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• v.15(4); Winter 2016

A Case Study Documenting the Process by Which Biology Instructors Transition from Teacher-Centered to Learner-Centered Teaching

Gili marbach-ad.

† College of Computer, Mathematical and Natural Sciences, University of Maryland, College Park, MD 20742

Carly Hunt Rietschel

‡ College of Education, University of Maryland, College Park, MD 20742

Associated Data

A case study approach was used to obtain an in-depth understanding of the change process of two university instructors who were involved with redesigning a biology course to implement learner-centered teaching. Implications for instructors wishing to transform their teaching and for administrators who wish to support them are provided.

In this study, we used a case study approach to obtain an in-depth understanding of the change process of two university instructors who were involved with redesigning a biology course. Given the hesitancy of many biology instructors to adopt evidence-based, learner-centered teaching methods, there is a critical need to understand how biology instructors transition from teacher-centered (i.e., lecture-based) instruction to teaching that focuses on the students. Using the innovation-decision model for change, we explored the motivation, decision-making, and reflective processes of the two instructors through two consecutive, large-enrollment biology course offerings. Our data reveal that the change process is somewhat unpredictable, requiring patience and persistence during inevitable challenges that arise for instructors and students. For example, the change process requires instructors to adopt a teacher-facilitator role as opposed to an expert role, to cover fewer course topics in greater depth, and to give students a degree of control over their own learning. Students must adjust to taking responsibility for their own learning, working collaboratively, and relinquishing the anonymity afforded by lecture-based teaching. We suggest implications for instructors wishing to change their teaching and administrators wishing to encourage adoption of learner-centered teaching at their institutions.

This is the analogy I thought of, the first semester was where you drop a ball on a hard floor, and at first it bounces really high, then the next bounce is a little lower, hopefully it’s going to be a dampened thing, where we make fewer and fewer changes. Alex

It seems to take a village to send a course in a new direction!! Julie

INTRODUCTION

This study documents the process by which instructors transition from teacher-centered instruction to emphasizing learner-centered teaching in an introductory biology course. Weimer (2013 ) defines teacher-centered instruction as lecture-based teaching wherein students are “passive recipients of knowledge” (p. 64). She characterizes learner-centered teaching as “teaching focused on learning—what the students are doing is the central concern of the teacher” (p. 15). Weimer delineates five principles of learner-centered teaching, which are 1) to engage students in their learning, 2) to motivate and empower students by providing them some control over their own learning, 3) to encourage collaboration and foster a learning community, 4) to guide students to reflect on what and how they learn, and 5) to explicitly teach students skills on how to learn. Of note, various terms are used in the literature to refer to strategies that are related to learner-centered teaching (e.g., active learning, student-centered teaching).

The literature suggests that teacher-centered instruction as opposed to learner-centered teaching promotes memorization ( Hammer, 1994 ) rather than desired competencies like knowledge application, conceptual understanding, and critical thinking emphasized in national reports (American Association for the Advancement of Science [AAAS], 2011). Further, lecture-based teaching fails to promote understanding of the collaborative, interdisciplinary nature of scientific inquiry ( Handelsman et al ., 2007 ). Notably, female and minority students have expressed feelings of alienation and disenfranchisement in classrooms using teacher-centered instruction ( Okebukola, 1986 ; Seymour and Hewitt, 1997 ).

A recommended practice that can support implementation of learner-centered teaching is the use of the backward design ( Wiggins and McTighe, 2005 ). The backward design model involves articulation of learning goals, designing an assessment that measures achievement of the learning goals, and developing activities that are aligned with the assessment and learning goals.

Despite robust evidence documenting the superiority of learner-centered teaching over teacher-centered instruction (as reviewed by Freeman et al ., 2014 ), instructors continue to adhere to teacher-centered instruction. A recent study showed that the majority of faculty members participating in professional development programs designed to help them adopt learner-centered teaching practices continue to rely on lecture-based pedagogy as indicated by classroom observational data ( Ebert-May et al ., 2011 ). Possible reasons for such loyalty to lecturing include the following: 1) instructors’ own personal experiences with lecture as undergraduates ( Baldwin, 2009 ); 2) personal beliefs that transmission of knowledge to students through lecture is the best way to teach ( Wieman et al ., 2010 ); 3) the perception that lecture preparation is more time-effective than preparing learner-centered activities ( Dancy and Henderson, 2010 ); 4) student resistance to active learning ( Henderson and Dancy, 2007 ; Seidel and Tanner, 2013 ; Bourrie et al ., 2014 ); 5) initial difficulties are often encountered when transitioning to learner-centered teaching, requiring several iterations to perfect a new teaching style; 6) learner-centered teaching encourages instructors to cover fewer topics in greater depth to promote meaningful learning ( Weimer, 2013 ), and many instructors are uncomfortable with such loss of content coverage ( Fink, 2013 ); and 7) the learner-centered instructor must change his/her role from an expert who delivers knowledge to a “teacher-facilitator,” giving a degree of control over the learning process to students, and many instructors are uncomfortable with the unpredictability and vulnerability that comes with relinquishing control in the classroom ( Weimer, 2013 ). Further, universities oftentimes fail to incentivize and encourage faculty members to prioritize teaching to a similar degree as research ( Fairweather et al ., 1996 ). It has been argued that the professional culture of science assigns higher status to research over teaching, encouraging scientists to adopt a professional identity based on research that typically ignores teaching ( Brownell and Tanner, 2012 ).

Given that many instructors face challenges and intimidation while implementing learner-centered teaching in their classrooms, there is a need to explore their experiences and learn what support instructors need as they engage in the process of transforming their courses. Science education researchers have recently emphasized the critical need “to better understand the process by which undergraduate biology instructors decide to incorporate active learning teaching strategies, sustain use of these strategies, and implement them in a way that improves student outcomes” ( Andrews and Lemons, 2015 , p. 1).

Case studies have been shown as a useful tool to understand change processes ( Yin, 2003 ). A case study approach represents a qualitative method of inquiry that allows for in-depth description and understanding of the experience of one or more individuals ( Creswell, 2003 ; Merriam, 2009 ). Yin (2003 , p. 42) provides a rationale for using single, longitudinal case studies that document participants’ perspectives at two or more occasions to show how conditions and processes change over time. In this study, we used a case study approach to obtain an in-depth understanding of the change process of two university instructors (Julie and Alex) who were involved with redesigning a biology course. The instructors sought to transform the course from a teacher-centered, lecture-style class to one that incorporated learner-centered teaching. We interviewed the two instructors on multiple occasions; we also interviewed a graduate teaching assistant (GTA) and an undergraduate learning assistant (ULA) to gain their perspectives on teaching the course. We explored the motivation, challenges, and thought processes of the instructors during the interviews. We used several data sources in addition to the interviews to build the case study, including class observations by external observers and student feedback data.

Given that faculty members have difficulty changing their teaching, there are recommendations to use theoretical models of change to examine processes of change ( Connolly and Seymour, 2015 ). We looked for theoretical models of change ( Ellsworth, 2000 ; Rogers, 2003 ; Kezar et al ., 2015 ) and found that the innovation-decision model ( Rogers, 2003 ) has recently been used by science education researchers ( Henderson, 2005 ; Bourrie et al ., 2014 ; Andrews and Lemons, 2015 ). Therefore, we decided to use this model to theoretically approach our data. Specifically, we decided to use the adapted model developed by Andrews and Lemons (2015) , which they modified to represent the change process that biology instructors experience when redesigning a course. This model includes the following stages: 1) knowledge, in which the instructor learns about the innovation and how it functions; 2) persuasion/decision, in which the instructor develops an attitude, positive or negative, toward the innovation and decides whether or not to adopt the innovation; 3) implementation, when the instructor behaviorally implements the innovation; and 4) reflection, in which instructor considers the benefits and challenges of using the innovation. On the basis of reflection, an instructor decides to stay with the present version of the implementation or to start the process once again in an iterative manner by seeking new knowledge (see Figure 1 ). According to Rogers (2003) , a condition to begin the change process is that an instructor must be dissatisfied with his or her current teaching approach. Such dissatisfaction is one contributing factor leading an instructor to begin seeking new knowledge about new teaching strategies. Other external and internal factors usually influence an instructor’s decision to change his or her teaching, including release time, institutional commitment, and instructor attitude ( Andrews and Lemons, 2015 ).

Innovation-decision model adapted from Rogers (2003) , Henderson (2005) , and Andrews and Lemons (2015) .

Context of the Study

This study was conducted at a research-intensive university on the East Coast of the United States. The instructors cotaught Principles of Biology III: Organismal Biology (BSCI207). BSCI207 follows two prerequisite courses, BSCI105 and BSCI106. BSCI105 covers molecular and cellular biology, while BSCI106 covers ecology, evolution, and diversity. BSCI207 requires students to synthesize concepts and principles taught in prerequisite courses, apply them across contexts in biology, and generally engage in higher-order learning (e.g., interdisciplinarity, conceptual understanding, quantitative reasoning). The course enrolls between 100 and 200 students per semester.

In Fall 2013, the provost’s office distributed a call for grant proposals encouraging instructors to redesign their courses to incorporate evidence-based teaching approaches. The call specifically required applicants to design experimental studies to evaluate their course redesign approaches in comparison with their usual teaching approaches. Julie and Alex applied for the grant and were funded. Their proposed evidence-based teaching approach was to incorporate a series of small-group active-engagement (GAE) exercises throughout the semester. The traditional section would retain the usual three 50-minute lectures per week schedule. The experimental section would replace one 50-minute lecture with a shortened 20-minute lecture followed by a 30-minute GAE exercise with content matched to the traditional class occurring that day.

The instructors designed the GAEs to accomplish a series of learning goals that were consistent with Weimar’s five principles of learner-centered teaching. For example, one of the GAE goals was to foster collaboration among students in order to mimic the scientific process of inquiry. This goal was in accord with Weimer’s (2013 ) learner-centered teaching principle of collaboration, creating a learning community with a shared learning agenda, and modeling how experts learn. To accomplish this goal, the instructors implemented the GAEs in a small-group setting and required students to exchange ideas and achieve consensus on a single worksheet.

A second goal, which accords with Weimer’s (2013 ) framework, was to engage students in their learning and motivate them to take responsibility and control over their learning process. For example, one of the GAEs asked students to complete a humorous, fictional case study involving a spaceship captain and deadly neurotoxins. In this activity, students needed to use mathematical equations to calculate membrane potentials and to create simulations of conditions that impact membrane potential. Another activity was to collaboratively create a plot of ion transport rate versus concentration. Students were given a computer simulation that they used to generate data; they then entered the data into a Google documents Excel spreadsheet. This created a classroom database that was used to build the plot, which the instructor displayed using the lecture hall projector at the end of class. This activity involved multiple components of learner-centered teaching, including collaboration, student engagement, and student responsibility for learning. Detailed descriptions of a selection of GAEs are published elsewhere ( Carleton et al ., in press, 2017 ; Haag and Marbach-Ad, in press, 2017 ).

The provost grant offered funding that could be used for various purposes. The instructors decided to use the funding for summer salary to develop GAEs and to pay for support from a science education expert. Grant awardees were required to participate in Faculty Learning Communities (FLCs) and teaching workshops arranged by the campus teaching and learning center.

In Fall 2014, the instructors started to implement their experiment. Jeffrey, a third instructor, joined Alex and Julie to teach both sections; each of them was responsible for teaching several topics associated with their specific research expertise. In the GAE class, students were divided into small groups to complete a learning activity pertaining to the course topic. In total, 12 GAE sessions were held during the semester. Both GAE and traditional classes were taught in large auditoriums. For each GAE session, students self-selected into groups of three to five students. Four GTAs circulated among the groups to facilitate group work. Students were asked to leave empty rows around their respective groups to allow GTAs to move throughout the groups. This same topic was covered only by lecture format in the traditional class.

