Characterizing interactions in E-cadherin assemblages

Cadherin intermolecular interactions are critical for cell-cell adhesion and play essential roles in tissue formation and the maintenance of tissue structures. In this study, we focus on E-cadherin, a classical cadherin that connects epithelial cells, to understand how they interact in cis and trans conformations when attached to the same cell or opposing cells. We employ coevolutionary sequence analysis and molecular dynamics simulations to confirm previously known interaction sites as well as to identify new interaction sites. The sequence coevolutionary results yield a surprising result indicating that there are no strongly favored intermolecular interaction sites, which is unusual and suggests that many interaction sites may be possible, with none being strongly preferred over others. By using molecular dynamics, we test the persistence of these interactions and how they facilitate adhesion. We build several types of cadherin assemblages, with different numbers and combinations of cis and trans interfaces to understand how these conformations act to facilitate adhesion. Our results suggest that, in addition to the established interaction sites on the EC1 and EC2 domains, an additional plausible cis interface at the EC3-EC5 domain exists. Furthermore, we identify specific mutations at cis/trans binding sites that impair adhesion within E-cadherin assemblages

Non-Coding RNA in Raw and Commercially Processed Milk and Putative Targets Related to Growth and Immune-Response

Background: Bovine milk contains extracellular vesicles (EVs) that play a role in cellular communication, acting in either an autocrine, paracrine, or an exocrine manner. The unique properties of the EVs protect the cargo against degradation. We proled the ncRNAs (non-coding RNA) present in the EVs from ve uid dairy products - raw whole milk, heat-treated skim milk, homogenized heat-treated skim milk, pasteurized homogenized skim milk, and pasteurized heavy whipping cream (four replicates each) obtained at different processing steps from a commercial dairy plant. EVs and their cargo were extracted by using a validated commercial kit that has been shown to be ecient and specic for EVs. Because many ncRNAs and the ncRNAs of bovine are less well characterized that human but are generally highly conserved, both human and Bos taurus databases were probed for putative targets. Results: Thirty microRNAs (miRNAs), isolated from milk, with their corresponding 1546 putative gene targets have functions associated with immune response and growth and development, indicating the potential for these ncRNAs to benecially support mammary health and growth for the cow as well as neonatal gut maturation. The most abundant miRNAs were miR-125, which is involved in host bacterial and viral immune response, and human homolog miR-718 in the regulation of p53, VEGF, and IGF signaling pathways, respectively. Sixty-two miRNAs were enriched and 121 miRNAs were diluted throughout all the milk samples when compared to raw whole milk. In addition, our study explored the putative roles of other ncRNAs which included 88 piRNAs (piwi-interacting RNA), 64 antisense RNAs, and 105 longintergenic ncRNAs contained in the bovine exosomes. Conclusion: Together, the results indicate that bovine milk contains signicant numbers of ncRNAs with putative regulatory targets associated with immune- and developmental-functions important for neonatal bovine health, and that processing signicantly increases the abundance of these ncRNA species. It is worth noting, however, that these gene regulatory targets are putative, and, though not necessary, further evidence could be generated through experimental validation

Profiling of the Exosomal Cargo of Bovine Milk Reveals the Presence of Immune- and Growth-modulatory Non-coding RNAs (ncRNA)

The objective of the study was to characterize non-coding RNAs (ncRNA) present in raw milk collected from one commercial dairy processing facility. Silo milk was selected because it should be representative of raw milk of “typical” lactating dairy cows. Our hypothesis was that raw bovine milk would contain immune- and developmental-related ncRNA that may support the health of the mammary gland of the cow and could aid in the maturation of the neonatal gut. Four samples of raw silo milk were collected on separate days, and total RNA was purified and profiled by using next-generation sequencing (RNA-seq). Our findings indicate an over-representation of ncRNA that target genes related to both immune modulation and growth and development, supporting our hypothesis that ncRNA in raw milk may help support the health of both the cow and calf

ISCB Student Council Symposium 2021, a virtual global venue : challenges and lessons learned

