chemistry from Dammam university 2007. After her graduation she worked as a chemistry teacher in high school then move to Bahrain complete her master's degree in environmental biotechnology ( 2009 ) . In 2019 she completes her PhD in Environmental Science and Engineering at KAUST, after that she resume working in R&DC on different research project . Her main interest in petroleum biotechnology ( MIC microbial influences corrosion ,MEOR microbial enhance oil recovering, bio-surfactant, bio-desulfurization, etc.) . She conducted the first study that use metagenomic tools to analyze the microbial community at the corrosion site in pipelines in Saudi Aramco for (water, oil) pipeline of oil plant by using the comparative metagenomic analysis along with bioinformatics and statistics.
She participated in many Volunteering project in local schools to make kid-friendly science experiment with the student. (2008 – present) and Lead many student groups that participate in different competitions.
Abstract: Microbial EcoGenomics and Biotechnology Laboratory, Biological and Environmental Science and Engineering Division (BESE), King Abdullah University of Science and Technology (KAUST), Saudi Arabia.
Cold or hot deserts and volcanic environments are part of the essence of Saudi Arabia and Antarctica. The microbial community that thrives under extreme conditions is underexplored and its potential for application in biotechnology remains hidden. The push towards greener chemistry and less impactful production in industrial processes requires enzymes that can withstand harsh industrial conditions, for example high or low temperatures, pH and pressure. Regarding Saudi extreme soils, for example, there is a huge potential for bioprospecting the extreme Red Sea mangroves, Al Ula desert soil and Al Wahbah volcanic Crater, one of the volcanic sites in Saudi Arabia with highly saline soils (Sodium-23-59 g/kg).On the other hand, Antarctica is also a mosaic of extremes that contains cold deserts and active polar volcanoes, such as Deception Island, a marine stratovolcano with remarkable temperature gradients over very short distances, with temperatures approaching 100°C of fumaroles and sub-zero temperatures observed in glaciers. Here I will present the utilization of metagenomics and cultivation-based framework to describe diversity and function as well biotechnological potential of non-culturable microbes from these two very different extreme sites. Altogether, our data provides insights into the metabolic potential of microbes from Saudi Arabia and Antarctica's extreme environments with great potential for oil industry and other biotechnological applications.
Bio: Professor of Bioscience at KAUST. Former Full Professor at Federal University of Rio de Janeiro (UFRJ) and Visiting Professor at the Department of Land, Air and Water Resources, University of California - Davis, USA. Director of the Institute of Microbiology (2010-2014) and Vice-President of the Brazilian Society of Microbiology (2011-2014); holds a BSc in Biological Sciences, MSc (Microbiology), PhD in Microbiology from UFRJ and Wageningen University & Research (WUR), The Netherlands. Associate editor: Journal of Microbiological Methods, Frontiers in Microbiology (Review editor). BMC Microbiology, Brazilian Journal of Microbiology (BJM) and International Journal of Biodiversity. His background is on environmental microbiology, with focus on molecular microbial ecology, microbiome sciences and biotechnology.
Abstract: Monitoring the changes that occur to water during distribution is vital to ensure water safety. The biological stability of reverse osmosis (RO) produced drinking water, characterized by low bacterial cell concentration and low assimilable organic carbon, in combination with chlorine disinfection is underexplored. Monitoring microbial quality and ensuring membrane-treated water safety has taken advantage of the rapid development of DNA-based techniques. This study addresses the use of DNA-based methods for low biomass samples typical in chlorinated DWDSs. The bacterial community analysis using 16S ribosomal RNA (rRNA) gene sequencing was performed and both water and biofilm samples (before/after chlorination) were compared. Results from this study highlight the importance of implementing multiple barriers to ensure water safety. Changes in water quality detected even when high-quality disinfected RO-produced water is distributed highlight microbiological challenges that chlorinated systems endure, especially at high water temperatures.
Bio: I come from a civil and environmental engineering background, where I apply an interdisciplinary approach combining microbiology and engineering to investigate water treatment and distribution-related problems. I am a Research Scientist at the Water Desalination and Reuse Center, where I am instigating and coordinating research focused on drinking water production and distribution: seawater desalination, biofouling control strategies, and innovative microbial monitoring of distribution networks.
Lunch will be served in Building 19, Hall 2 (by invitation only)
Session 6 will be chaired by Professor Stefan Arold. This session will feature six student talks related to the field of Metagenomics. Session starts at 2:00pm with Student speaker Wenkai Han's talk.