In Fall 2015, the instructors no longer conducted a comparative experiment while teaching. Julie and Alex continued to coteach the course with the GAE format with many modifications to the activities and other aspects of the course (see Results ). Jeffrey continued to teach a different section of the course independently. Henceforth, we will describe the experience of Julie and Alex in their process of transforming the course.

Teaching Staff

Julie and Alex are associate professors. Lisa is a doctoral-level teaching assistant (TA) in the biology department. Lisa was a GTA in the Fall 2014 and Fall 2015 semesters. Jason was a freshman student in the GAE section of the Fall 2014 semester. In Fall 2015, Jason served as a guided study session (GSS) peer leader in BSCI207. GSS leaders are students who have taken a course on implementing evidence-based teaching approaches, and who have also completed the course they are tutoring with a high grade. GSS students are expected to facilitate small-group discussions outside class. Jason also volunteered to attend all GAE sessions to help facilitate.

Data Collection Instruments

Yin (2003) notes that multiple data sources are important in building case studies. As such, we use interview data, class observations, student feedback on the course, and information written in the grant proposal.

Interview Protocol.

Julie and Alex were interviewed independently immediately following Fall 2014 for 20 minutes each. Julie was also interviewed independently in the beginning of Fall 2015 for 1 hour. Julie and Alex were interviewed together immediately following Fall 2015 for approximately 1 hour. Lisa and Jason were also interviewed following Fall 2015 for 20–30 minutes each. We used semistructured interview protocols (see the Supplemental Material) with additional questions to probe for clarification. The questions probed participants’ motivation for change, attitudes toward change, barriers and challenges, administrative supports, details about the implementation, and teaching philosophies.

Class Observations.

Two independent raters conducted class observations. Each year, raters attended six classes. In Fall 2014, they observed GAE class sessions and the parallel, content-matched class sessions that took place in the traditional class (overall 12 sessions). This procedure allowed the raters to compare the class sessions covering the same material but with differing teaching approaches (i.e., learner-centered vs. teacher-centered instruction). The two raters attended each class session together. Once in the class, the raters used a rubric to evaluate the class. In Fall 2014, raters used a rubric based on a previously constructed rubric that was created by the biology department for peer observations ( http://extras.springer.com/2015/978-3-319-01651-1 , in SM-Evaluation of teaching performance.pdf). In Fall 2015, to better document group work, the raters used the rubric developed by Shekhar and colleagues (2015) .

Student Feedback.

Students were invited to reflect on GAEs by providing anonymous written feedback on note cards following the activity. We use some of these data in the present study.

Data Analysis

Interviews were conducted by a science education researcher, audiotaped, and transcribed. A science education researcher and a doctoral student in counseling psychology separately analyzed the interviews and the note cards to define emergent themes. Then, they negotiated the findings until they could agree upon the themes ( Maykut and Morehouse, 1994 ). The instructors were shown the interpretation of data to verify accuracy of interpretations. We present the results in accordance with the adapted Rogers (2003) model presented in Andrews and Lemons (2015) . We slightly adapted the Andrews and Lemons (2015) model to the iterative process through which our instructors progressed to modify the course (see Figure 1 ).

Motivation for Change

Before 2014, the traditional BSCI207 class as taught was a three-credit course with three 50-minute lectures per week. Alex described the traditional course:

Before the GAEs came into being, we taught in the very standard, traditional lecture. We used mostly PowerPoint to show text and images, occasionally we would bring a prop in, like sometimes I would bring a piece of a tree to gesture towards as I was lecturing about water transport or something like that. But it was basically standard lecture.

The instructors were dissatisfied with the traditional lecture format for the following reasons:

• Evidence for inferiority of teacher-centered instruction compared with learner-centered teaching . The instructors expressed awareness of the empirical data documenting the superiority of learner-centered teaching over teacher-centered instruction, “There’s a lot of research that suggests that [teacher-centered instruction] may not be the best way to help the students understand what we’re trying to get them to understand” (Alex).
• Lecture hinders understanding of the process of science. The instructors also expressed a desire to get students to learn the process of science early in their education, rather than to passively receive information. “We are being asked as science professors more and more to try and get our students to understand that science is a process, earlier and earlier in their career, and to model what real science is like in their education” (Alex).
• Lecture promotes overreliance on memorization. The instructors discussed a goal to modify the course so as to decrease focus on memorization and increase emphasis on problem solving and conceptual understanding. Julie described: “BSCI207 is the biology majors’ class, and it’s a lot of what the pre meds are taking, and so, critical thinking I think [is important], we’re constantly trying to get them to not just memorize and regurgitate but to put the ideas together.”

We also rearranged the material. So they [the lectures] used to be in a taxonomic orientation, I would give a whole lecture titled the biology of fungi, and the students complained that this taxonomic focus seemed to resemble the structure of BSCI106 [the prerequisite course]. I decided to explode those taxonomic lectures, and take the bits of content that I still thought were valuable, and spread them into other parts. So for example the stuff on mating types, which is wacky and interesting to me, and I hope to the students, is now in a lecture on sex. And they don’t realize half the lecture is on fungi. So they’re susceptible to packaging I think, and we don’t get the complaint any more that the course is redundant to BSCI106 (Alex).

Organisms don’t care about our disciplinary boundaries of research. The organism doesn’t understand that there’s biophysics, and biochemistry, and evolutionary biology, and ecology, and genetics. All these attributes of their biology have to function simultaneously on several different spatial and temporal scales … if we think they do, then we continually miss things that otherwise would fall out naturally if we were a little less wedded to our disciplines.

Relatedly, the instructors noted that most students enrolled in BSCI207 without having taken introductory physics or chemistry, which they thought was preventing students from drawing upon highly relevant concepts (e.g., thermodynamics) from these courses for biology.

• Underrepresented groups do poorly in traditional classes. The instructors quantitatively examined student performance for specific student subgroups (i.e., underrepresented minority students, female students) in previous BSCI207 semesters. They observed that there were disproportionate D/F/W grades for underrepresented students. Coupled with the science education literature documenting the ability of active learning to help underrepresented groups ( Preszler, 2009 ; Haak et al ., 2011 ; Eddy and Hogan, 2014 ), the instructors speculated that adding active learning to the traditional class might help underrepresented students.

In Fall 2014, the instructors went through the process of course revision that follows the adapted model by Rogers (2003) and Andrews and Lemons (2015 ; see Figure 1 ). In the following sections, we discuss their progression through the innovation-decision model. Table 1 shows a summary of the change process for the Fall 2014 semester.

First Iteration of the instructors’ change process

Before the Fall 2014 semester, the instructors engaged in several efforts to increase knowledge about evidence-based teaching approaches to modify the course. The knowledge sources were as follows:

I will go ask [physics education professional] questions. When something doesn’t go well I’ll meet with the postdocs [from physics education research group (PERG)] over there and say, what are they not getting here, how can we make this better, so I’m always trying to get resources to help.

• Reading the science education literature. As a new instructor, Julie participated in the college workshop for new instructors. The workshop was led by the director of the teaching and learning center, who provided several resources for using evidence-based teaching approaches, including an article giving an overview of learning styles ( Felder, 1993 ), a book on teaching tips ( McKeachie and Svinicki, 2006 ), and the book Scientific Teaching ( Handelsman et al ., 2007 ). In her interview, Julie commented, “So I read a lot of books,… I think it was getting students to think about math, I read one of the books [that the director of the college teaching and learning center] had given me [ Scientific Teaching ].”
• Observing other instructors teaching. The instructors had observed another instructor who implemented evidence-based teaching approaches in a small class of BSCI207 (<40 students). This pilot implementation was successful, and the instructors were interested in investigating whether the learner-centered teaching model used could be scaled up to a large-enrollment class.

Persuasion/Decision.

Following the knowledge-generation phase, the instructors felt prepared to change their teaching to a more learner-centered teaching style. They decided to conduct a comparative experiment during the first implementation of the GAEs (i.e., traditional vs. GAE classes; see Marbach-Ad et al ., in press, 2017 ). Although the instructors were aware of the literature documenting the effectiveness of learner-centered teaching, they had several reasons to execute the experiment:

• Obtain evidence for overall effectiveness. The instructors were unsure whether their activities were the best way to change the course (e.g., they were unsure of the challenges that would emerge, how the intervention would impact students). The instructors also wished to explore cost-effectiveness, since they knew that changing the course would require a high instructor time commitment.
• Convince colleagues to adopt learner-centered teaching approaches. The instructors noted that faculty in the department were unconvinced of the superiority of learner-centered teaching approaches, and they thought that a comparison study bringing empirical evidence might demonstrate that changing one’s teaching style is worthwhile. Alex stated, “[A] lot of my motivation for this experiment was to try to provide some evidence that these approaches were worth the effort, and because there is resistance clearly, from some of our colleagues who have been teaching the course for a long time.”
• Respond to grant award requirements. As mentioned earlier, the institution announced a call for proposals for instructors to revise their teaching. The instructions required applicants to propose comparative experiments during course revision to document effectiveness.

Implementation.

As proposed in the provost grant application, the instructors executed the comparison study. In the traditional class, instructors delivered a 50-minute lecture three times per week. In the GAE class, one lecture was replaced with a GAE. The GAE consisted of a brief 20-minute introductory lecture (a short version of the lecture presented to traditional class students) and a 30-minute group activity. As scientists, the instructors wished to manipulate the addition of the GAE day only and to keep remaining variables constant across classes. Therefore, homework assignments, examinations, optional computer tutorials, and office hours availability were consistent in both classes (see Table 2 ).

Fall 2014 class comparison

In the GAE class, on the day of the GAEs, students were instructed to sit with groups of three to five students (of their own choosing) and to leave empty rows between groups. Students were asked to have at least one laptop per group. As discussed previously, the GAEs were designed to be more learner centered relative to traditional lecture classes. To illustrate this here, we give Alex’s description of the membrane transport GAE: “The students had a little computer simulation, and they used that to generate data that they then entered into a Google docs spreadsheet in real time in the class, and there were enough students in the class that their responses produced this beautiful textbook plot of transport rate versus concentration. They built that relationship in a way that otherwise I would have just told them.”

Reflection.

Following the Fall 2014 semester, the instructors reflected on the various pros and cons of the learner-centered teaching intervention in the interviews. Observers and students also provided feedback that was used by the instructors to reflect on both sections of the course and on the comparative experiment. Several themes emerged from these data:

It’s much less about my spouting facts, it’s about my thinking ahead of time to get them to draw conclusions and get them to cement ideas. My role was partly just to control the chaos sometimes, and to control that the TAs had the information they needed so they could provide guidance to the students.

Importantly, observers noted that the instructors were very actively engaged with student groups throughout the GAEs, helping students to work through problems and understand concepts. Julie also commented that teaching with GAEs requires greater proficiency with material than lecturing: “To use these activities, you have to know the material better than if you’re going to straight lecture. And I think some instructors are maybe still learning BSCI207, what is all the material in it. And until you teach it straight a couple of times you probably don’t have the background to really understand.”

We spent less time talking about dating the origins of life using various methods (fossil record, carbon dating); we got rid of a lecture on prokaryotes and had to shrink some of the nutrient assimilation information from two lectures to one.

The instructors explained that, in order to minimize loss of content coverage, they decided to have a GAE class only once per week and to pick GAEs corresponding to lecture topics for which “there was the least amount of lost material by focusing on a particular exercise” (Alex). An additional solution was to move in-class lectures to online, preclass lectures. Julie described this change: “We also ask students to review some of the material that is lost during lecture time into the prep slides they review ahead of time.” However, Julie wondered whether students would benefit from online lectures to the same degree as in-person lectures: “I am still worried they don’t get so much out of those [online lectures] and so miss much of that information.”