Since 2004, the ISCB Student Council has been organizing different symposia worldwide, gathering together the community of young computational biologists. Due to the coronavirus disease 2019 (COVID-19) pandemic situation, the world scientific community was forced to cancel in-person meetings for almost two years, imposing the adoption of virtual formats instead. After the successful editions of our continental symposia in 2020 in the USA, Latin America, and Europe, we organized our flagship global event, the Student Council Symposium (SCS) 2021, trying to apply all previous lessons learned and to exploit the advantages that virtuality has to offer

Global network of computational biology communities: ISCB's regional student groups breaking barriers [version 1; peer review: Not peer reviewed]

Regional Student Groups (RSGs) of the International Society for Computational Biology Student Council (ISCB-SC) have been instrumental to connect computational biologists globally and to create more awareness about bioinformatics education. This article highlights the initiatives carried out by the RSGs both nationally and internationally to strengthen the present and future of the bioinformatics community. Moreover, we discuss the future directions the organization will take and the challenges to advance further in the ISCB-SC main mission: “Nurture the new generation of computational biologists”.Fil: Shome, Sayane. University of Iowa; Estados UnidosFil: Parra, Rodrigo Gonzalo. European Molecular Biology Laboratory; Alemania. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Fatima, Nazeefa. Uppsala Universitet; SueciaFil: Monzon, Alexander Miguel. Università di Padova; ItaliaFil: Cuypers, Bart. Universiteit Antwerp; BélgicaFil: Moosa, Yumna. University of KwaZulu Natal; SudáfricaFil: Da Rocha Coimbra, Nilson. Universidade Federal de Minas Gerais; BrasilFil: Assis, Juliana. Universidade Federal de Minas Gerais; BrasilFil: Giner Delgado, Carla. Universitat Autònoma de Barcelona; EspañaFil: Dönertaş, Handan Melike. European Molecular Biology Laboratory. European Bioinformatics Institute; Reino UnidoFil: Cuesta Astroz, Yesid. Universidad de Antioquia; Colombia. Universidad Ces. Facultad de Medicina.; ColombiaFil: Saarunya, Geetha. University of South Carolina; Estados UnidosFil: Allali, Imane. Universite Mohammed V. Rabat; Otros paises de África. University of Cape Town; SudáfricaFil: Gupta, Shruti. Jawaharlal Nehru University; IndiaFil: Srivastava, Ambuj. Indian Institute of Technology Madras; IndiaFil: Kalsan, Manisha. Jawaharlal Nehru University; IndiaFil: Valdivia, Catalina. Universidad Andrés Bello; ChileFil: Olguín Orellana, Gabriel José. Universidad de Talca; ChileFil: Papadimitriou, Sofia. Vrije Unviversiteit Brussel; Bélgica. Université Libre de Bruxelles; BélgicaFil: Parisi, Daniele. Katholikie Universiteit Leuven; BélgicaFil: Kristensen, Nikolaj Pagh. Technical University of Denmark; DinamarcaFil: Rib, Leonor. Universidad de Copenhagen; DinamarcaFil: Guebila, Marouen Ben. University of Luxembourg; LuxemburgoFil: Bauer, Eugen. University of Luxembourg; LuxemburgoFil: Zaffaroni, Gaia. University of Luxembourg; LuxemburgoFil: Bekkar, Amel. Universite de Lausanne; SuizaFil: Ashano, Efejiro. APIN Public Health Initiatives; NigeriaFil: Paladin, Lisanna. Università di Padova; ItaliaFil: Necci, Marco. Università di Padova; ItaliaFil: Moreyra, Nicolás Nahuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Ecología, Genética y Evolución de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Ecología, Genética y Evolución de Buenos Aires; Argentin

Revealing the impact of lifestyle stressors on the risk of adverse pregnancy outcomes with multitask machine learning

