Cristina Alexandru

Cristina Alexandru
Postdoctoral Research Associate (2018-present)

Born and raised in a small Romanian town, I developed an interest in traditions and cultures from all over the world and a passion for food. I am particularly fond of the history from the periods of the Three Kingdoms of Korea, the Tang and Yuan Chinese Dynasties and, above all, the Empire of Alexander the Great and the Hellenistic period following his death. 

I love any kind of food (apart from seafood), especially soups and steaks, but my absolute favourite, however, remains good homemade chips. I am always up for having fun with a board game and I am best known as a Bubble Tea enthusiast! 

Growing up, my favourite school subject was Mathematics, but I eventually opted for a future scientific career.

Career History

2018 - present: Postdoctoral Research Associate, Molecular Biology and Biotechnology Department, University of Sheffield, UK – Working on the regulation potential of introns in 3’UTRs of cancer genes.

2014 - 2019: PhD Chemistry, Institute of Medical Sciences / Biodiscovery Centre, University of Aberdeen, Scotland – Focused on biochemical and computational studies of enzyme-mediated cyclisation of peptides and specialised in protein purification, mass spectrometry and molecular dynamics.

2010 - 2014: BSc Genetics and Microbiology, Molecular Biology and Biotechnology Department, University of Sheffield – Investigated the factors influencing macrophage polarization in the lung in COPD conditions.

Research Focus

Alternative intron splicing permits a single gene to encode many different proteins and can therefore influence both gene expression and regulation. Despite their prevalence, 3’UTR introns (3UIs) are seen as signatures of non-functional transcripts to be degraded via non-sense mediated decay (NMD). However, such splicing events may remove regulatory sequences like miRNA response elements (MREs) and RNA binding proteins (RBPs), or may regulate transcript levels by modulation of NMD, and could thus represent a novel, unexplored mechanism of gene regulation.

Investigating and comparing the occurrence of 3UIs in both healthy individuals and cancer patients could help us confirm the above hypothesis, by observing a potential pattern relevant to cancer. In this sense, we are applying several bioinformatic tools for analysing control and cancer-specific samples from The Cancer Genome Atlas (TCGA). Our results have so far recorded a large number of 3UIs in both healthy and diseased tissues, suggesting these are not just errors in splicing events. We are also measuring their impact on transcript stability, as well as their regulatory effects, by identifying whether 3UIs overlap with miRNA binding sites. This will help us gain a better understanding on the fundamental principles of gene regulation and to analyse differences between individuals of different populations.


  1. Osei , E , Kwain , S , Mawuli , G T , Anang , A K , Owusu , K B-A , Camas , M , Camas , A S , Ohashi , M , Alexandru-Crivac , C-N , Deng , H , Jaspars , M & Kyeremeh , K 2018 , ' Paenidigyamycin A, Potent Antiparasitic Imidazole Alkaloid from the Ghanaian Paenibacillus sp. DE2SH ' , Marine Drugs , vol. 17 , no. 1 , 9.
  2. C. N. Alexandru-Crivac, L. Dalponte, W. E. Houssen, M. Idress, M. Jaspars, K. A. Rickaby and L. Trembleau, Chapter 15: Cyclic Peptides – A Look to the Future, in Cyclic Peptides: From Bioorganic Synthesis to Applications, 2017, editors: J.Koehnke, J. Naismith, W.A van der Donk.
  3. C. N. Alexandru-Crivac, C. Umeobika, N. Leikoski, J. Jokela, K. Rickaby, A.M. Grilo, P. Sjö,   A.T. Plowright, M. Idress, E. Siebs, A. Nneoyi-Egbe, M. Wahlsten, K. Sivonen, M. Jaspars, L. Trembleau, D.P. Fewer and W.E. Houssen, Cyclic Peptide Production Using a Macrocyclase with Enhanced Substrate Promiscuity and Relaxed Recognition Determinants, Chem. Commun., 2017,53, 10656-10659;
  4. C.N. Alexandru-Crivac, J. Booth, K.A. Rickaby, A.F. Nneoyiegbe, U. Umeobika, A.R. McEwan, L. Trembleau, M. Jaspars, W.E. Houssen and D.V. Shalashilin, A Blind Test of Computational Technique for Predicting the Likelihood of Peptide Sequences to Cyclize, J. Phys. Chem. Lett. 2017, 8, 2310−2315.