• Engagement in learning. Overall, the instructors reflected that most GAEs provided a space for students to interact with one another, TAs, and instructors: Julie added, “I think it was nice to see the energy in the class and the way the students took to the activities, it was different for them.” Observers noted that the GAE class treatment condition was usually associated with increased student interactivity. Specifically, they noted that students in the GAE class were not only more engaged in the GAEs, but that they also tended to raise more questions during the PowerPoint presentations relative to students in the traditional class. Students reflected on their note cards following GAEs, and in the end-of-semester survey, noting that they felt that many of the GAEs were engaging (see Marbach-Ad et al ., in press, 2017 ).
• Giving students control over learning. The instructors noted, “The GAEs represented a chance to turn the class over to the students for some part of the time, where they could do something actively, instead of just sitting there listening to us” (Alex).

It’s actually a bit more how real science works, right, even as somebody who runs a lab, I don’t go into my lab and sit there and talk to my graduate students for four hours, I mean we have a brief conversation about how they should tackle something, and then they go off and work more on it. So it’s more of a checking in and then separating again. That’s kind of how this class works, the GAEs do give the students a little more of a feel of how collaborative real science works, and how no one person is sort of dictating everything, everyone needs to be a bit independent. … I think that this active model gives the students, for the first time, a real taste of how a real scientist would approach a problem.

Students commented on the opportunity afforded by GAEs to take an active role in their learning: “I learned how to apply what we learn in lecture class to actual problems”; “I kind of felt like a real scientist since I was put in a situation in which I had to make a hypothesis myself.”

• Disengagement. The instructors noted that, for some GAEs, students were disengaged. For example, in the GAE on stress and strain, two students were doing measurements in front of the class for 10–15 minutes, and the remaining students were instructed to input data into Excel files. These data were then used to make calculations. Students also expressed their dissatisfaction with this activity on the note cards that they handed in to the instructors: “I feel I understood the concept well once Dr. Julie wrote the plots on the board. This activity was more tedious and like busy work”; “ We could have easily compared values without experimentally finding them. I didn’t feel this deepened my understanding of concepts.”
• Insufficient time for reflection. The instructors noted that most exercises were too long, which did not leave sufficient time for reflection. Alex noted, “Well I think also making sure that if we get the exercise done in the right, short amount of time, then that does give us time to add a reflection at the end. Connecting the results of our exercise back to some larger idea.”
• Student preparation. The instructors felt that students would gain more from the exercise, if they were to come to GAE classes with better understanding of concepts relevant to the GAE. Then, more time could also be allotted for summary and reflection on important concepts. Alex commented, “We probably will need the students to do a bit of preparation before they come in to these active exercises, so that we can spend less time setting it up, and more time summing it up.”
• Assessments and grading misaligned with GAEs. In this implementation, instructors kept the same assessment plan for both the traditional class and the GAE class in order to compare achievement across classes. This resulted in a mismatch between the course activities and the assessments in the GAE section. For example, there were no final examination questions specifically covering GAE material. Of note, the instructors analyzed their final examination questions before conducting the experiment and saw that the questions required students to demonstrate high levels of thinking ( Bloom and Krathwohl, 1956 ; e.g., knowledge application, quantitative analysis), and they believed the GAEs would improve students’ abilities in these areas. Further, the instructors did not count GAE participation toward final grades, which instructors and observers believed had a detrimental effect on GAE attendance. Julie noted that “on the GAE days, only 60% of the students would come. That was partly because they wouldn’t get any credit for it, and they weren’t seeing that it was helping them learn the material better.” Analyses showed that students with higher grade point averages (GPAs) were those who chose to attend on the GAE days (see Marbach-Ad et al ., in press, 2017 ). Given this, the instructors felt that attendance should be incentivized in future implementations of the learner-centered teaching intervention to motivate and benefit a wider range of students.
• Resistance to learner-centered activities. The instructors felt that students’ low attendance specifically on GAE days may also have been because the students did not perceive the benefit of GAEs for their learning. “I feel sort of parental here, maybe the GAEs are like broccoli and brussels sprouts, they need them, they just don’t know it yet” (Alex).
• Group dysfunction. The instructors and observers noted several issues with the groups. Some groups were not engaged, and some students were not participating within their groups (e.g., one student would be left out). In some activities, some groups would finish the activity very quickly and would subsequently appear bored and waiting for further summary or instruction. Julie was frustrated with these occurrences and noted, “People would be sitting there on their phones.” One reason for student disengagement could be that students groups were unassigned and could include different students each week: students “would sit and associate with whoever was around them” (Julie).
• Auditorium-setting challenges. The instructors commented on the difficulty of doing GAEs in the large auditorium: “It’s still tricky to think about how you actually stage all of this, there is a bit of theater to running a large class with 200 students, how you move from one aspect of the process to another [lecture to group activities] quickly, without losing people, without too much noise and disturbance” (Alex).
• Little impact on grade distributions. Alex and Julie were hopeful that the GAEs would lead to large improvements in students’ grades as compared with traditional learning. However, the effect of GAEs was very small. Alex commented, “This was the biggest outcome from my perspective, and it drove much of the revisions for 2015. This is interesting, as it shows that even though we were unable to realize a big payoff in the first year, we nevertheless saw something that we thought was worth keeping and hopefully improving upon.”
• TA training required. The instructors reflected that they did not provide adequate TA preparation for the GAEs: “We hadn’t really prepared the GAEs enough ahead of time so that we could talk about them with the TAs. The TAs at times were really clueless about what was supposed to be happening” (Julie). TAs, although instructed to guide and facilitate groups, apparently lacked the skills to engage students, as observers noted that most of them passively waited for students to ask questions rather than actively approaching students with questions, instructions, etc.

On the basis of their reflection, Julie and Alex decided to continue teaching with GAEs and to seek new knowledge to improve GAEs. In the following sections, we discuss their continued progression through the innovation-decision model (see Figure 1 ). A summary of the change process in Fall 2015 is shown in Table 3 .

Second iteration of the instructors’ change process

• Learn about methods to form successful groups. The instructors reviewed the literature and consulted with the director of the teaching and learning center and other faculty members in the department to form new strategies on building effective groups in auditorium settings. The literature shows that groups work best when they are permanent and students are held accountable to other group members ( Michaelsen and Black, 1994 ; Michaelsen et al ., 2004 , 2008). The literature also shows that taking student diversity into account is important in creating successful groups ( Watson et al ., 1993 ). For example, Watson and colleagues (1993) reported that, although it takes time, heterogeneous groups outperformed homogeneous groups on several performance measures, including generating perspectives and alternative solutions. The instructors also learned from the director of the teaching and learning center about the Pogil method ( pogil.org ), in which students are assigned different roles during group work (e.g., recorder, facilitator). They weighed the pros and cons of implementing this method in the classroom.
• Learn about methods to flip courses. The instructors learned from models of flipped classes ( Hamdan et al ., 2013 ; Jensen et al ., 2015 ), which highlight how to capitalize on out-of-class time to cover material to prepare for face-to-face active learning. In this regard, instructors sought assistance from the information technology office about presentation software (i.e., Camtasia) that can deliver automated lectures effectively.
• Seek expert guidance. During the summer, the instructors again consulted with science education experts to enhance the GAEs. For example, they consulted with a science education expert on how to revise the concept map assignment. Julie described how this guidance helped her “leave the activity a bit more free form and get the students to make a graphic organizer of their own design rather than trying to fill in some pre-designed boxes.” As another example, the science educator recommended strategies about how to streamline GAEs to maximize time spent on developing conceptual understanding and minimize time spent on the mechanics of exercises.
• Learn about strategies to enhance TA support. The instructors wished to decrease student to TA ratio. However, GTAs require departmental funding, which was unavailable. The teaching and learning center director and the biological sciences administration offered to involve ULAs who are unpaid but receive alternative benefits, such as leadership and teaching experience and undergraduate course credit. This model was reported to be successful in our university ( Schalk et al ., 2009 ) and in other institutions ( Otero et al ., 2010 ).

Following reflection on the comparative experiment, instructors sought to keep improving the course and decided to make several changes:

• Teach all sections with learner-centered teaching. Although the instructors reported that keeping the GAE class format requires more time to prepare relative to lecturing and takes time from their research (“fine tuning the GAEs—that took weeks” [Julie]), they decided to implement the GAEs in all sections and to work to improve them.

[Last semester] I had a couple of students up front doing the experiment, and everyone else was kind of twiddling their thumbs while we gathered the data. We talked about the data but we didn’t really have time [to do data analysis and summarize concepts]. I think this year I’m just going to give them last year’s data, and have each group do some analysis.

As another way to modify GAEs, instructors decided to utilize more outside resources such as published, case-based activities. Julie described, “I’d love to come up with some more case studies that we could do. You know the Buffalo site [ http://sciencecases.lib.buffalo.edu/cs/collection ] has all the case studies for all the science classes. So I’m constantly perusing that. A couple of the GAEs that I developed actually come from there.”

I haven’t figured out what the best prep work is. What Alex has been doing is taking the slides he showed last year and just posting them online. I’m not sure that’s the best, or really enough.… But, then they just read. I mean he tries to put more words on them. I tried to find some videos that I thought were appropriate, and I’m not sure that’s any better. I was going to do some of these with Camtasia. In this way you can actually have the slides and actually talk over them and record. But I couldn’t make the software work. I haven’t really gone there yet, I will have to figure that out.

To encourage students to prepare for the GAEs, the instructors decided to give a preclass quiz covering the out-of-class preparatory materials. Julie described,

We’re also doing a quiz this time, we’re giving that preparatory information, they have to have done it by the morning before, they have to take a little 2-point quiz [before class] to show that they’ve covered that material. Then we have the whole class time [for the GAE] so that we’re not so rushed in trying to do to many things at one time.

• Train the TAs better, add ULAs, and involve both teams in the process of GAE development. The instructors decided to expand the team of assistants to decrease the ratio between students and TAs. Julie described the change from Fall 2014 to Fall 2015: “We have a bigger team. We have two of these ULAs, and then we have three UTAs, and two GTAs. So a team of seven helpers, and each person has a different job. The ULAs are specifically supposed to be trying out the GAEs ahead of time. So we kind of run things past them. And then we meet with all the TAs, and then talk through the GAEs beforehand. They have an assigned part of the class, where each of them is hopefully seeing the same students over and over, and hopefully getting to work with them to develop a rapport, and they go in the middle of the activity, so kind of checking in, so what do you think, kind of getting students to verbalize.” The benefit of this new format, where each TA was responsible for a subsection of the large class, was that it approximated a smaller class discussion session in which students could get to know their TAs more personally.
• Revise group structure. On the basis of the literature and their previous experiences, the instructors decided to assign permanent, diverse groups of four at the beginning of the semester. They also decided to instruct students on how to sit in the auditorium with their groups (in two rows rather than in a single line, to enhance group communication) and to award points for completing group work exercises.

In the Fall 2015 implementation, there were several changes to the course (for a comparison of 2014 and 2015 GAE classes, see Table 4 ).

GAE class comparison between Fall 2014 and Fall 2015

• Modify the activities. The instructors devoted a full weekly class period to the GAE instead of 30 minutes. On the basis of their experiences in the previous semester, they revised some GAEs and adapted them to the time frame. Although they had more time for the GAEs, they wished to make them more efficient and interactive: “I think we had to cut some, with the GAEs, because they were taking way too long, but I think in a few cases we simplified them, took out 1/3 of them or something” (Alex). Instead of the 20-minute pre-GAE lecture that was presented in the Fall 2014 implementation, students were asked to prepare for activities at home by watching videos, reviewing lecture slides, and reading textbook materials. In contrast with Fall 2014, the students were awarded three points for participating in the GAE activity and two points for completing a quiz covering preparatory materials that was due before the GAE class. The instructors wished to assign points to these activities in order to “really give them weight” (Julie). “[The activities] formed a large part of the exams as well. So making the activities more integral to the class was a big change” (Julie).