Abstract: During the pandemic, the severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) spreads and evolves, and many variants emerge due to the vaccine and natural infection-derived immune pressure. The spike proteins, especially the human ACE2 receptor-binding domain (RBD), are the primary mutation target sites due to their role in infection and antibody neutralization. Dominant variants of concern (VOC), like B.1.351 (Beta), B.1.167.2 (Delta), B.1.1529 (Omicron), B.A.2 (Omicron sublineage), have multiple mutations in the region. Here we develop a novel in silico approach combining the antibody/ACE2 structure modeling and large protein language transformer model on RBD sequences, to successfully model the fitness landscape of the spike RBD, by accurately predicting the effects of mutations on ACE2 binding and antibody escape. We validate the model’s effectiveness with the existing RBD sequences in the GISAID database. Under the immune selection pressure, we find a significant correlation between the model scores and known variants sampling time (Spearman r=0.55). The model can be combined with the genetic algorithm capable of forecasting novel mutations that may cause futural concern. The model forecasts the Alpha and Omicron variants’ mutations in the RBD region before they appeared and spread. We further apply our model to forecast variants that may occur and spread in the future and characterize how these variants combine with antibodies in vitro. This approach could be used for the predictive profiling of the rapidly evolving viral diseases and other potential outbreaks, like antibiotic resistance.
Bio: Wenkai received his Bachelor’s degrees in both Bioscience and Computer Science from 2014 to 2018 at the University of Science and Technology of China, Hefei, China. After that, he received his Master’s degree in Computer Science from King Abdullah University Of Science and Technology (KAUST), Saudi Arabia. Currently, he is a Ph.D. candidate in the Department of Computer, Electrical and Mathematical Sciences & Engineering, at KAUST. His current research includes protein informatics, transcriptomic data analysis, and microbiology.
Abstract: Traditionally, the birth of new proteins in biological entities has been attributed to gene duplication and subsequent specialization. However, it has been recognized over the last decade that new genes may materialize de novo from parts of the genome that were previously non-coding. Such is the case of the common rice Oryza sativa subspecies japonica, in which a comparative genomics study that analyzed 13 related Oryza species found at least 175 de novo open reading frames with significantly similar ancestral non-coding sequences, and with an unexpectedly high rate of generation and retention of de novo genes (Zhang et al., 2019). However, the structure of these proteins and their relevance to rice biological pathways is still unknown. In this talk, I will give a brief overview of the bioinformatic approaches that we are using at Stefan Arold’s lab for predicting the structural features of these de novo sequences, with a focus on 3D structure and biophysical properties. I will explain how these techniques help us prioritize candidates from such comparative and/or meta-genomics studies for experimental testing, and how, through some curious findings, they might improve our understanding of the applications and limitations of one of the latest structure prediction algorithms.
Bio: Obtained my B.S. in Biotechnology from Tecnológico de Monterrey, Mexico, in 2017. Then completed a M.S. in Bioscience in KAUST, at the Structural Biology and Engineering lab, with Stefan Arold as my advisor. I am now pursuing a PhD in Bioscience in Stefan Arold's lab, where I am focusing in methods to model protein structures and predict the impact of mutations in the structure and function of proteins with clinical relevance, as well as applying similar methods to predict the 3D structure of de novo proteins and the catalytic fitness of new proteins of environmental and commercial interest such as oceanic PETases.
Abstract: Antimicrobial resistance in Gram-negative organisms is an emerging threat to public health. Since the identification of the first mobile colistin resistance (mcr) gene, mcr-1, in 2015 has attracted worldwide attention. Presently, a total of 10 different mcr-family genes (i.e. mcr-1 to mcr-10) have been described. Here, based on the National Database of Antibiotic-Resistant Organisms and NCBI, we have developed a motif-based mechanism for detecting and differentiating mcr genes. Clustering was done considering the percent identity of 88 such that mcr-1 to mcr-10 are present in 10 clusters. There are additional 39 clusters so we developed motifs for these additional novel mcr classes. This work also showed that mcr genes may circulate silently in the environment a while before being discovered/reported. Further, we investigated the occurrence and global dissemination of known mcr gene variants in publicly available KAUST Metagenomic Analysis Platform (KMAP). In metagenomic data wastewater and humans are significant factors leading to the spread of mcr Antibiotic-Resistant Genes (ARGs) across natural environments. We also applied our motifs on recently available data in a metagenomic study (preprint) to test motif power in distinguishing known classes and possibly new ones.
