Professor and RCR Coordinator
3128 East Science Hall, Science & Engineering Complex
(205) 934-8335
Research and Teaching Interests: Systems Biology, Network Science, Functional Genomics, Bioinformatics, Deep/Machine Learning using Animal and Plant Models
Office Hours: By appointment
Education:
- Ph.D., Max-Planck Institute for Plant Breeding Research, Cologne, Germany, Genetics
Dr. Mukhtar conducted his Ph.D. research on Arabidopsis transcriptional regulatory networks at the Max Planck Institute Cologne, Germany under the supervision of Dr. Imre Somssich. He was fortunate to continue his postdoctoral research in the laboratory of Dr. Jeff Dangl, a Howard Hughes Medical Institute investigator and a member of the National Academy of Sciences, at the University of North Carolina at Chapel Hill. He employed genomics, bioinformatics and computer-aided systems-level analyses to generate the first large-scale Arabidopsis-pathogens protein-protein interaction network in collaboration with the Dana Farber Cancer Institute & Center for Cancer Systems Biology, an affiliate of Harvard Medical School in Boston, MA. His four years long post-doc resulted in a first author publication in Science (The Arabidopsis-pathogens interactome; Mukhtar et al. Science 2011), as well as a number of co-authored high impact papers, including another large-scale network assembly (the Arabidopsis interactome map; Science 2011), global mapping of the G-protein interactions (Molecular Systems Biology 2011) and sequencing and assembly of 19 strains of pathogenic bacterium P. syringae using next-generation sequencing approaches (PLoS Pathogens 2011).
Dr. Mukhtar’s research at UAB focuses at the interface of bioinformatics and life sciences. He is broadly interested in interdisciplinary research projects focused on genomics/systems biology using computational and deep learning approaches in diverse models (Ahmed et al. Nature Comm 2018, Smakowska-Luzan et al. Nature, 2018, Kumar et al, 2020, iScience). He aims to understand how macromolecular networks are organized in the cells and how pathogenic or disease-associated cues perturb such networks (Mishra et al. 2021 Current Opinion in Plant Biology). Specifically, in Arabidopsis, his laboratory is using machine/deep learning approaches to identify gene function of novel ORFs in plant pathology and plant physiology (Mishra et al. MPMI, 2020). His laboratory generates large-scale datasets of macromolecular interactions and study them by applying mathematical modeling, bioinformatics, and computational tools, both existing and developed by us in-house, to decipher the network structure, topological properties of the interactions, and ultimately extract new biological information. Currently, he is funded by two NSF awards as the PI. IOS-1557796 ($800,000) is focused on understanding the manipulation of sugar regulatory networks by pathogens. The second award NSF grant (IOS-2038872; $1,027,270), which employs artificial intelligence (machine and deep learning approaches) to predict functions of micro- and macronutrients in plants. Owing to the collaborative nature of his research, he has been given a secondary faculty appointment in the Department of Surgery at UAB School of Medicine as well as appointed as a Scientist at the Nutrition and Obesity Research Center as well as a member of the Program of Immunology at SOM. Besides, he has collaborations that led to joint research publications and active grant funding from NIH as co-investigator (Co-I) in the departments of Dermatology and Neurobiology at UAB SOM. Using network science and machine learning approaches, he aims to identify major regulators in a cell that can be potential drug targets. His interdisciplinary and cross-disciplinary experience includes a record of 62 peer-reviewed manuscripts (Shahid Mukhtar Google Scholar). The Shahid Mukhtar Lab carries out a highly research-active program with graduate and undergraduate students working on a wide range of bioinformatics/genomics projects.
Dr. Mukhtar has a strong track record of mentoring students who have won awards and scholarships, including the Rising Star Award from UAB National Alumni Society, NSF Graduate Research Scholar Program fellowship, Ireland Research Travel Scholarship, Biology Outstanding Student Development Award, CAS Dean's Outstanding Student Award and CAS Dean's scholarship, and various awards for poster and oral presentations at UAB EXPO, regional and national meetings/conferences. He has been recognized with a number of teaching awards, including the 2021 UAB President's Award for Excellence in Teaching, and the 2021 CAS Dean's Award for Excellence in Teaching.
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Research Interests
Genomics is the study of the entire genome, usually starting with whole genome sequencing. In the recent years, genomics and proteomics have become powerful tools for revealing gene function and genomic organization in large scale. Functional genomics relies on using the sequence data to explore how DNA and proteins work with each other and the environment to create complex, dynamic living systems.
The long-term goal of the Shahid Mukhtar Lab is to understand how macromolecular networks control biological processes and how environmental perturbations in such networks can explain diverse phenotypes. The current projects under investigation include:
- Dynamics of Transcriptional Regulatory Networks in Plant Defense
- Microbial Community Transcriptional Networks
- Predictive Modelling to Identify Key Functionalities in Host-Pathogen Interactions
- Exploring the Phytohormone-mediated Dynamic Properties of Network Modules
- Systems Analyses to Identify Host Cell Metabolism Functional Modules.