So the next time I drew a map in the room, [which showed] two students in the front, and two students in the back. When you have a very formal auditorium, you have to try and help them assort with each other and talk with each other. The other thing we did was, we were giving each group two copies of the assignment, so they didn’t each have one. So that kind of helped, that kind of had them sharing things.

Finally, TAs and ULAs were assigned to stay with one section of the lecture hall throughout the semester. Thus, TAs and ULAs developed a rapport with a large group of students throughout the semester and were able to learn their names, which facilitated communication.

• Add more and better-trained TAs. Before Fall 2015, the instructors trained the TAs to better engage with student groups in class. Class observation data showed that, in Fall 2014, some TAs were lacking in their ability to engage actively with students. One observer described, “When I observed the classes last year [Fall 2014], they [TAs] were standing in the side [of the auditorium], and sometimes they got to students, but just students that raised their hands. They weren’t active. They were very passive, most of them, because they didn’t know what to do.” Following the implementation in Fall 2015, the observer noticed a change in TA involvement: “Now, it’s more about instruction, they circulate between groups and encourage them to ask questions, they encourage students that aren’t participating … it’s not enough to throw them [the TAs] in the classroom.”

Overall, instructors noticed improvements in the areas that they targeted to improve, and they also felt there were areas that they wished to continue improving.

• Student preparation. Julie described that although new techniques were put in place to increase student preparation, students often seemed unprepared for the activities: “And my data for that is essentially for the first 20 minutes of the GAE they would spend saying, what are we doing? There was a lot of flailing. It took them a lot longer to get going with the GAE than I thought, and I’m not sure if that’s because the preparatory material is not really preparing them, or that they just took the online quiz and didn’t really go through the preparatory material.” Julie thought about changing the nature of the preparatory lectures, “I would still like to explore turning those into little online lectures rather than having them read the slides.”
• Student attendance . Alex commented that the strategy of assigning points to participating in GAEs “made a big difference in attendance […] by incentivizing their attendance, at least on GAE days, they were coming.” The instructors commented that incentivizing participation in the GAEs and the preactivity quizzes increased the amount of student–instructor interaction regarding point grabbing. Alex stated, “The downside of associating points with everything is that I think we spent the largest fraction of our student interaction time dealing with the points related to the GAEs, excused absences, non-excused absences, anxiety about the points, I mean these are tiny amounts of points, but the students took it very seriously. But I think it was one of the top 3 issues that students came up with this semester.”
• Mechanics of exercises. Julie was very frustrated with how students could not effectively operate Excel software: “And they still don’t know Excel. My biggest frustration was that I thought Excel would make their lives easier, and it made their lives harder. I’m almost ready to go back to pencil and paper, just to get them to plot things and think about things, because they’re not getting back to the scientific inquiry and hard thinking, they’re just so stuck in which box do I click.” Alex added, “My issue is that the preparatory materials do a good job preparing them intellectually for what’s the point, but then they do get stuck on the mechanics, what they’re doing with their hands.”
• Allocating time for reflection. The instructors described that they improved substantially in the area of summarizing major concepts and timing activities: “I think we did a pretty good job of every 15 or 20 minutes bringing them back together and saying ok, you would have done this by now. There were a couple that worked really well, and a couple where we were still pressed for time. I think that generally it was far improved” (Julie). Because the instructors had the full class period to devote to the GAE and did not need to compare learner-centered teaching with teacher-centered instruction, they felt that the timing of the activities was much improved. Julie noted, however, “I always overestimate what students can do. I’m still adjusting.”
• Technical issues. There were difficulties with connecting to the wireless Internet in the lecture hall, particularly among students who failed to download the appropriate tools before coming to class. Further, students have different types of computers and software programs and knowledge of software programs required for the course.

The organized approach helped students see the material as well as make a few friends, in fact, I remember coming onto my dorm floor and seeing four people from my class working together, and they were actually in that GAE group, they had made a study group because they were used to working together. One of the aims of this project gets students communicating instead of competing.

Julie commented that there is still room for improvement in the student groups: “I saw a number of groups where at least one person would be left out. I don’t know if that’s a physical orientation, if we could point them toward each other it would be better. Next year one thing we talked about is going to groups of 3, because with 3 you can always get across each other and be more … everybody can talk to each other.” The instructors considered the benefits of the Pogil. Julie explained that they tried to appoint a different group member to act as the scribe each week during GAE activities as a way to increase student participation in groups. The instructors did not strictly enforce this policy, as they were not sure it was beneficial.

And we also had some undergraduates this year, … and I think they were really helpful because they understand what the students are capable of, more than we do … a lot of times they can give you some insight into what’s going on or what classes undergraduates are most likely taking at the same time. It was very helpful.

Finally, Lisa felt that the level of engagement among the teaching staff was higher than for a standard lecture course: “Everyone was very engaged, it’s a unique class to TA for, because I feel like the TAs and the professors are far more engaged than in a standard lecture course, so it was kind of nice.” Alex reflected that, in the future, “It would be even better,” since they will have “a whole floor of ULAs that had us for 207,” and they “will be well-positioned” to assist in the redesigned course.

This case study examines instructor change processes when moving from teacher-centered instruction toward learner-centered teaching. In this study, we examined the change process through the lens of the innovation-decision model ( Rogers, 2003 ; Andrews and Lemons, 2015 ), which recognizes several stages of change: knowledge, decision/persuasion, implementation, and reflection. The model is iterative, recognizing that transforming courses may require multiple revisions as instructors reflect on the inherent challenges and imperfections that arise when changing a course ( Henderson, 2005 ). Consistent with this literature, the first implementation of learner-centered course revision was fraught with imperfections, and the instructors persisted through two rounds of course revision before gaining satisfaction with their teaching approach, although they plan to continue enhancing the course with each semester.

Andrews and Lemons (2015) note that dissatisfaction with one’s current teaching approach is an important motivator leading instructors to change their teaching. Our instructors were dissatisfied with the lecture mode of teaching in their courses due to personal dislike for it, and the sense that it encouraged student reliance on memorization and hindered interdisciplinary thinking. Other motivators for change included 1) awareness of national recommendations to use learner-centered teaching ( AAAS, 2011 ); 2) a hope that underrepresented students would benefit from learner-centered instruction, based on education literature documenting such benefits ( Okebukola, 1986 ; Seymour and Hewitt, 1997 ); and 3) institutional support (i.e., a provost office grant initiative).

These motivations led the instructors to seek new knowledge about learner-centered teaching approaches and how to implement them, which, according to the adapted innovation-decision model ( Andrews and Lemons, 2015 ), is a first step toward changing a biology course. In the present study, knowledge-seeking strategies included consultation with science education experts and information technology experts, reading the empirical literature, observing other faculty members who had adopted evidence-based teaching practices, and involvement with a discipline-based FLC. Following the knowledge stage, the instructors progressed through the decision/persuasion and implementation stages of change. In the reflection stage, the instructors discussed what worked well, challenges, and areas they wished to improve in the subsequent iteration. We present here implications from this study for instructors seeking to change their courses, and also for administrators wishing to promote learner-centered instruction at their institutions.

IMPLICATIONS FOR INSTRUCTORS

Weimer (2013) noted that engaging students in their own learning is messy, unpredictable, and challenging as compared with teacher-centered instruction. The process can be difficult for the faculty members who want to change as well as for the students. First, the instructor must adopt a new role as “instructor-facilitator” ( Weimer, 2013 ), giving up a degree of control to the students to take responsibility for their own learning. Relating to their new role, our instructors reported that, on the one hand, the instructor-facilitator role felt like controlling chaos at times, particularly in the beginning, but that it was markedly beneficial for student learning and for their own teaching. For instance, it gave students an opportunity to be independent learners and to engage with their peers in collaborative problem solving, more closely modeling the process of science. Thus, although it may be intimidating to share control over the learning process with students, it appears that there are benefits for both students and instructors.

Second, learner-centered teaching encourages instructors to cover fewer topics in greater depth, as opposed to more topics in less depth ( Weimer, 2013 ). Despite being uncomfortable with losing content coverage due to the function of BSCI207 as a preparation course for the MCAT and a prerequisite, our instructors decided to remove some course topics and consolidate others into shorter units. Next, they implemented several solutions to the necessary loss of content coverage. First, they moved lecture content to required preclass, online lectures that substituted for in-class content coverage. Second, they were strategic about which course topics they used to redesign as GAEs. Specifically, they selected course topics that were historically conceptually challenging for students (e.g., membrane transport). Our faculty members’ transition process provides an example of how faculty members can identify and implement solutions for concerns about loss of content coverage.

Third, a fundamental principle of learner-centered teaching is to encourage collaboration in the classroom ( Weimer, 2013 ). To this end, our instructors implemented GAEs, a series of group work–based activities. Student collaboration is important, because it promotes sharing of the learning agenda ( Johnson et al ., 1984 ; Weimer, 2013 ), and collaboration is a skill that is essential for the workplace ( Hart Research Associates, 2015 ). Group work is a common and accessible strategy that instructors can use to increase learner-centered teaching in their classrooms. Our instructors experienced various challenges and implemented several revisions to group work activities throughout their change process. The most successful strategies for optimizing group work included 1) increasing the number of TAs and the amount of TA training; 2) creating diverse and permanent student groups to increase accountability ( Michaelsen et al ., 2004 ); 3) assigning grades and preparation assignments for group work activities; and 4) restructuring group work activities to provide more time for whole-class summary and reflection on concepts. Group work is just one type of teaching strategy that can increase learner-centered teaching. Each instructor needs to discover what kinds of approaches are most suitable to increase their level of learner-centered teaching. When selecting and implementing new teaching strategies, it is highly recommended to seek guidance from experts, more experienced faculty members, or from a teaching and learning community.

Transitioning away from lecture-based instruction to learner-centered instruction can be challenging for students as well as instructors. The literature has shown that students resist many learner-centered approaches that require them to engage in the classroom rather than sit anonymously in lecture ( Michaelsen et al ., 2008 ; Shekhar et al ., 2015 ). Our instructors learned about student resistance through several means: 1) student feedback that was collected on note cards at the end of GAE classes, 2) end-of-semester surveys asking students to reflect on each activity, and 3) low attendance on GAE days as compared with lecture class days. It is important for instructors transitioning their courses to monitor student resistance and satisfaction, as our instructors used these data to modify the activities from the first to second iteration.

The instructors used several strategies to reduce student resistance. First, through student feedback, instructors learned that they needed to provide students with better explanations for the purpose of doing GAEs as opposed to sitting in lecture class. Weimer (2013) emphasizes the importance of providing students explicit instruction on how to best learn. Therefore, at the second iteration of the learner-centered implementation, the instructors were explicit about the rationale for the GAEs. At various points throughout the semester, the instructors explained how the GAEs were helpful in enhancing skills (e.g., critical thinking, problem solving, collaboration, understanding the interdisciplinary nature of science, relating course material to everyday life and to scientific research) that are recommended by national organizations ( AAAS, 2011 ) and employers ( Hart Research Associates, 2015 ). Second, instructors awarded class participation points for completing GAE exercises and grades for completing the preclass online quiz. This strategy resulted in better alignment between requirements of students and course assessments, which accords with Wiggins and McTighe’s (2005) backward design theory. This method of GAE grading resulted in much higher student attendance as compared with the first iteration. Third, instructors used evidence-based strategies to reduce resistance within student groups, including creating permanent, diverse groups at the start of the semester. Fourth, instructors took student feedback into account with regard to their satisfaction with specific activities and modified activities with the goal of maximizing student engagement.