Abstract: The complexity and diversity of marine microbial communities present many challenges in experimentally and computationally analyzing metagenomics samples. Recent advances have enabled the characterization of microbiomes and the functionality of microbial communities in their environments. Our overall aim is to develop benchmarks for the functional characterization of microbial communities, from sample collection to sequencing data analysis. Our experimental work involves collecting raw sea water samples from the Red Sea and recording their metadata. DNA, RNA and metabolites are extracted from the samples using novel extraction methods. After characterizing a marine microbial community and obtaining high-quality multi-omics data, we aim to develop computational methods to explore the functional characteristics of microbial communities. This functional metagenomics research will explore how microbial communities directly interact with hosts and the environment around them. This will be done by integrating methods for protein function prediction, predicting physical interactions between proteins, predicting metabolic pathways and interactions between microbes and the environment.
Bio: After completing my BSc in biology, psychology and neuroscience at UMass Amherst, I completed my MSc thesis on cholinergic neurotransmission in Hydra under the supervision of Prof. Takashi Gojobori. I'm currently pursuing my PhD at KAUST under the supervision of Prof. Robert Hoehndorf. My research interests include bioinformatics and using computational methods for data analysis in biology. I am interested in working on biological questions that require multi-omics data integration.
Abstract. In (hyper)arid-desert ecosystems resources are limited, and xerophytic plants evolved morphological and physiological adaptation to optimize water/nutrient uptake and storage, such as the formation of a rhizosheath root system. The water and nutrients enriched in and by the rhizosheath represent important resources for the plant, but also for the desert edaphic microorganisms (bacteria, archaea, and fungi) that are attracted by this “resource island” in an otherwise oligotrophic sandy-rocky soil. However, by adapting the Darwinian “Survival of the Fittest” theory to the microbial world, we expect that desert soil microorganisms strongly compete to colonize the favorable rhizosheath–root system niches, and only those functionally equipped (i) to support the host (and their own) survival through biopromotion and biofertilization activities, and (ii) to outcompete potential rivals, can succeed in this. By combining metabarcoding and shotgun metagenomics, we demonstrated that edaphic microbial community diversity, stability and biomass increased from the non-vegetated soils to the rhizosheath–root system. Non-vegetated soil communities promoted autotrophy lifestyle, while those associated with the plant-niches were mainly heterotrophs and enriched in microbial plant-growth promoting capacities, as well as in antibiotic resistance genes and CRISPR-Cas motifs. These results revealed how the colonization of the rhizosheath zone is trigged by an intense microbial “Arms Race” aimed at the control of microbial biomass and by the plant-selection of beneficial microorganisms able to improve the fitness and survival of the host in a win-win interaction. Our results support that such density/competitive niches may also represent evolutionary hotspots that can enhance the resilience and success of the rhizosheath–root microbial communities and their host during environmental stresses and fluctuations, such as those predicted by climate change.
Bio: Ramona Marasco received her BSc degree in Agriculture Biotechnology (2007) and her MSc degree in Plant, food, and environmental Biotechnology (2008) from the University of Milan (Italy). Afterwards she obtained her PhD in Chemistry, Biochemistry and Ecology of Pesticides (2011). During this period, she focused her research on the study of the plant-microbe interaction in arid and saline environments, including hot and cold desert, salty system, and agricultural area in temperate zones. She participated in several scientific expeditions in North Africa deserts, arid and saline environments during her postdoctoral fellow at the Department of Food, Environmental and Nutritional Science at the University of Milan (2012-2014). She moved to KAUST in 2014 as a postdoctoral fellow in the Extreme Systems Microbiology Lab lead by prof. Daniele Daffonchio, and she became Research Scientist in 2017. The main topic of her research is to understand the ecological role of microbial communities naturally associated to plant under stress conditions, such as drought and salinity, and to use this knowledge to gain information about the functional role of microbes in plant fitness, and their possible use to counteract the effects induced by the global climate change.
