A cross-sectional study confirms temporary post-COVID-19 vaccine menstrual irregularity and the associated physiological changes among vaccinated women in Jordan

Background: COVID-19 vaccines continue to save people’s lives around the world; however, some vaccine adverse events have been a major concern which slowed down vaccination campaigns. Anecdotal evidence pointed to the vaccine effect on menstruation but evidence from the adverse event reporting systems and the biomedical literature was lacking. This study aimed to investigate the physiological changes in women during menstruation amid the COVID-19 vaccination. Methods: A cross-sectional online survey was distributed to COVID-19 vaccinated women from Nov 2021 to Jan 2022. The results were analyzed using the SPSS software. Results: Among the 564 vaccinated women, 52% experienced significant menstrual irregularities post-vaccination compared to before regardless of the vaccine type. The kind of menstrual irregularity varied among the vaccinated women, for example, 33% had earlier menstruation, while 35% reported delayed menstruation. About 31% experienced heavier menstruation, whereas 24% had lighter menstrual flow. About 29% had menstruation last longer, but 13% had it shorter than usual. Noteworthy, the menstrual irregularities were more frequent after the second vaccine shot, and they disappeared within 3 months on average. Interestingly, 24% of the vaccinated women reported these irregularities to their gynecologist. Conclusion: The COVID-19 vaccine may cause physiological disturbances during menstruation. Luckily, these irregularities were short-termed and should not be a reason for vaccine hesitancy in women. Further studies are encouraged to unravel the COVID-19 vaccine adverse effect on women’s health

Identifying a causal link between prolactin signaling pathways and COVID-19 vaccine-induced menstrual changes

COVID-19 vaccines have been instrumental tools in the fight against SARS-CoV-2 helping to reduce disease severity and mortality. At the same time, just like any other therapeutic, COVID-19 vaccines were associated with adverse events. Women have reported menstrual cycle irregularity after receiving COVID-19 vaccines, and this led to renewed fears concerning COVID-19 vaccines and their effects on fertility. Herein we devised an informatics workflow to explore the causal drivers of menstrual cycle irregularity in response to vaccination with mRNA COVID-19 vaccine BNT162b2. Our methods relied on gene expression analysis in response to vaccination, followed by network biology analysis to derive testable hypotheses regarding the causal links between BNT162b2 and menstrual cycle irregularity. Five high-confidence transcription factors were identified as causal drivers of BNT162b2-induced menstrual irregularity, namely: IRF1, STAT1, RelA (p65 NF-kB subunit), STAT2 and IRF3. Furthermore, some biomarkers of menstrual irregularity, including TNF, IL6R, IL6ST, LIF, BIRC3, FGF2, ARHGDIB, RPS3, RHOU, MIF, were identified as topological genes and predicted as causal drivers of menstrual irregularity. Our network-based mechanism reconstruction results indicated that BNT162b2 exerted biological effects similar to those resulting from prolactin signaling. However, these effects were short-lived and didn’t raise concerns about long-term infertility issues. This approach can be applied to interrogate the functional links between drugs/vaccines and other side effects

Chemocentric Informatics Approach to Drug Discovery: Identification and Experimental Validation of Selective Estrogen Receptor Modulators as Ligands of 5-Hydroxytryptamine-6 Receptors and as Potential Cognition Enhancers

We have devised a chemocentric informatics methodology for drug discovery integrating independent approaches to mining biomolecular databases. As a proof of concept, we have searched for novel putative cognition enhancers. First, we generated Quantitative Structure- Activity Relationship (QSAR) models of compounds binding to 5-hydroxytryptamine-6 receptor (5HT6R), a known target for cognition enhancers, and employed these models for virtual screening to identify putative 5-HT6R actives. Second, we queried chemogenomics data from the Connectivity Map (http://www.broad.mit.edu/cmap/) with the gene expression profile signatures of Alzheimer’s disease patients to identify compounds putatively linked to the disease. Thirteen common hits were tested in 5-HT6R radioligand binding assays and ten were confirmed as actives. Four of them were known selective estrogen receptor modulators that were never reported as 5-HT6R ligands. Furthermore, nine of the confirmed actives were reported elsewhere to have memory-enhancing effects. The approaches discussed herein can be used broadly to identify novel drug-target-disease associations