IMPLICATIONS FOR ADMINISTRATORS

Given that changing one’s teaching from teacher-centered instruction to learner-centered teaching is challenging, there must be administrative support for these efforts.

First, administrators can play a key role in acknowledging the importance of learner-centered teaching. Historically, universities have failed to encourage faculty members to prioritize teaching to a similar degree as research ( Fairweather et al ., 1996 ). Unfortunately, many tenure-track faculty members at research-intensive universities fear that they may be penalized for investing the time to adopt learner-centered teaching. Research-oriented universities should prioritize teaching in order to support more widespread adoption of evidence-based teaching approaches. Julie reflected on her frustration with the university’s message that teaching is devalued relative to research:

I think for assistant professors, I was actually scolded for putting time into teaching and trying to participate in teaching improvements and so, I think it’s discouraged, perhaps rightly so, because they’re not going to value it, so if that’s going to take away from what’s required to get tenure, to get promoted, they want you to know that. So they’re just being honest perhaps.

As part of a university culture that values learner-centered teaching, administrators (e.g., chairs, promotion committees) should acknowledge instructors who are making the effort to transition their courses and understand if their teaching evaluations are lower during the initial semesters of transition.

Second, as evidenced by our study and by others in the literature, transitioning from lecture-based teaching to learner-centered teaching requires a large time commitment from instructors. Thus, funding and release time are valuable supports that administrators can provide to improve the quality of teaching at their institutions. The provost grant was a fundamental support contributing to our instructors’ success in transitioning a core biology course. Further, the fact that teaching fellowships were awarded from the university provost shows that our research-intensive university is beginning to value faculty members’ adoption of learner-centered teaching. Alex commented on these fellowships:

The message comes through that the university values teaching, otherwise we wouldn’t have these fellowships from the Provost, that’s about as high up as it gets, I mean there is this signal, a voice that says, great, please do this. But then when the rubber meets the road, are you going to get promoted? It is not considered a substitute for quality research productivity as a research-active faculty.

Third, learner-centered instruction requires more human resources relative to teacher-centered instruction (e.g., for grading, facilitating small-group discussions, demonstrations, assisting in revising course activities). Administrators should consider ways to assign more TAs to courses that use learner-centered teaching. TAs and/or ULAs could be compensated through financial means or through other methods like course credit. Our university, for example, has developed a training program for undergraduate TAs, in which they receive training in how to facilitate small groups.

Fourth, in universities where there are state-of-the art facilities for teaching and learning, there should be a priority for courses that adopt innovative teaching approaches. In our university, such facilities are in a state of development, and administrators are planning to incentivize faculty who are using evidence-based teaching approaches by giving them priority to teach in the new, state-of-the art teaching and learning facility, which includes classrooms with round tables, movable seats, and advanced technology.

Finally, universities should provide support for a campus teaching and learning expert and an FLC. These resources were fundamental in the transition process of our faculty members. FLCs may be discipline-based ( Marbach-Ad et al ., 2010 ) or campus-wide ( Cox, 2001 ). FLCs and teaching and learning experts can provide pedagogical and curricular guidance, as well as emotional support for the stressors associated with teaching.

Supplementary Material

Acknowledgments.

This work has been approved by the University of Maryland Institutional Review Board (IRB protocol 601750-2). We thank our teaching team members who participated in the study.

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Proteomic analysis identifies dysregulated proteins in albuminuria: a south african pilot study.

Simple Summary

1. introduction, 2. materials and methods, 2.1. ethics statement, 2.2. the study population, 2.3. clinical laboratory tests, 2.4. urine protein extraction, 2.5. retrospective power analysis, 2.6. data analysis and pathway analysis, 3.1. clinical and demographic characteristics of patients, 3.2. performance of study-specific system suitability-quality control, 3.3. differentially abundant proteins between cases and controls, 3.4. potential markers for albuminuria and normoalbuminuria classification, 3.5. functional enrichment analysis of differentially abundant proteins, 4. discussion, 5. conclusions, supplementary materials, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

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Share and Cite

Khoza, S.; George, J.A.; Naicker, P.; Stoychev, S.H.; Fabian, J.; Govender, I.S. Proteomic Analysis Identifies Dysregulated Proteins in Albuminuria: A South African Pilot Study. Biology 2024 , 13 , 680. https://doi.org/10.3390/biology13090680

Khoza S, George JA, Naicker P, Stoychev SH, Fabian J, Govender IS. Proteomic Analysis Identifies Dysregulated Proteins in Albuminuria: A South African Pilot Study. Biology . 2024; 13(9):680. https://doi.org/10.3390/biology13090680

Khoza, Siyabonga, Jaya A. George, Previn Naicker, Stoyan H. Stoychev, June Fabian, and Ireshyn S. Govender. 2024. "Proteomic Analysis Identifies Dysregulated Proteins in Albuminuria: A South African Pilot Study" Biology 13, no. 9: 680. https://doi.org/10.3390/biology13090680

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The Biology Corner

Biology Teaching Resources

Case Study – Mitosis, Cancer, and the HPV Vaccine

Students in my anatomy class get a quick review of the cell and mitosis. This activity on HPV shows how the cell cycle relates to overall health. In fact, many of the chapters in anatomy have anchoring phenomena on diseases and health. For example, cystic fibrosis is a cellular transport problem, but has serious effects on the lungs and respiratory system

When learning about the cell, we discuss how Tay-sachs is a disease associated with the lysosomes, and cystic fibrosis is a membrane transport problem. The older anatomy students can no see how those organelles are related to the overall health and functioning of the body.

This activity discusses how cancer is a problem with the cell cycle. Viruses, like HPV, or human papillomavirus, can disrupt the cell cycle and cause cervical cancer. The project reads like a case study, where students read text and answer questions.

They also analyze data from the CDC and even interpret infographics to help them understand the association between mitosis (cell cycle) and cancer.

The activity also asks them to evaluate the need for the HPV vaccine in both girls and boys by comparing data regarding cases of other types of cancer that can be associated with the virus.

Students can write the answers directly on the slides which can be assigned and submitted through Google Classroom. The final slide asks for a synthesis of the information and to take a position on whether young people should get a vaccine to protect against certain types of cancer.

There is no right or wrong answer, I only ask that my students justify their position with scientific details that show their understanding of the cell cycle and viruses.

The TpT link has answers or suggested responses as well as a download of the PowerPoint version of Google Slides.

Shannan Muskopf

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8 2.1 Case Study: Why Should You Study Human Biology?

Created by CK-12/Adapted by Christine Miller

Case Study: Our Invisible Inhabitants

Lanying is suffering from a fever, body aches, and a painful sore throat that feels worse when she swallows. She visits her doctor, who examines her and performs a throat culture. When the results come back, he tells her that she has strep throat, which is caused by the bacteria  Streptococcus pyogenes . He prescribes an antibiotic that will either kill the bacteria or stop it from reproducing, and advises her to take the full course of the treatment even if she is feeling better earlier. Stopping early can cause an increase in bacteria that are resistant to antibiotics.

Lanying takes the antibiotic as prescribed. Toward the end of the course, her throat is feeling much better — but she can’t say the same for other parts of her body! She has developed diarrhea and an itchy vaginal yeast infection. She calls her doctor, who suspects that the antibiotic treatment has caused both the digestive distress and the yeast infection. He explains that our bodies are home to many different kinds of microorganisms, some of which are actually beneficial to us because they help us digest our food and minimize the population of harmful microorganisms. When we take an antibiotic, many of these “good” bacteria are killed along with the “bad,” disease-causing bacteria, which can result in diarrhea and yeast infections.

Lanying’s doctor prescribes an antifungal medication for her yeast infection. He also recommends that she eat yogurt with live cultures, which will help replace the beneficial bacteria in her gut. Our bodies contain a delicate balance of inhabitants that are invisible without a microscope, and changes in that balance can cause unpleasant health effects.

What Is Human Biology?

As you read the rest of this book, you’ll learn more amazing facts about the human organism, and you’ll get a better sense of how biology relates to your health. Human biology is the scientific study of the human species, which includes the fascinating story of human evolution and a detailed account of our genetics, anatomy, physiology, and ecology. In short, the study focuses on how we got here, how we function, and the role we play in the natural world. This helps us to better understand human health, because we can learn how to stay healthy and how diseases and injuries can be treated. Human biology should be of personal interest to you to the extent that it can benefit your own health, as well as the health of your friends and family. This branch of science also has broader implications for society and the human species as a whole.

Chapter Overview: Living Organisms and Human Biology

In the rest of this chapter, you’ll learn about the traits shared by all living things, the basic principles that underlie all of biology, the vast diversity of living organisms, what it means to be human, and our place in the animal kingdom. Specifically, you’ll learn:

• The seven traits shared by all living things: homeostasis , or the maintenance of a more-or-less constant internal environment; multiple levels of organization consisting of one or more cells ; the use of energy and metabolism ; the ability to grow and develop; the ability to evolve adaptations to the environment; the ability to detect and respond to environmental stimuli; and the ability to reproduce .
• The basic principles that unify all fields of biology, including gene theory, homeostasis, and evolutionary theory.
• The diversity of life (including the different kinds of biodiversity), the definition of a species, the classification and naming systems for living organisms, and how evolutionary relationships can be represented through diagrams, such as phylogenetic trees.
• How the human species is classified and how we’ve evolved from our close relatives and ancestors.
• The physical traits and social behaviors that humans share with other primates.

As you read this chapter, consider the following questions about Lanying’s situation:

• What do single-celled organisms (such as the bacteria and yeast living in and on Lanying) have in common with humans?
• How are bacteria, yeast, and humans classified?
• How do the concepts of homeostasis and biodiversity apply to Lanying’s situation?
• Why can stopping antibiotics early cause the development of antibiotic-resistant bacteria?

Attribution

Figure 2.1.1

Photo (face mask) by Michael Amadeus , on Unsplash is used under the Unsplash license (https://unsplash.com/license).

Mayo Clinic Staff (n.d.). Strep throat [online article]. MayoClinic.org. https://www.mayoclinic.org/diseases-conditions/strep-throat/symptoms-causes/syc-20350338

The ability of an organism to maintain constant internal conditions despite external changes.

The smallest unit of life, consisting of at least a membrane, cytoplasm, and genetic material.

The chemical processes that occur in a living organism to sustain life.

The production of offspring by sexual or asexual process.

The variety of life in the world, ecosystem, or in a particular habitat.

Human Biology Copyright © 2020 by Christine Miller is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License , except where otherwise noted.

