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Stability of Fecal Microbiota during Degradation in Ex Situ Cheetahs in the US 美国原地猎豹粪便微生物群在降解过程中的稳定性
Pub Date : 2024-03-01 DOI: 10.1530/mah-23-0022
Morgan Maly, Adrienne E Crosier, Mia M. Keady, Reade B Roberts, Matthew Breen, Carly R. Muletz-Wolz
Objective:Gut health and its relationship to gut microbiota is an important consideration in the care and well-being of managed endangered species, such as the cheetah (Acinonyx jubatus). Non-invasive fecal sampling as a proxy for gut microbiota is preferred and collecting fresh fecals is the current gold standard. Unfortunately, even in managed facilities, collecting fresh samples from difficult to observe or dangerous animals is challenging. Therefore, we conducted a study to determine the terminal collection timepoint for fecal microbial studies in the cheetah.Methods:We longitudinally sampled eight freshly deposited fecals every 24 hours for five days and assessed bacterial relative abundance, diversity, and composition changes over time.Results:Our data indicated that fecal samples up to 24 hours post-defecation provided accurate representations of the fresh fecal microbiome. After 24 hours, major changes in community composition began to occur. By 72 hours, individual cheetah fecal microbiota signatures were lost.Conclusions:Our findings suggest that cheetah fecal samples should be collected within 24 hours of defecation in humid environments, especially if precipitation occurs, in order to provide a more biologically accurate representation of the gut microbiome, and we provide visual characteristics that can aid researchers in approximating time since defecation.Significance:These data provide guidelines for researchers investigating cheetah and other large felids and carnivores where the ability to collect fresh fecal deposits is limited.
目的:肠道健康及其与肠道微生物群的关系是管理濒危物种(如猎豹)的护理和福利的一个重要考虑因素。无创粪便采样作为肠道微生物群的替代物是首选,而收集新鲜粪便是目前的黄金标准。遗憾的是,即使在有管理的设施中,从难以观察或危险的动物身上采集新鲜样本也是一项挑战。因此,我们进行了一项研究,以确定猎豹粪便微生物研究的最终采集时间点。方法:我们在五天内每 24 小时对八份新鲜粪便进行纵向采样,并评估细菌的相对丰度、多样性和组成随时间的变化。结果:我们的数据表明,排便后 24 小时内的粪便样本可以准确地反映新鲜粪便微生物群。24 小时后,群落组成开始发生重大变化。结论:我们的研究结果表明,在潮湿的环境中,尤其是在出现降水的情况下,应该在排便后 24 小时内采集猎豹粪便样本,以便从生物学角度更准确地反映肠道微生物群,我们还提供了视觉特征,可以帮助研究人员大致确定排便后的时间。
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引用次数: 0
Deep Learning Enables Early-Stage Prediction of Preterm Birth Using Vaginal Microbiota 深度学习利用阴道微生物群实现早产的早期预测
Pub Date : 2024-03-01 DOI: 10.1530/mah-23-0024
Kaushik Karambelkar, Mayank Baranwal
Objective: Preterm birth (PTB) is one of the leading issues concerning infant health and is a problem that plagues all parts of the world. Vaginal microbial communities have recently garnered attention in the context of PTB, however, the vaginal microbiome varies greatly from individual to individual, and this variation is more pronounced in racially, ethnically and geographically diverse populations. Additionally, microbial communities have been reported to evolve during the duration of the pregnancy, and capturing such a signature may require higher, more complex modeling paradigms. In this study, we develop a neural controlled differential equations (CDEs) based framework for identifying early PTBs in racially diverse cohorts from irregularly sampled vaginal microbial abundance data. Methods: We obtained relative abundances of microbial species within vaginal microbiota using 16S rRNA sequences obtained from vaginal swabs at various stages of pregnancy. We employed a recently introduced deep learning paradigm known as ``Neural CDEs" to predict PTBs. This method, previously unexplored, analyzes irregularly sampled microbial abundance profiles in a time-series format.Results: Our framework is able to identify signatures in the temporally evolving vaginal microbiome during trimester~2 and can predict incidences of PTB (mean test set ROC-AUC = 0.81, accuracy = 0.75, f1 score = 0.71) significantly better than traditional ML classifiers, thus enabling effective early-stage PTB risk assessment. Conclusion and Significance: Our method is able to differentiate between term and preterm outcomes with a substantial accuracy, despite being trained using irregularly sampled microbial abundance profiles, thus overcoming the limitations of traditional time-series modeling methods.
