Tathagata Dasgupta

Cytokines in ovarian folliculogenesis, oocyte maturation and luteinisation

Cytokines are key regulators of ovarian physiology, particularly in relation to folliculogenesis and ovulation, where they contribute to creating an environment supporting follicle selection and growth. Their manifold functions include regulating cellular proliferation/differentiation, follicular survival/atresia, and oocyte maturation. Several cytokines, such as TGF‐β‐superfamily members, are involved at all stages of folliculogenesis while the production of others is stage‐dependent. This review draws upon evidence from both human and animal models to highlight the species‐specific roles at each milestone of follicular development. Given these pivotal roles and their ease of detection in follicular fluid, cytokines have been considered as attractive biomarkers of oocyte maturational status and of successful assisted reproductive outcome. Despite this, our understanding of cytokines and their interactions remains incomplete, and is still frequently limited to overly simplistic descriptions of their interrelationships. Given our increased appreciation of cytokine activity in complex and highly regulated networks, we put forward the case for using Bayesian modelling approaches to describe their hierarchical relationships in order to predict causal physiological interactions in vivo. Mol. Reprod. Dev. 81: 284–314, 2014.

Bayesian modeling suggests that IL-12 (p40), IL-13 and MCP-1 drive murine cytokine networks in vivo

BackgroundCytokine-hormone network deregulations underpin pathologies ranging from autoimmune disorders to cancer, but our understanding of these networks in physiological/pathophysiological states remains patchy. We employed Bayesian networks to analyze cytokine-hormone interactions in vivo using murine lactation as a dynamic, physiological model system.ResultsCirculatory levels of estrogen, progesterone, prolactin and twenty-three cytokines were profiled in post partum mice with/without pups. The resultant networks were very robust and assembled about structural hubs, with evidence that interleukin (IL)-12 (p40), IL-13 and monocyte chemoattractant protein (MCP)-1 were the primary drivers of network behavior. Network structural conservation across physiological scenarios coupled with the successful empirical validation of our approach suggested that in silico network perturbations can predict in vivo qualitative responses. In silico perturbation of network components also captured biological features of cytokine interactions (antagonism, synergy, redundancy).ConclusionThese findings highlight the potential of network-based approaches in identifying novel cytokine pharmacological targets and in predicting the effects of their exogenous manipulation in inflammatory/immune disorders.

Quantitative criticism of literary relationships

Significance Famous works of literature can serve as cultural touchstones, inviting creative adaptations in subsequent writing. To understand a poem, play, or novel, critics often catalog and analyze these intertextual relationships. The study of such relationships is challenging because intertextuality can take many forms, from direct quotation to literary imitation. Here, we show that techniques from authorship attribution studies, including stylometry and machine learning, can shed light on inexact literary relationships involving little explicit text reuse. We trace the evolution of features not tied to individual words across diverse corpora and provide statistical evidence to support interpretive hypotheses of literary critical interest. The significance of this approach is the integration of quantitative and humanistic methods to address aspects of cultural evolution. Authors often convey meaning by referring to or imitating prior works of literature, a process that creates complex networks of literary relationships (“intertextuality”) and contributes to cultural evolution. In this paper, we use techniques from stylometry and machine learning to address subjective literary critical questions about Latin literature, a corpus marked by an extraordinary concentration of intertextuality. Our work, which we term “quantitative criticism,” focuses on case studies involving two influential Roman authors, the playwright Seneca and the historian Livy. We find that four plays related to but distinct from Seneca’s main writings are differentiated from the rest of the corpus by subtle but important stylistic features. We offer literary interpretations of the significance of these anomalies, providing quantitative data in support of hypotheses about the use of unusual formal features and the interplay between sound and meaning. The second part of the paper describes a machine-learning approach to the identification and analysis of citational material that Livy loosely appropriated from earlier sources. We extend our approach to map the stylistic topography of Latin prose, identifying the writings of Caesar and his near-contemporary Livy as an inflection point in the development of Latin prose style. In total, our results reflect the integration of computational and humanistic methods to investigate a diverse range of literary questions.

Predicting oocyte fertilisability in intracytoplasmic sperm injection cycles: a retrospective observational study

Abstract Background Conception assisted by intracytoplasmic sperm injection (ICSI) requires oocyte stripping for morphological evaluation of maturity status. However, this approach prevents further maturation and poorly predicts fertilisability, so more robust assessment strategies are needed. Given that cytokines orchestrate oocyte development, we aimed to assess the association of follicular fluid cytokine profiles with maturation stage and develop predictive machine learning-based methods to identify those with the greatest fertilisation potential. Methods In this retrospective study, follicular fluid was collected at oocyte retrieval from 64 women and linked to oocyte maturity status or fate—namely, germinal vesicle (n=26), metaphase I (51), metaphase II not fertilised (51), and metaphase II fertilised (84). 51 follicular fluid cytokines were profiled by multiplex immunoassay. Machine learning-based classifiers to predict oocyte fertilisability were subjected to iterative feature reduction to a threshold suitable for developing a clinically viable assessment of oocyte maturity. Women gave written, informed consent. Findings Cytokine profiles varied dynamically throughout maturation. When applied to naive samples with known outcome, classifiers developed using tumour necrosis factor-related apoptosis-inducing ligand and interleukin 18 profiles alone correctly discriminated 89% of metaphase II not fertilised oocytes (ie, those with the highest fertilisability) with high confidence from all other maturation stages. Interpretation These classifiers offer the prospect of cost-effective, point-of-care testing, and streamlined ICSI-based workflows. This assessment circumvents stripping such that immature or low fertilisability oocytes could benefit from in-vitro maturation and increase the pool of usable oocytes. Further studies will confirm the robustness of these classifiers in women with a broader morbidity spectrum and their translational value across a range of clinical settings. Funding Infertility Research Trust.

A Bayesian view of murine seminal cytokine networks

It has long been established that active agents in seminal fluid are key to initiating and coordinating mating-induced immunomodulation. This is in part governed by the actions of a network of cytokine interactions which, to date, remain largely undefined, and whose interspecific evolutionary conservation is unknown. This study applied Bayesian methods to illustrate the interrelationships between seminal profiles of interleukin (IL)-1alpha, IL-1beta, IL-2, IL-4, IL-5, IL-6, IL-9, IL-10, IL-12 (p70), IL-13, IL-17, eotaxin, granulocyte-colony stimulating factor (G-CSF), granulocyte macrophage-colony stimulating factor (GM-CSF), interferon (IFN)-gamma, keratinocyte-derived chemokine (KC), monocyte chemoattractant protein (MCP-1), macrophage inflammatory protein (MIP-1) alpha, MIP-1beta, regulated on activation normal T cell expressed and secreted (RANTES), tumour necrosis factor (TNF)-alpha, leptin, inducible protein (IP)-10 and vascular endothelial growth factor (VEGF) in a rat model. IL-2, IL-9, IL-12 (p70), IL-13, IL-18, eotaxin, IFN-gamma, IP-10, KC, leptin, MCP-1, MIP-1alpha and TNF-alpha were significantly higher in serum, whilst IL-1beta, IL-5, IL-6, IL-10, IL-17, G-CSF and GM-CSF were significantly higher in seminal fluid. When compared to mouse profiles, only G-CSF was present at significantly higher levels in the seminal fluid in both species. Bayesian modelling highlighted key shared features across mouse and rat networks, namely TNF-alpha as the terminal node in both serum and seminal plasma, and MCP-1 as a central coordinator of seminal cytokine networks through the intermediary of KC and RANTES. These findings reveal a marked interspecific conservation of seminal cytokine networks.