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Comparative case study on NAMs: towards enhancing specific target organ toxicity analysis

• Regulatory Toxicology
• Open access
• Published: 29 August 2024

Cite this article

You have full access to this open access article

• Kristina Jochum 1 ,
• Andrea Miccoli 1 , 2 , 5 ,
• Cornelia Sommersdorf 3 ,
• Oliver Poetz 3 , 4 ,
• Albert Braeuning 5 ,
• Tewes Tralau 1 &
• Philip Marx-Stoelting   ORCID: orcid.org/0000-0002-6487-2153 1  

Traditional risk assessment methodologies in toxicology have relied upon animal testing, despite concerns regarding interspecies consistency, reproducibility, costs, and ethics. New Approach Methodologies (NAMs), including cell culture and multi-level omics analyses, hold promise by providing mechanistic information rather than assessing organ pathology. However, NAMs face limitations, like lacking a whole organism and restricted toxicokinetic interactions. This is an inherent challenge when it comes to the use of omics data from in vitro studies for the prediction of organ toxicity in vivo. One solution in this context are comparative in vitro–in vivo studies as they allow for a more detailed assessment of the transferability of the respective NAM data. Hence, hepatotoxic and nephrotoxic pesticide active substances were tested in human cell lines and the results subsequently related to the biology underlying established effects in vivo. To this end, substances were tested in HepaRG and RPTEC/tERT1 cells at non-cytotoxic concentrations and analyzed for effects on the transcriptome and parts of the proteome using quantitative real-time PCR arrays and multiplexed microsphere-based sandwich immunoassays, respectively. Transcriptomics data were analyzed using three bioinformatics tools. Where possible, in vitro endpoints were connected to in vivo observations. Targeted protein analysis revealed various affected pathways, with generally fewer effects present in RPTEC/tERT1. The strongest transcriptional impact was observed for Chlorotoluron in HepaRG cells (increased CYP1A1 and CYP1A2 expression). A comprehensive comparison of early cellular responses with data from in vivo studies revealed that transcriptomics outperformed targeted protein analysis, correctly predicting up to 50% of in vivo effects.

Avoid common mistakes on your manuscript.

Introduction

Given the at times heated discussions about regulatory toxicology in the political and public domain, the quite remarkable track record of toxicological health protection sometimes tends to go unnoticed. Not only are chemical scares such as the chemically induced massive acute health impacts in the 1950ies, 60ies and 70ies a thing of the past (Herzler et al. 2021 ), but in many parts of the world, there are now regulatory frameworks in place which aim at the early identification of potential health risks from chemicals. Within Europe, the most notable in terms of impact are probably REACH (EC 2006 ) and the regulations on pesticides (EC 2009 ) both of which still overwhelmingly rely on animal data for their risk assessments. This has manifold reasons, one being the historical reliability of animal-based systems for the prediction of adversity in humans. However, there are a number of challenges to this traditional approach. These comprise capacity issues when it comes to the testing of thousands of new or hitherto untested substances, the testing of mixtures, the ever-daunting question of species specificity or the limitation of current in vivo studies regarding less accessible endpoints such as for example immunotoxicity or developmental neurotoxicity.

Over recent years, so-called New Approach Methodologies (NAMs) have thus attracted increased attention and importance for regulatory toxicology. The United States Environmental Protection Agency (US EPA 2018 ) defines NAM as ‘…a broadly descriptive reference to any technology, methodology, approach, or combination thereof that can be used to provide information on chemical hazard and risk assessment that avoids the use of intact animals… ’. One instance of an attempt to replace an animal test with an in vitro test system is the embryonic stem cell test in the area of developmental toxicology (Buesen et al. 2004 ; Seiler et al. 2006 ). This stand-alone test was first evaluated for assessing the embryotoxic potential of chemicals as early on as 2004 (Genschow et al. 2004 ). While its establishment as a regulatory prediction model took several more years, one major outcome was the realization that the use of NAMs in general is greatly improved when used as part of a biologically and toxicologically meaningful testing battery (Marx-Stoelting et al. 2009 ; Schenk et al. 2010 ). It should be noted that despite all the potential of such testing batteries a tentative one to one replacement of animal studies is neither practical nor straight forward. The reason is not only the complexity of the endpoints in question but also practical constraints. This was recently exemplified by Landsiedel et al. who pointed out that with the number of different organs and tissues tested during one sub-chronic rodent study, and assuming that 5 NAMs are needed to address the adverse outcomes in any of those organs, it would take decades just to replace this one study. Any regulatory use of NAMs should hence preferably rely on their direct use (Landsiedel et al. 2022 ).

An example from the field of hepatotoxicity testing is the in vitro toolbox for steatosis that was developed by Luckert et al. ( 2018 ) based on the adverse outcome pathway (AOP) concept by Vinken ( 2015 ). The authors employed five assays covering relevant key events from the AOP in HepaRG cells after incubation with the test substance Cyproconazole. Concomitantly, transcript and protein marker patterns for the identification of steatotic compounds were established in HepaRG cells (Lichtenstein et al. 2020 ). The findings were subsequently brought together in a proposed protocol for AOP-based analysis of liver steatosis in vitro (Karaca et al. 2023a ).

One promising use for such cell-based systems is their combination with multi-level omics. In conjunction with sufficient biological and mechanistic knowledge, the wealth of information provided by multi-omics data should potentially allow some prediction of substance-induced adversity. That said any such prediction can of course only be reliable within the established limits of such systems such as the lack of a whole organism and incomplete toxicokinetics and restrictions on adequately capturing the effects of long-term exposure (Schmeisser et al. 2023 ). Regulatory use and trust in cell-based systems will, therefore, strongly rely on how they compare to the outcome of studies based on systemic data (Schmeisser et al. 2023 ).

Pesticide active substances are a group of compounds with profound in vivo data. Some examples for active substances commonly used in PPPs are the fungicides Cyproconazole, Fluxapyroxad, Azoxystrobin and Thiabendazole, as well as the herbicide Chlorotoluron and the multi-purpose substance 2-Phenylphenol. For these compounds, several short- and long-term studies in rodents have been conducted and multiple adverse effects in target organs like liver or kidneys were observed (see Table  1 ). Liver steatosis, as one potential adverse health outcome, has been associated with triazole fungicides, such as Cyproconazole, but other active substances such as Azoxystrobin are suspected to interfere with the lipid metabolism as well (Gao et al. 2014 ; Luckert et al. 2018 ). Potential modes of action for adverse effects include the activation of nuclear receptors, such as the constitutive androstane receptor (CAR), which has been shown for Cyproconazole and Fluxapyroxad (Marx-Stoelting et al. 2017 ; Tamura et al. 2013 ; Zahn et al. 2018 ). Notably, even when an active substance is considered to be of low acute toxicity, e.g. Chlorotoluron, Thiabendazole and 2-Phenylphenol (EC 2015 ; US EPA 2002 ; WHO 1996 ), they might still exhibit adverse chronic effects (Mizutani et al. 1990 ; WHO 1996 ). This is the reason why pesticide active substances and plant protection products (PPP) are assessed extensively before their placing on the market (EC 2009 ).

The target organs most frequently affected by pesticide active ingredients are the liver and kidneys (Nielsen et al. 2012 ). Hence, an in vitro test system aimed at the prediction of pesticide organ toxicity should be able to model effects on these two target organs. One of the best options currently available for hepatotoxicity studies in vitro is the cell line HepaRG (Ashraf et al. 2018 ). Before their use in toxicological assays, the cells undergo a differentiation process resulting in CYP-dependent activities close to the levels in primary human hepatocytes (Andersson et al. 2012 ; Hart et al. 2010 ). They also feature the capability to induce or inhibit a variety of CYP enzymes (Antherieu et al. 2010 ; Hartman et al. 2020 ) and the expression of phase II enzymes, membrane transporters and transcription factors (Aninat et al. 2006 ). Antherieu et al. ( 2012 ) demonstrated that HepaRG cells can sustain various types of chemically induced hepatotoxicity following acute and repeated exposure. Hence, HepaRG cells have the potential to replace the use of primary human hepatocytes in the study of acute and chronic effects of xenobiotics in the liver. In 2012, the European Commission Joint Research Centre’s European Union Reference Laboratory for Alternatives to Animal Testing (EURL ECVAM) coordinated a validation study finding differentiated HepaRG cells as a reliable and relevant tool for CYP enzyme activity studies (EURL ECVAM 2012 ). This led to the proposal of a respective draft test guideline by the OECD in 2019 (OECD 2019 ). Additionally, as part of the US EPA Tox21 project, HepaRG cells were used for an assay assessing toxicogenomics (Franzosa et al. 2021 ).

A promising test system for investigations of nephrotoxicity is the tERT1 immortalized renal proximal tubular epithelial cell line RPTEC/tERT1 (further referred to as RPTEC). These non-cancerous cells have been found to closely resemble primary counterparts showing typical morphology and functionality (Shah et al. 2017 ; Wieser et al. 2008 ). Aschauer et al. ( 2015 ) demonstrated the applicability of RPTEC for investigation of repeated-dose nephrotoxicity using a transcriptomic-based approach. Simon et al. ( 2014 ) showed similar toxicological responses of RPTEC and the target tissue to exposure to benzo[ a ]pyrene and cadmium. Conclusively, RPTEC can be a useful tool for toxicological studies.

In the present study, six pesticide active substances were analyzed in two cell lines, namely the liver cell line HepaRG and the kidney cell line RPTEC. Assays were performed following exposure to the highest non-cytotoxic concentration and comprised targeted protein and transcriptomics analysis. Triggered pathways were identified and compared with established results from in vivo experiments.

Materials and methods

All test substances were purchased in analytical grade (purity ≥ 98.0%) from Sigma-Aldrich, Pestanal® (Taufkirchen, Germany): Cyproconazole, CAS no. 94361–06-5, catalog no. 46068, batch no. BCCD4066; Fluxapyroxad, CAS no. 907204–31-3, catalog no. 37047, batch no. BCCF6749; Azoxystrobin, CAS no. 131860–33-8, catalog no. 31697, batch no. BCCF6593; Chlorotoluron, CAS no. 15545–48-9, catalog no. 45400, batch no. BCBW1414; Thiabendazole, CAS no. 148–79-8, catalog no. 45684, batch no. BCBV5436; 2-Phenylphenol, CAS no. 90–43-7, catalog no. 45529, batch no. BCCF1784. William’s E medium, fetal calf serum (FCS) good forte (catalog no. P40-47500, batch no. P131102), recombinant human insulin and l -glutamine were acquired from PAN-Biotech GmbH (Aidenbach, Germany), FCS superior (catalog no. S0615, batch no. 0001659021) from Bio&Sell (Feucht bei Nürnberg, Germany). Dimethyl sulfoxide (DMSO, purity ≥ 99.8%), hydrocortisone-hemisuccinate (HC/HS), hydrocortisone, epidermal growth factor (EGF) and neutral red (NR) were purchased from Sigma-Aldrich (Taufkirchen, Germany). Dulbecco’s modified eagle medium (DMEM) and Ham’s F Nutrition mix were obtained from Gibco® Life Technologies (Karlsruhe, Germany), trypsin–EDTA, Penicillin–Streptomycin and insulin-transferrin-selenium from Capricorn Scientific GmbH (Ebsdorfergrund, Germany).

Cell culture

HepaRG cells were obtained from Biopredic International (Sant Grégoire, France) and kept in 75 cm 2 flasks under humid conditions at 37 °C and 5% CO 2 . Cells were grown in proliferation medium consisting of William’s E medium with 2 mM l -glutamine, supplemented with 10% FCS good forte, 100 U mL −1 penicillin, 100 µg mL −1 streptomycin, 0.05% human insulin and 50 µM HC/HS for 2 weeks. Then, HepaRG cells were passaged using trypsin–EDTA solution and seeded in 75 cm 2 flasks, 6-well, 12-well and 96-well plates at a density of 20 000 cells per cm 2 . Cells in cell culture dishes were maintained in proliferation medium for another 2 weeks before the medium was changed to differentiation medium (i.e., proliferation medium supplemented by 1.7% DMSO) and cells were cultured for another 2 weeks. Thereafter, cells were used in experiments within 4 weeks, while media was changed to treatment media (i.e., proliferation media supplemented by 0.5% DMSO and 2% FCS) 2 days prior to the experiments.