目的:早产(PTB)是影响婴儿健康的主要问题之一,也是困扰世界各地的一个问题。最近,阴道微生物群落在早产方面引起了人们的关注,然而,不同个体的阴道微生物群落差异很大,而且这种差异在种族、民族和地域不同的人群中更为明显。此外,据报道,微生物群落在怀孕期间会发生演变,而捕捉这种特征可能需要更高、更复杂的建模范例。在本研究中,我们开发了一个基于神经控制微分方程(CDEs)的框架,用于从不规则采样的阴道微生物丰度数据中识别种族多样化队列中的早期 PTB。方法:我们利用从怀孕不同阶段的阴道拭子中获取的 16S rRNA 序列,获得了阴道微生物群中微生物物种的相对丰度。我们采用了最近推出的一种称为 "神经 CDE "的深度学习范式来预测 PTB。这种方法以时间序列的形式分析不规则采样的微生物丰度剖面,这在以前还没有被探索过:结果:我们的框架能够识别妊娠期~2 个月期间随时间演变的阴道微生物群特征,并能预测 PTB 的发病率(平均测试集 ROC-AUC = 0.81,准确率 = 0.75,f1 得分 = 0.71),明显优于传统的 ML 分类器,从而实现了有效的早期 PTB 风险评估。结论和意义:我们的方法使用不规则采样的微生物丰度曲线进行训练,但仍能准确区分足月儿和早产儿,从而克服了传统时间序列建模方法的局限性。
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引用次数: 0
Metabolomics in the Era of Artificial Intelligence 人工智能时代的代谢组学
Pub Date : 2024-02-01 DOI: 10.1530/mah-23-0017
Elizabeth A Coler, Wunxuan Chen, Alexey V Melnik, James T Morton, Alexander A Aksenov
Artificial Intelligence (AI) is rapidly revolutionizing our daily lives, as it automates mundane tasks, enhances productivity, and transforms how we interact with technology. We believe it is inevitable that AI will soon become a crucial tool in common research practices, from data analysis to writing papers. Here we explore how this transition is occurring in the field of mass spectrometry-based metabolomics, a rapidly growing area of science. Metabolomics focuses on studying small molecules in biological systems, offering valuable insights into metabolic processes and their impact on health, diseases, and physiological conditions. With the remarkable advancements in sequencing technologies and the exploration of the microbiome, the combination of sequencing and metabolomics presents profound opportunities to understand biological complexity. Incorporating AI will unlock new possibilities and will, in all likelihood, contribute to scientific discoveries in the future. In this review we discuss the current role of AI in metabolomics. Existing practices are examined and we also provide a perspective on future directions for integrating AI into scientific research.
人工智能(AI)正在迅速彻底改变我们的日常生活,因为它能将平凡的任务自动化,提高生产率,并改变我们与技术的交互方式。我们相信,人工智能将不可避免地很快成为从数据分析到撰写论文等常见研究实践中的重要工具。在这里,我们将探讨这一转变是如何在基于质谱的代谢组学领域发生的,这是一个快速发展的科学领域。代谢组学侧重于研究生物系统中的小分子,为了解代谢过程及其对健康、疾病和生理状况的影响提供宝贵的见解。随着测序技术的显著进步和对微生物组的探索,测序与代谢组学的结合为了解生物的复杂性提供了深远的机遇。结合人工智能将开启新的可能性,并很有可能在未来为科学发现做出贡献。在本综述中,我们将讨论人工智能目前在代谢组学中的作用。我们研究了现有的做法,并对将人工智能融入科学研究的未来方向提出了展望。
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引用次数: 0
Metabolomics in the Era of Artificial Intelligence 人工智能时代的代谢组学
Pub Date : 2024-02-01 DOI: 10.1530/mah-23-0017
Elizabeth A Coler, Wunxuan Chen, Alexey V Melnik, James T Morton, Alexander A Aksenov
Artificial Intelligence (AI) is rapidly revolutionizing our daily lives, as it automates mundane tasks, enhances productivity, and transforms how we interact with technology. We believe it is inevitable that AI will soon become a crucial tool in common research practices, from data analysis to writing papers. Here we explore how this transition is occurring in the field of mass spectrometry-based metabolomics, a rapidly growing area of science. Metabolomics focuses on studying small molecules in biological systems, offering valuable insights into metabolic processes and their impact on health, diseases, and physiological conditions. With the remarkable advancements in sequencing technologies and the exploration of the microbiome, the combination of sequencing and metabolomics presents profound opportunities to understand biological complexity. Incorporating AI will unlock new possibilities and will, in all likelihood, contribute to scientific discoveries in the future. In this review we discuss the current role of AI in metabolomics. Existing practices are examined and we also provide a perspective on future directions for integrating AI into scientific research.