Psychosocial and stress-related factors (PSFs), defined as internal or external stimuli that induce biological changes, are potentially modifiable factors and accessible targets for interventions that are associated with adverse pregnancy outcomes (APOs). Although individual APOs have been shown to be connected to PSFs, they are biologically interconnected, relatively infrequent, and therefore challenging to model. In this context, multi-task machine learning (MML) is an ideal tool for exploring the interconnectedness of APOs on the one hand and building on joint combinatorial outcomes to increase predictive power on the other hand. Additionally, by integrating single cell immunological profiling of underlying biological processes, the effects of stress-based therapeutics may be measurable, facilitating the development of precision medicine approaches.ObjectivesThe primary objectives were to jointly model multiple APOs and their connection to stress early in pregnancy, and to explore the underlying biology to guide development of accessible and measurable interventions.Materials and MethodsIn a prospective cohort study, PSFs were assessed during the first trimester with an extensive self-filled questionnaire for 200 women. We used MML to simultaneously model, and predict APOs (severe preeclampsia, superimposed preeclampsia, gestational diabetes and early gestational age) as well as several risk factors (BMI, diabetes, hypertension) for these patients based on PSFs. Strongly interrelated stressors were categorized to identify potential therapeutic targets. Furthermore, for a subset of 14 women, we modeled the connection of PSFs to the maternal immune system to APOs by building corresponding ML models based on an extensive single cell immune dataset generated by mass cytometry time of flight (CyTOF).ResultsJointly modeling APOs in a MML setting significantly increased modeling capabilities and yielded a highly predictive integrated model of APOs underscoring their interconnectedness. Most APOs were associated with mental health, life stress, and perceived health risks. Biologically, stressors were associated with specific immune characteristics revolving around CD4/CD8 T cells. Immune characteristics predicted based on stress were in turn found to be associated with APOs.ConclusionsElucidating connections among stress, multiple APOs simultaneously, and immune characteristics has the potential to facilitate the implementation of ML-based, individualized, integrative models of pregnancy in clinical decision making. The modifiable nature of stressors may enable the development of accessible interventions, with success tracked through immune characteristics

Exploring molecular mechanisms with simulations and data analyses

Proteins are the active players in the cells and carry out most of the significant functions throughout biology such as metabolism, immunity, and maintaining structural integrity. Protein characteristic behaviors such as dynamics and residue level co-evolution play a critical role in determining the detailed behaviors of any given protein. We have employed a range of computational methods: molecular dynamics simulations, principal component analysis of conformations, molecular modeling, and co-evolution analysis of sequences to attempt to understand the molecular mechanisms in membrane transporters and transmembrane proteins. The systems considered here include the Multi-Drug-Resistant efflux pumps present in gram-negative bacteria and the classical cadherins observed in epithelial cells. We gain critical insights into mechanisms from these studies regarding functional dynamics and identifying structural features that play key roles in the efflux of cyclic lipids in HpnN transporters. Further, our studies newly identify a novel cis interface between classical cadherin. This originates from a strong correlation between co-evolving residues, suggesting that this interaction plays an important role in cell-cell adhesion. Based on this work, we have been able to devise a workflow incorporating these methods that provide new insights for selection of sites to target to inhibit function

Exploring molecular mechanisms with simulations and data analyses

Proteins are the active players in the cells and carry out most of the significant functions throughout biology such as metabolism, immunity, and maintaining structural integrity. Protein characteristic behaviors such as dynamics and residue level co-evolution play a critical role in determining the detailed behaviors of any given protein. We have employed a range of computational methods: molecular dynamics simulations, principal component analysis of conformations, molecular modeling, and co-evolution analysis of sequences to attempt to understand the molecular mechanisms in membrane transporters and transmembrane proteins. The systems considered here include the Multi-Drug-Resistant efflux pumps present in gram-negative bacteria and the classical cadherins observed in epithelial cells. We gain critical insights into mechanisms from these studies regarding functional dynamics and identifying structural features that play key roles in the efflux of cyclic lipids in HpnN transporters. Further, our studies newly identify a novel cis interface between classical cadherin. This originates from a strong correlation between co-evolving residues, suggesting that this interaction plays an important role in cell-cell adhesion. Based on this work, we have been able to devise a workflow incorporating these methods that provide new insights for selection of sites to target to inhibit function