The RPTEC cell line was obtained from Evercyte GmbH (Vienna, Austria) and cultivated as previously described (Aschauer et al. 2013 ; Wieser et al. 2008 ). Cells were grown in a 1:1 mixture of DMEM and Ham’s F-12 Nutrient Mix, supplemented with 2.5% FCS superior, 100 U mL −1 penicillin, 100 µg mL −1 streptomycin, 2 mM l -glutamine, 36 ng mL −1 hydrocortisone, 10 ng mL −1 EGF, 5 µg mL −1 insulin, 5 µg mL −1 transferrin and 5 ng mL −1 selenium. RPTEC were cultivated in 75 cm 2 flasks until they reached near confluence. Then, cells were passaged using trypsin–EDTA and seeded at 30% density in 75 cm 2 flasks for further sub-cultivation and 6-well, 12-well and 96-well plates for experiments. To obtain complete differentiation, cells in cell culture dishes were maintained for 14 days before they were used in experiments.

Test concentrations

All substances were dissolved in DMSO and diluted in the respective medium to a final DMSO concentration of 0.5% before incubation. HepaRG treatment medium and 0.5% DMSO in RPTEC medium served as solvent controls for HepaRG cells and RPTEC, respectively. At least 3 biological replicates, i.e., independent experiments, were performed for each assay.

Cell viability

Cell viability was investigated with the WST-1 assay (Immunservice, Hamburg, Germany), according to the manufacturer’s protocol and subsequent NR uptake assay according to Repetto et al. ( 2008 ). HepaRG cells and RPTEC were seeded in 96-well plates and incubated with the test substances for 72 h. Triton X-100 (0.01%, Thermo Fisher Scientific, Darmstadt, Germany) was used as positive control for reduced cell viability. At the end of the incubation period, 10 µL WST-1 solution was added to each well and incubated for 30 min at 37 °C. The tetrazolium salt WST-1 is metabolized by cellular mitochondrial dehydrogenases of living cells to a formazan derivative, the absorbance of which was measured at 450 nm with an Infinite M200 PRO plate reader (Tecan, Maennedorf, Switzerland). The reading of each well was related to the absorbance value at the reference wavelength of 620 nm, and blank values were subtracted before the relation to the solvent control.

Afterwards the NR uptake assay was performed, where incorporation of NR into lysosomes of viable cells is measured. One day prior to the assay, NR medium was prepared by diluting a 4 mg mL −1 NR stock solution in PBS 1:100 with the respective cell culture medium for HepaRG cells and RPTEC, and incubated at 37 °C over night. After the WST-1 measurement, the incubation medium was removed and cells were washed twice with PBS. Subsequently, 100 µL NR medium, previously centrifuged for 10 min at 600 ×  g , was added and incubated for 2 h. Afterwards, cells were washed twice with PBS, and 100 µL destaining solution (49.5:49.5:1 ethanol absolute, distilled water, glacial acetic acid) per well was added. Plates were shaken at 500 rotations min −1 for 10 min and fluorescence of NR was measured with an Infinite M200 PRO plate reader (Tecan, Maennedorf, Switzerland) at 530 nm excitation and 645 nm emission. Each reading was subtracted by the blank value and normalized to the solvent control.

Multiplexed microsphere-based sandwich immunoassays

Marker proteins and protein modifications were analyzed by Signatope GmbH (Tübingen, Germany) with a multiplexed microsphere-based sandwich immunoassay. Cells were seeded in 6-well plates and incubated with the test substances for 36 and 72 h. Protein extraction was performed by adding 250 µL pre-cooled extraction buffer, supplied by the company, to the cells in each well and subsequent incubation for 30 min at 4 °C. Cell lysates were transferred to 1.5 mL reaction tubes and centrifuged for 30 min at 4 °C and 15 000 ×  g . The supernatant was aliquoted in 60 µL batches and stored at -80 °C until shipment. After thawing, aliquots were directly used and not frozen again. Samples were analyzed for 8 proteins and protein modifications, each representing a marker for a certain form of toxicity (Table  2 ).

Quantitative real-time PCR and PCR profiler arrays

RT-qPCR was conducted to ensure well performing RNA for subsequent PCR profiler arrays. Cells were seeded in 12-well plates and incubated with the test substances for 36 h. RNA extraction was performed with the RNA easy Mini Kit (Qiagen, Venlo, Netherlands) according to the manufacturer’s manual. Yield RNA concentration and purity were analyzed with a Nanodrop spectrometer (NanoDrop 2000, Thermo Fischer Scientific, Darmstadt, Germany) and RNA samples were stored at -80 °C until further use. Reverse transcription to cDNA was conducted using the High-Capacity cDNA Reverse Transcription Kit (Applied Biosystems, Waltham, MA, USA) according to the manufacturer’s protocol with a GeneAmp ® PCR System 9700 (Applied Biosystems, Darmstadt, Germany) and cDNA samples were stored at – 20 °C. RT-qPCR was performed with Maxima SYBR Green/ROX Master Mix (Thermo Fisher Scientific, Darmstadt, Germany) according to manufacturer’s protocol. In brief, 9 µL master mix, consisting of 5 µL Maxima SYBR Green/ROX qPCR Master Mix, 0.6 µL each of forward and reverse primers (2.5 µM) and 2.8 µL nuclease-free water, was added to each well of a 384-well plate. Primer sequences are shown in Online Resource 1. Subsequently, 20 ng cDNA was added to each well to a final volume of 10 µL and RT-qPCR was performed with an ABI 7900HT Fast Real-Time PCR system instrument (Applied Biosystems, Darmstadt, Germany). In brief, activation took place at 95 °C for 15 min, followed by 40 cycles of 15 s at 95 °C and 60 s at 60 °C, followed by 15 min at 60 °C and default melting curve analysis. Data were processed using 7900 software v241 and Microsoft Excel 2021. Threshold cycle (C T ) was set to 0.5, melting curve was checked and manual baseline correction was performed for each gene individually. Yield C T -values were extracted to Microsoft Excel 2021 and relative gene expression was obtained with the 2 −ΔΔCt method according to Livak and Schmittgen ( 2001 ). GUSB and HPRT1 served as endogenous control genes for HepaRG cells, GUSB and GAPDH were used for RPTEC. Primer efficiency was tested beforehand according to Schmittgen and Livak ( 2008 ). Only RNA samples showing amplification in RT-qPCR were used for further analysis with PCR profiler arrays. For quality control purposes, yield 2 −ΔΔCt values from RT-qPCR and PCR profiler arrays were compared and had to be within the same range (Online Resource 1).

For performing the PCR profiler array, cDNA was synthesized from 1 µg RNA using the RT 2 First Strand Kit (Qiagen, Venlo, Netherlands) according to the manufacturer’s protocol with a GeneAmp® PCR System 9700 (Applied Biosystems, Darmstadt, Germany). Subsequently, the RT 2 Profiler™ PCR Array Human Molecular Toxicology Pathway Finder or Nephrotoxicity (Qiagen, Venlo, Netherlands) was conducted with RT 2 SYBR ® Green ROX qPCR Mastermix (Qiagen, Venlo, Netherlands) according to the manufacturer’s protocol. RT-qPCR was performed with an ABI 7900HT Fast Real-Time PCR system instrument (Applied Biosystems, Darmstadt, Germany), where activation of polymerase took place for 10 min at 95 °C, followed by 40 cycles of 15 s at 95 °C and 60 s at 60 °C and default melting curve analysis. Data were analyzed using 7900 software v241 and Excel 2021. C T was set to 0.2, melting curve was checked and manual baseline correction was performed. Yield C T -values were extracted and further analyzed.

• Pathway analysis

Further evaluation of PCR array data was performed with functional class scoring methods such as Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG), as well as with the bioinformatics analysis and search tool Ingenuity Pathway Analysis Software (IPA). Following the manufacturer’s instructions, yield C T -values were uploaded to the Qiagen Gene Globe Webportal Footnote 1 and analyzed using the standard ΔΔC T method referring to an untreated control. A cut-off C T was set to 35, all 5 built-in housekeeping genes were manually selected as reference genes and their arithmetic mean used for normalization. Means of fold regulation and p-values were calculated and further evaluated with the bioinformatics tools following the protocol provided in Online Resource 2. The processed results from HepaRG cells and RPTEC were used as input data individually, as well as combined. For the combined analysis, duplicate genes that were present on both arrays were removed.

To generate a first overview, the percentage of differentially expressed genes (DEG) per pathway was determined as previously published (Heise et al. 2018 ). Genes were assorted to pathways as suggested on the manufacturer’s web page. Footnote 2 The percentage of DEG was calculated as number of genes whose expression significantly differed by a fold change of 2, as determined by Student’s t- test (p < 0.05), related to the total number of genes in the pathway.

GO enrichment and KEGG analysis

The freely available web tools GOrilla Footnote 3 and ShinyGO 0.80 Footnote 4 were used for GO enrichment and KEGG analysis, respectively (Eden et al. 2007 , 2009 ; Ge et al. 2020 ). Detailed protocols are provided in Online Resource 2 together with the R code for determining DEG and background genes (see Data availability), which was adapted from Feiertag et al. ( 2023 ).

Ingenuity pathway analysis

In addition to GO enrichment and KEGG analysis, further evaluation of PCR array data was performed with the bioinformatics analysis and search tool IPA (Qiagen, Hilden, Germany, analysis date: Nov. 2023) as previously published (Karaca et al. 2023b ). IPA is a commercial bioinformatics tool for analyzing RNA data, predicting pathway activation and functional interrelations using a curated pathway database. Using Fisher’s exact test, IPA identifies overrepresented pathways by measuring significant overlaps between user-provided gene lists and predefined gene sets. Means of fold regulation and p -values were uploaded to IPA following the protocol provided in Online Resource 2. Cut-off was set to – 1.5 and + 1.5 for fold regulation and 0.05 for the p -value. Fold regulation represents fold change results in a biologically meaningful way. In case the fold change is greater than 1, the fold regulation is equal to the fold change. For fold change values less than 1, the fold regulation is the negative inverse of the fold change. No further filtering was applied and an IPA core analysis was run. One Excel spread sheet per substance was obtained including all predicted diseases or functions annotations, the associated categories, the p-value of overlap as well as the number and names of the DEG found in the respective annotation (Online Resource 3). Predicted effects on other organs than the liver or the kidneys, such as heart or lungs, were discarded. For further comparison with in vivo data only the categories were used, combined with the p-value of the annotation, which was the highest.

Comparison with animal studies

The data obtained from targeted protein and transcriptomics analyses were compared with known in vivo observations from Draft Assessment Reports (DARs) of the pesticide active substances required for pesticide legislation. To facilitate the comparison of the data, the in vitro data was transformed into a more comprehensible form by applying evaluation matrices as shown in Table  3 .

The in vivo effects attributed to the pesticide active substances were taken from the publication by Nielsen et al. ( 2012 ). Additionally, the DARs of the two substances not reported in Nielsen et al . were analyzed and assigned accordingly. All in vivo effects identified by the authors for liver and kidneys can be found in Online Resource 1. Based on expert knowledge, descriptions of in vitro outcomes were combined with in vivo observations (see Tables  4 and 5 ).

Based on the combination of the in vitro and the in vivo data, it was possible to draw conclusions on the concordance of the predictions. In order to establish optimized thresholds for regarding an effect as in vitro positive, the analyses were performed by considering at least medium effects, strong and very strong effects, or very strong effects only (see Table  3 ) and comparing these to the corresponding in vivo effect. In case multiple in vitro predictors were connected to the same in vivo observation, a positive prediction from one was sufficient to be considered in vitro positive. For protein analyses, the comparison was performed for the data from HepaRG cells and RPTEC individually, as well as combined, where a positive prediction from one of the cell lines was considered sufficient and compared to hepatotoxic and nephrotoxic in vivo effects. For the gene transcription analysis, the categories obtained by IPA were compared to in vivo observations from DARs. A further evaluation integrating protein and transcriptional data was conducted, wherein a positive result from either data type was sufficient to classify a sample as in vitro positive. Online Resource 1 shows the combination of the results in detail. The percentage of concordance between in vitro prediction and in vivo observation was calculated. Indicative concordance was defined as percentage of in vivo positive observations that were predicted to be positive by the in vitro test system.