人工智能(AI)正在迅速彻底改变我们的日常生活,因为它能将平凡的任务自动化,提高生产率,并改变我们与技术的交互方式。我们相信,人工智能将不可避免地很快成为从数据分析到撰写论文等常见研究实践中的重要工具。在这里,我们将探讨这一转变是如何在基于质谱的代谢组学领域发生的,这是一个快速发展的科学领域。代谢组学侧重于研究生物系统中的小分子,为了解代谢过程及其对健康、疾病和生理状况的影响提供宝贵的见解。随着测序技术的显著进步和对微生物组的探索,测序与代谢组学的结合为了解生物的复杂性提供了深远的机遇。结合人工智能将开启新的可能性,并很有可能在未来为科学发现做出贡献。在本综述中,我们将讨论人工智能目前在代谢组学中的作用。我们研究了现有的做法,并对将人工智能融入科学研究的未来方向提出了展望。
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引用次数: 0
Rationale behind phosphate therapy to modulate the gut microbiome and protect against surgery-related infection. 磷酸盐治疗调节肠道微生物组和预防手术相关感染的原理。
Pub Date : 2023-02-01 Epub Date: 2023-09-11 DOI: 10.1530/mah-23-0011
John C Alverdy

Despite major advances in infection control and the ever increasing use of broader spectrum antibiotics in surgery, postoperative infections continue to occur under the best of care and in the best institutions. Postoperative infections, also termed "surgical site infections (SSIs), can range from superficial wound infections to deep organ space infections. SSIs can be superficial and only require medical treatment (i.e antibiotics), whereas others such as deep organ space infections resulting from an anastomotic leak can require multiple surgeries leading to sepsis and occasionally shock and death. Many if not most stakeholders in the field including surgeons, infectious disease specialists, infection control nurses, etc., in general advocate the use of prophylactic antibiotics and the enforcement of greater levels of sterility reasoning that all postoperative infections must arise from some type of direct contamination event. In this piece, the alternative view is presented that today, in the era of mandated asepsis protocols, enhanced recovery programs, and enforcement of prophylactic antibiotics in all cases, many if not most postoperative infections and SSIs occur from pathogens endogenous to the patient not from sources exogenous to the patient. It is also suggested that applying broader antibiotic coverage in elective surgery is neither an evolutionarily stable strategy nor inexorable in the context of emerging knowledge in the field of gut ecology. Here this concept is reviewed and the rationale behind using agents that preserve the gut microbiome and attenuate pathogen virulence in lieu of applying broader spectrum antibiotics and greater levels of sterility.

尽管在感染控制方面取得了重大进展,并且在外科手术中越来越多地使用广谱抗生素,但术后感染仍在最好的护理和机构中发生。术后感染,也被称为“手术部位感染(SSIs)”,范围从浅表伤口感染到深器官间隙感染。SSIs可以是浅表的,只需要药物治疗(即抗生素),而其他如吻合口瘘引起的深部器官间隙感染可能需要多次手术,导致败血症,偶尔还会导致休克和死亡。该领域的许多(如果不是大多数的话)利益相关者,包括外科医生、传染病专家、感染控制护士等,普遍主张使用预防性抗生素,并加强无菌水平,认为所有术后感染都必须由某种类型的直接污染事件引起。在这篇文章中,提出了另一种观点,即今天,在强制无菌方案、加强康复计划和在所有情况下强制使用预防性抗生素的时代,许多(如果不是大多数的话)术后感染和SSI是由患者内源性病原体而非患者外源性病原体引起的。此外,在肠道生态学领域新兴知识的背景下,在选择性手术中应用更广泛的抗生素覆盖范围既不是进化上稳定的策略,也不是不可阻挡的。在这里,我们回顾了这一概念,以及使用保护肠道微生物组和减弱病原体毒力的药物来代替应用更广谱的抗生素和更高水平的无菌性背后的原理。
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引用次数: 0
Sex hormones, sex chromosomes, and microbiota: Identification of Akkermansia muciniphila as an estrogen-responsive microbiota. 性激素、性染色体和微生物群:粘蛋白阿克曼菌作为雌激素反应性微生物群的鉴定。
Pub Date : 2023-01-01 Epub Date: 2023-10-09 DOI: 10.1530/mah-23-0010
Anil Sakamuri, Pritam Bardhan, Ramakumar Tummala, Franck Mauvais-Jarvis, Tao Yang, Bina Joe, Benard Ojwang Ogola

Microbiota composition is known to be linked to sex. However, separating sex hormones and sex chromosome roles in gut microbial diversity is yet to be determined. To investigate the sex chromosome role independent of sex hormones, we used the four-core genotype mouse model. In this mouse model, males with testes and females with ovaries have XX or XY sex chromosome complement. In gonadectomized four-core genotype mice, we observed a significant decrease in the levels of estradiol (P<0.001) and progesterone (P<0.03) in female and testosterone (P<0.0001) in male mice plasma samples. Independent of sex chromosome complement, microbial α diversity was increased in gonadectomized female but not male mice compared to sex-matched gonad-intact controls. β diversity analysis showed separation between male (P<0.05) but not female XX and XY mice. Importantly, Akkermansia muciniphila was less abundant in gonadectomized compared to gonadal intact female mice (P<0.0001). In the presence of β-estradiol, Akkermansia muciniphila growth exponentially increased, providing evidence for the identification of a female sex hormone-responsive bacterium (P<0.001).

众所周知,微生物群的组成与性别有关。然而,性激素和性染色体在肠道微生物多样性中的作用尚待确定。为了研究独立于性激素的性染色体作用,我们使用了四核心基因型小鼠模型。在这种小鼠模型中,有睾丸的雄性和有卵巢的雌性具有XX或XY性染色体互补。在切除性腺的四核心基因型小鼠中,我们观察到雌二醇水平显著降低(与性腺完整的雌性小鼠相比,切除性腺的PAkkermansia muciniphila含量较低)(PAkkermensia mucinihila生长呈指数级增加,为鉴定一种雌性性激素反应细菌提供了证据(P
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Microbiota and host
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