For details about these projects, please check out the Shahid Mukhtar Lab.
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Recent Courses
- BY210: Genetics
BY210 provides an introduction to: (a) molecular genetic technologies, (b) principles and mechanisms of inheritance, (c) structure, action and regulation of genes, (d) applications of modern molecular genetic technology to human health and agriculture and (e) necessary background for upper-level courses in molecular genetics, cell biology and evolution.
- BY434/634/734: Functional Genomics and Systems Biology
BY434/634/734 covers state-of-the-art, high-throughput, established and novel, experimental and computational approaches used in genome sequencing, transcriptomics, proteomics and metabolomics to obtain, integrate and analyze complex data. It is aimed to familiarize students with knowledge on experimental perturbation of genomes, gene regulatory networks, comparative genomics and evolution, as well as basic bioinformatics. This course is a combination of text-based lectures, computer exercises and discussions of the current literature relevant to Functional Genomics and Systems Biology.
- BY499/789: Seminar in Genetics/Genomics
BY499/789 is designed to refine and extend students’ fluency (both verbal and written) in genetic concepts and techniques. Through the dissection of research papers on diverse genetics/genomics aspects, students learn how to critically think about experimental designs. Students also learn how to devise and propose hypothetical experiments to address unanswered questions in genetics/genomics. They can also appreciate the peer review process through critiquing the peer-reviewed papers.
- BY397: Advanced Directed Reading
Dr. Shahid Mukhtar has been serving as a as a mentor for BY397 for reading and independent study in the areas of Genomics/Bioinformatics and computational modeling.
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Graduate Students
Current Students
- Ben Marsella, Lab Manager
- Peter Blair, Ph.D. student
- Yali Sun, Ph.D. student
- Timothy C. (TC) Howton, Ph.D. student
- John Lawson, M.S. student
- Hayden Hamidi, M.S. student
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Select Publications
- Mishra B., Kumar N. and Mukhtar MS*. Network biology to uncover functional and structural properties of the plant immune system (2021). Current Opinion in Plant Biology 62, 102057. https://doi.org/10.1016/j.pbi.2021.102057
- Mishra B, Sun Y, Howton TC, Kumar N and Mukhtar MS*. Dynamic modeling of transcriptional gene regulatory network uncovers novel pathways during the onset of Arabidopsis leaf senescence (2018). NPJ Systems Biology and Applications, https://doi.org/10.1038/s41540-018-0071-2.
- Ahmed H§, Howton TC§, Sun Y§, Weinberger N, Belkhadir Y and Mukhtar MS*. Network biology discovers pathogen contact points in host protein-protein interactomes. (2018) Nature Communications. 9: 2312. doi: 10.1038/s41467-018-04632-8
- Smakowska E§, Mott A§, Stegmann M§, Parys K, Howton TC, Layeghifard M, Neuhold J , Lehner A, Grunwald K, Weinberger N, Kong J, Satbhai S Busch W, Madalisnki M, Mayer D, Stolt-Bergner P, Provart N, Mukhtar MS, Zipfel C, Desveaux D§, Guttman D§, and Belkhadir Y§. (2018). An extracellular network of Arabidopsis leucine-rich repeat receptor kinases. Nature. Jan 18;553(7688):342-346. doi: 10.1038/nature25184. Epub 2018 Jan 10. § Equal contributions.
- Washington EJ, Mukhtar MS, Finkel OM, Wan L, Banfield MJ, Kieber JJ, Dangl JL. (2016).The P. syringae type III effector HopAF1 suppresses plant immunity by targeting methionine recycling to block ethylene induction. Proceedings of the National Academy of Sciences (PNAS), Jun 21;113(25):E3577-86. doi: 10.1073/pnas.1606322113).
- Weßling R, et al . Mukhtar MS (36 authors) (2014). Convergent targeting of a common host protein-network by pathogen effectors from three kingdoms of life. 16(3):364-75. doi: 10.1016/j.chom.2014.08.004. Cell Host Microbe, IF: 12.33.
- Mukhtar MS, Carvunis A-R, Dreze M, Epple P, et al., Vidal M, Beynon J, Braun P and Dangl JL (2011). Independently evolved virulence effectors converge onto hubs in a plant immune system network. 333: 596-601. SCIENCE
- The Arabidopsis Interactome Mapping Consortium (2011). Evidence for Network Evolution in an Arabidopsis Interactome Map. 333: 601-607. SCIENCE
- Mukhtar MS, Nishimura MT, Dangl JL (2009). NPR1 in Plant Defense: It's Not Over 'til It's Turned over. 29:804-806. CELL.
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Academic Distinctions and Professional Societies
- American Phytopathological Society
- American Society of Plant Biologists
- International Society of Molecular Plant-Microbe Interactions
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Student Groups
- Phi Sigma
- Alpha Epsilon Delta
- Sigma Xi