Abstract:Antimicrobial resistance (AMR) has become a critical threat to human health globally. Since the COVID-19 pandemic, there has been much speculation about how COVID-19 and AMR may be interconnected. Considering that poor sanitation can be a factor that links COVID-19 and AMR threats, focusing on the prevalence of antibiotic resistance genes (ARGs) in untreated hospital wastewater may be needed. In this study, untreated wastewater was sampled from a hospital (hospital A) designated to treat COVID-19 patients during the first wave of COVID-19 pandemic in contrast to another hospital (hospital B) that did not receive any COVID-19 patients. Metagenomics investigated the relative abundance and mobile potential of ARGs and determined the correlation of ARGs with time/incidence of COVID-19. Metagenome-assembled genomes (MAGs) associated with ARGs were also assessed. Metagenomic analysis revealed the larger antibiotic resistome in hospital A (3.79 ‰) in contrast to hospital B (0.164 ‰). ARGs resistant to macrolides, sulfonamides and beta-lactams were found to be predominant and positively correlated with time in hospital A. Specifically, beta-lactamases of class B and part of class D (carbapenem-resistant) positively correlated with time, suggesting the prevalence of carbapenem-resistant ARGs in hospital A. Besides, non-carbapenemase blaVEB were found to be co-resistant to carbapenems across carbapenem-resistant MAGs. Furthermore, carbapenem-resistant pathogens (i.e., Shigella flexneri and Arcobacter butzleri) were found in hospital A revealed the risk of co-infection. This highlighted specific concerns related to AMR dissemination during the COVID-19 pandemic that may arise from untreated hospital wastewater discharge.
Bio: Changzhi Wang is a Ph.D. student in Prof. Pei-Ying Hong’s group. He received his bachelor’s in Bioinformatics from the Southern University of Science and Technology (SUSTech), Shenzhen, China and received his master’s in Environmental Science and Engineering from KAUST, Jeddah, Saudi Arabia. His research involves handling large data sets obtained from multi-omics and applying various bioinformatic pipelines to elucidate the presence of biological contaminants. He is keen to develop bioinformatics that would enable multi-omics in routine monitoring of water treatment technologies and water quality. He has been focused on this area for the past 4 years.
Abstract: In infectious diseases, molecular diagnostics are revolutionizing clinical practice by helping doctors understand a patient's cases caused by infection before symptoms and complications. Moreover, using machine learning algorithms to assist doctors in clinical decision-making and diagnosis is critical for patient treatment decisions and outcomes. However, current automated diagnosis systems only utilize associative deep learning methods that identify diseases strongly correlated with a patient's symptoms without considering the genetic risk factors that may cause complications. Alternatively, they could be related to other complex disorders affecting the patient's situation. In that case, understanding how different viral strains affect individual patients and, in particular, how they interact with different human host cells and immune responses is a fundamental step in order to formulate accurate treatment plans. Since the outbreak of the COVID-19 disease, host genetic variations play a significant role in the manifestation of different degrees of severity of illness among different individuals. It is crucial to use this disease as the first case study in our research. Thus, we develop a deep learning model that provides automated medical plans and predicts the severity score as well as multi-organs dysfunction scores during infection by integrating genetic and viral data with metadata and analyzing risk factors. Our preliminary result shows that our model performs better than state-of-the-art on synthetic data. The data was generated based on descriptive information that explained the severity of COVID-19 patients from scientific articles and medical reports. In addition, we test models on actual medical records of the sensitivity of obtaining medical reports. The predicted scores assist doctors in having a better understanding of the COVID-19 cases and provide an accurate treatment plan that could eventually reduce the severity and complication of infectious diseases
Bio: Sakhaa Al-Saedi is a Ph.D. student and the founder of the startup Medvation, inventing educational kits that teach children concepts of robotics and machine learning through fun and engaging methods. She completed her bachelor's degree in Computer Science in 2017 at Taibah University. Before starting her master's degree at KAUST in 2018, she worked as a product developer at the prototyping lab of the Namma Al-Munawara company in Madinah. She completed her master's degree in Computer Science at KAUST in 2020. There, she worked on human genome sequencing to evaluate the impact of Saudi-specific allele frequencies on variant calling. Sakhaa's research interests include applying deep learning algorithms in the development of genetic variant calling workflows for analyzing human genome sequencing data, developing a platform for integrating multi-omics data, as well as generating AI art from biomedical and genetic data. She is currently working in the Comparative Genomics and Genetics Lab (CGG) and Structural and Functional Bioinformatics Group (SFB) of Professor Takashi Gojobori and Professor Xin Gao, developing an automated genetic-based medical diagnostic system for treatment of infectious diseases using causal deep learning.
Provost Dr. Lawrence Carin and Distinguished Professor Takashi Gojobori will give closing remarks to the 2022 conference and announce poster competition winners.
Please download the PDF version of the Agenda below.