Docent Lectures at the VH Faculty

Senior Lecturer Theodoros Ntallaris, DVM, PhD, Dipl. ECAR

Is a cow without any sign of disease really healthy? By every clinical measure, yes. But look at her through a culture plate or a DNA sequencer and you see something else: a crowded community of bacteria, some of them with reduced susceptibility to antibiotics. The cow has not changed. Only the way we look at her has.

Every cow carries bacteria in her reproductive tract, and we know this community matters for fertility. What we do not know is what a healthy one looks like. In a study of 40 healthy dairy cows we found 34 bacterial species, many of them opportunists that are harmless in a healthy cow but can cause disease under the wrong conditions. The picture also shifts with breed, since Swedish Red and Holstein cows carry different bacterial signatures, and with reproductive state, since heifers that became pregnant carried a different mix than those that did not. So “healthy” is not one thing.

The same bacteria also show reduced susceptibility to antibiotics, even in cows with no disease. This is resistance as monitoring programmes measure it: a warning sign, not proof that treatment would fail. Most of the cows carrying such bacteria had never been treated with antibiotics, and these silent carriers are largely missing from resistance monitoring, which has so far looked mainly at the gut and the udder.

My research programme starts from this gap. I want to define what a healthy and resilient reproductive microbiota looks like, where resilience means the ability to return to normal after calving, insemination or treatment. I will follow cows over time and across several farms, combine culture with DNA sequencing, and map the resistance genes. The work rests on three questions: how breed shapes the microbiota, how it changes with reproductive stage and parity, and where resistance hides. Each can grow into a doctoral project.

The goal is practical. With a baseline of normal, we can tell natural variation from early signs of disease, treat more precisely, and use fewer antibiotics, protecting both fertility and the drugs we still depend on.

References

1. Ntallaris, T., Guo, Y., Grundin, J., Nohrborg, S., & Hansson, I. (2026, September 17–19). Bacterial signatures are breed-specific in the anterior vaginal microbiome of cattle [Oral communication]. 29th Annual Conference of the European Society for Domestic Animal Reproduction (ESDAR), Olsztyn, Poland. https://onlinelibrary.wiley.com/doi/epdf/10.1111/rda.70274
2. Ntallaris, T., Hansson, I., Morrell, J. M., & Varga, C. (2025). Silent carriers: Antimicrobial resistance in vaginal bacterial isolates of dairy cattle. Reproduction in Domestic Animals, 60(S3), 5. https://onlinelibrary.wiley.com/doi/10.1111/rda.70096
3. Macleod B., Ntallaris, T., Morrell, J., Hansson, I., Varga, C. (2026). Distribution and antimicrobial resistance profiles of vaginal bacteria in healthy dairy cows. Frontiers in veterinary science. https://www.frontiersin.org/journals/veterinary-science/articles/10.3389/fvets.2026.1761682/abstract
4. Morrell, J. M., Ntallaris, T., Hansson, I., & Cojkic, A. (2026). The seminal microbiome /microbiota and implications for antibiotics in semen extenders. Animal reproduction science, 294, 108347. Advance online publication. https://doi.org/10.1016/j.anireprosci.2026.108347 

Veterinary nursing science is a relatively young field at SLU, with clinical nutrition as one of its focus areas. It goes beyond what animals eat to include body composition, overweight, physical activity and owner behaviour. In this lecture, I will use my research on canine overweight to take you on a journey “from assessment to precision”, guided by the long-term vision of reversing canine overweight.

Overweight is a common problem, affecting 30–40% or more of dogs in Sweden and internationally. Dogs and their owners often share sedentary lifestyles, and perceptions of normal body weight have become skewed, causing healthy-weight dogs to be perceived as underweight and overweight to go unrecognised. This “weight blindness” may contribute to a further increase in overweight prevalence. While the importance of adequate bone and muscle mass is well known for human health, research on canine overweight has focused mainly on fat mass or total body weight. In clinical practice, body condition is assessed using the nine-point Body Condition Score (BCS) system, which classifies dogs from severe underweight to obesity based on visual assessment and palpation. The BCS system is commonly used together with body weight. However, these clinical methods cannot accurately distinguish fat mass from muscle mass, which is important for canine health.

This presents two key challenges: “understanding” and “measuring”. Our research shows that veterinary nurses can address the first challenge, “understanding”, by receiving education in BCS assessment and passing this knowledge on to dog owners to tackle weight blindness. We also need a better understanding of canine overweight metabolism and how physical activity affects the distribution of fat and muscle mass in overweight dogs. To address the second challenge, “measuring”, we are developing more precise tools to complement traditional assessment systems. My long-term vision is to reverse canine overweight through better prevention and treatment, supported by an improved understanding of perceptions and overweight metabolism, as well as new measurement tools.

Simple and precise tools could help clinicians identify improvement or deterioration. Our research has shown promising results using tape measurements of the trunk and limbs and caliper measurements of skinfold thickness. Skinfold thickness measurements can help estimate total body fat percentage, while tape measurements may detect changes in fat and muscle mass distribution in dogs undergoing exercise programmes. Ongoing research aims to refine the BCS system by incorporating such simple, objective body measurements that represent both fat and muscle mass. The future goal is to develop and validate an online prediction tool based on the refined system to measure fat and muscle mass precisely and detect changes in body composition over time.

Looking ahead, I aim to advance veterinary nursing science at SLU by developing my research in clinical nutrition and the education in clinical nutrition provided to veterinary nurses, thereby strengthening preventive care and treatment in the areas of overweight, metabolism and nutrition. The new master’s programme provides opportunities to deepen veterinary nurses’ expertise in clinical nutrition and strengthen their role in clinical quality improvement related to nutrition. By building collaborations with experts in human and animal behaviour, as well as with doctors at human obesity clinics, my research could expand to explore how shared lifestyle choices influence nutrition, body composition, physical activity, health and well-being in dogs and their owners. In the longer term, collaboration between human and veterinary nurses, starting with the shared challenge of overweight, could help establish a new field with broader societal relevance: One Health Nursing Science, connecting human and animal health within the nursing discipline.

Individuals do vary. Obvious as that sounds, it was the starting point for the concept of animal personality, which over recent decades has moved attention in behavioural and evolutionary ecology from the population to the individual, and has since spread to other research disciplines. Although terms and definitions still differ, there is broad agreement that behaviour varies both among and within individuals. Among-individual variation describes consistent differences between animals, whereas within-individual variation describes how much each fluctuates around its own mean. Animals differ consistently in both, with some being far more predictable than others.

Animal welfare is multidimensional. Nutrition, health, the environment and social interactions all influence it through their combined effect on the animal’s mental state. No single measure captures that experience, so welfare is assessed through indicators that relate to variables which can be measured directly, or through proxy indicators that substitute for variables which cannot. It is also an individual outcome, likely influenced by the interplay between an animal’s personality and the conditions it encounters.

There is considerable interest in automated methods for health and welfare monitoring, in both commercial and research settings, with an array of available sensors allowing continuous observation of the individual animal. These range from simple accelerometers and positioning systems to more complex computer vision methods, and they differ in how directly they describe behaviour and how readily they can be applied. Detailed classification stays closest to the behaviour itself but is largely confined to research, while simpler measures serve as proxies yet can be recorded for every animal over long periods. Using repeated measures of individual behaviour makes it possible to study within-individual variation over time, across settings and in response to different challenges, allowing for individual response profiles that can reflect welfare states “beyond the average”. In addition, automated assessment may reveal changes in behaviour too subtle to detect by eye, potentially providing new welfare indicators.

So, what is normal?

In this lecture, I will present individual variation across different types of resources, drawing on examples from my research on dairy cattle and pigs, from where an animal spends its time to the use of brushes. Each relies on a different kind of sensor, and so on a different level of granularity, from broad proxies to more detailed descriptions of behaviour. Across all of them, animals differ consistently in how they use what is available to them. I will also show what emerges when these measures are applied to welfare challenges such as heat stress and disease, drawing on approaches developed in other research areas.

Looking ahead, my research aims to develop methods for quantifying behaviour, health and welfare, to evaluate existing technologies both as research tools and under commercial conditions, and to integrate different data sources in order to understand behavioural variation.