Statistical analysis

Statistical analysis was performed using R 4.2.1 and RStudio 2023.09.1 + 494. Data evaluation was done with Microsoft Excel 2021.

All experiments were performed in at least three independent biological replicates. Technical replicates, when applicable, were averaged and subsequently mean and standard deviation values were calculated from biological replicates. For targeted protein analysis, statistical significance was calculated with bootstrap technique using R package boot (Canty and Ripley 2016 ; Davidson and Hinkley 1997 ) to account for the high variability that results when the protein expression is affected. Data visualization was done using ggplot2 package (Wickham 2016 ). Calculation of statistical significance of altered gene transcription was performed using Student’s t -test, and R package ComplexHeatmaps was used for data visualization (Gu 2022 ). All R scripts can be found using the link provided in the Data availability section.

Impairment of cell viability

Each substance was tested for its effect on the viability of HepaRG cells and RPTEC. Based on these results, the highest non-cytotoxic concentration was determined and employed in further experiments together with a second concentration (i.e., 0.33 × highest non-cytotoxic concentration). For HepaRG cells, published data were used as a starting point for cytotoxicity testing and confirmed with WST-1 and NR uptake assays. The highest non-cytotoxic concentration, defined as the concentration determining a cell viability greater than 80%, is shown in Table  6 .

For RPTEC, a relatively new cell line, little data was available. At least 3 biological replicates were performed in technical triplicates to determine the highest non-cytotoxic concentrations (Table  6 ). The bar graphs in Online Resource 4 depict the concentration-dependent course of all tested concentrations per substance limited by solubility. Online Resource 1 provides a table with calculated approximations of substance concentrations in the target organ at LOAEL or NOEAL level based on in vivo toxicokinetic results from DARs. These approximations can be compared with the selected in vitro concentrations based on cytotoxicity experiments.

Effects on marker proteins

The result from multiplex microsphere-based sandwich immunoassays of treated HepaRG cells and RPTEC are shown in Figs.  1 and 2 , respectively. In HepaRG cells, incubation with the highest non-cytotoxic concentrations of Azoxystrobin, Chlorotoluron and Thiabendazole increased the expression of total LC3B, an indicator of autophagy, after 36 h (all three compounds) and 72 h (Chlorotoluron and Thiabendazole). Strong effects were observed on cleaved PARP, an indicator of apoptosis, after 36 h of incubation with 120 µM Cyproconazole (247 ± 147%) and 300 µM Thiabendazole (359 ± 204%). However, after 72 h incubation with 120 µM Cyproconazole, the level of cleaved PARP was strongly reduced. Expression of HIF 1-alpha, an indicator of hypoxia, was significantly increased after 36 h incubation with 45 µM Azoxystrobin (214 ± 24%). Fluxapyroxad and 2-Phenylphenol did not significantly increase the expression of any of the protein analytes.

Effects on protein abundance and protein modification of key proteins observed in HepaRG cells after 36 and 72 h of incubation with the test substances using a multiplexed microsphere-based sandwich immunoassay panel. Results are shown as means of 3 independent experiments, normalized to solvent controls. Statistical differences to the solvent control were calculated with bootstrapping (* p  < 0.05)

Effects on protein abundance and protein modification of key proteins in RPTEC after 36 and 72 h of incubation with the test substances using a multiplexed microsphere-based sandwich immunoassay panel. Results are shown as means of 3 independent experiments, normalized to solvent controls. Statistical differences to the solvent control were calculated with bootstrapping (* p  < 0.05)

In RPTEC, the abundance of p-elF4B, involved in eukaryotic translation initiation, was increased after 36 and 72 h incubation with 300 µM Cyproconazole (165 ± 45% and 201 ± 51%, respectively), all conditions of Fluxapyroxad, incubation with 3 µM Azoxystrobin for 36 h (166 ± 56%) and incubation with 900 µM Chlorotoluron for 36 and 72 h (238 ± 59% and 170 ± 44%, respectively). Thiabendazole exposure for 36 h resulted in an increase of cleaved PARP at both tested concentrations. Due to the high standard deviation, these results were not statistically significant.

Comparing the results from HepaRG cells and RPTEC, fewer effects were observed in RPTEC than in HepaRG cells. Effects of Azoxystrobin and Chlorotoluron on p-elF4B were observed in both cell lines, as well as increased levels of cleaved PARP after Thiabendazole exposure; yet these results were only significant in HepaRG cells. 2-Phenylphenol did not increase the expression of any of the tested proteins in either cell line, while Fluxapyroxad only affected p-elF4B in RPTEC.

A graphical representation of all data points from HepaRG and RPTEC including means and standard deviations can be found in Online Resource 4.

Changes at the gene transcription level

Changes at the protein level are often preceded by changes at the gene expression level. These were analyzed by RT 2 Profiler™ PCR arrays. Figures  3 and 4 show the results from HepaRG cells and RPTEC, respectively. The genes included in the array were assigned to certain pathways according to the information provided on the manufacturer’s web page. For data interpretation, the percentage of DEG was calculated. In HepaRG cells, most DEG were observed following the exposure to Chlorotoluron. Overall, genes categorized as CYPs and phase I were predominantly affected. Cyproconazole and Chlorotoluron exerted effects on genes associated with fatty acid metabolism (10 and 55%, respectively). Of all steatosis-associated genes, 47% were altered by Chlorotoluron. With regards to individual genes, the strongest increase was observed for CYP1A1 and CYP1A2 , both in the group of CYPs and phase I, after exposure to Chlorotoluron (479-fold and 57-fold, respectively) and Thiabendazole (330-fold and 215-fold, respectively).

Relative quantities of mRNA transcript levels observed after 36 h exposure of HepaRG cells to non-cytotoxic concentrations of the test substances using the Human Molecular Toxicology Pathway Finder RT 2 Profiler™ PCR Array. Data evaluation was performed using the 2 −∆∆ Ct method, according to Livak and Schmittgen ( 2001 ). All target genes were normalized to 5 housekeeping genes. Results are shown as mean of 3 biological replicates and statistical analysis was performed by one sample Student’s t -test (* p  < 0.05)

Relative quantities of mRNA transcript levels observed after 36 h exposure of RPTEC to non-cytotoxic concentrations of the test substances using the Human Nephrotoxicity RT 2 Profiler™ PCR Array. Data evaluation was performed using the 2 −∆∆ Ct method, according to Livak and Schmittgen ( 2001 ). All target genes were normalized to 5 housekeeping genes. Results are shown as mean of 3 biological replicates and statistical analysis was performed by one sample Student’s t -test (* p  < 0.05)

In RPTEC, the cluster encompassing most of the DEG was that associated with regulation of the cell cycle. Here, Cyproconazole, Fluxapyroxad, Azoxystrobin, and Chlorotoluron affected the expression of over 40% of the associated genes. Genes associated with apoptosis were altered following the exposure to all substances, particularly Cyproconazole and Chlorotoluron (47 and 37%, respectively). Cyproconazole additionally showed pronounced effects on genes encoding for extracellular matrix and tissue remodeling molecules (27 and 40%, respectively). All substances affected about 20% of all genes contained in the group of genes related to cell proliferation. Cyproconazole, Chlorotoluron and 2-Phenylphenol affected 25% of all oxidative stress-associated genes. In comparison to HepaRG cells, where CYPs and phase I was the most impacted group, in RPTEC only one of the DEG established for any of the substances belonged to the group of xenobiotic metabolism. At the level of individual genes, HMOX1, a nephrotoxicity marker, was induced over twofold after incubation with all substances, but highest for Cyproconazole (eightfold). Of all genes, the strongest induction was observed for IGFBP1 , a member of the insulin-like growth factor-binding protein family, which was increased 53-fold by incubation with Cyproconazole and over 52-fold after incubation with Chlorotoluron.

A graphical representation of all data points including means and standard deviations can be found in Online Resource 4 for HepaRG and RPTEC results.

Data analysis with GO enrichment and KEGG analysis

Gene expression results were analyzed with GO enrichment and KEGG analysis. All effects obtained in the analyses can be found in Online Resource 3.

The GO enrichment analysis of HepaRG DEG from the incubation with Cyproconazole pointed at changes in secondary and xenobiotic metabolic processes , and the combined analysis additionally resulted in significant enrichment of response to estrogen . DEG modulated by the exposure to Chlorotoluron were involved in 16 ontologies including metabolic, biosynthetic, and catabolic processes , with lipid metabolic process and organic hydroxyl compound metabolic process being the most statistically supported (i.e., p-value: 9.2 × 10 –8 and 7.7 × 10 –7 , respectively). In RPTEC, nucleic acid metabolic process was the only significantly enriched GO term for Chlorotoluron, while the combined analysis revealed a total of 23. Analysis of DEG from incubation with Thiabendazole resulted, among others, in hits for xenobiotic, terpenoid, and isoprenoid metabolic process in HepaRG and combined results. Although analysis of DEG from incubation with 2-Phenylphenol did not result in significantly enriched GO terms from the HepaRG or the RPTEC data; the combined data set showed 5 enriched terms with NADP metabolic process and myeloid leukocyte migration having the lowest p-values (6.9 × 10 –4 , both).

For KEGG analysis, the HepaRG data set for Fluxapyroxad and Chlorotoluron showed enrichment of drug metabolism-cytochrome P450 , as well as taurine and hypotaurine metabolism (Fluxapyroxad) and metabolic pathways (Chlorotoluron). Thiabendazole data revealed enrichment of steroid hormone biosynthesis , metabolism of xenobiotics by cytochrome P450 and chemical carcinogenesis-DNA adducts . RPTEC data set for Azoxystrobin and Chlorotoluron showed multiple cancer-related pathways. The combined data set only resulted in few pathways: hepatocellular carcinoma for Azoxystrobin, metabolic pathways for Chlorotoluron and mineral absorption for 2-Phenylphenol. All other analyses did not result in any significant enrichment.

Data analysis with ingenuity pathway analysis software

Gene expression data were further analyzed with the IPA software. In total 32 different categories of diseases or functions were predicted. Figure  5 shows the ten most frequently resulting categories. Liver Hyperplasia/Hyperproliferation is the only common category across all cell lines and substances. The statistical confidence of the pathway analysis was strongest for Chlorotoluron, which also induced most DEG. Comparing the three methodologies of input data, lower p-values were observed for HepaRG and combined analysis and most categories of diseases or functions were predicted by the combined analysis. Evidently, effects on the kidney were predicted from the input data from liver cells and vice versa.

Results obtained by analysis of transcriptomics data with Qiagen Ingenuity Pathway Analysis. The 10 categories most affected are represented. The x-axis shows the -log 10 value of the p-value obtained for the respective effect

In a final step, the data acquired from targeted protein and transcriptomics analyses were compared with known in vivo observations. Given that the comparison focused on aligning the responses from human cell lines with whole animal data, the analysis focused on the extent to which the omics-responses were indicative of the respective biological response in vivo (indicative concordance). To establish an optimized threshold for the evaluation of in vitro predictions, the in vitro data were transformed by applying evaluation matrices as shown in Table  3 . Based on that, activated key proteins and thus cellular functions were identified for each substance from targeted protein analyses. For the evaluation of gene transcripts, the p-values for the categories obtained by IPA were considered. Indicative concordance with known in vivo results is shown in Table  7 .

For the protein analysis, the indicative concordance ranged from 18 to 47% for the single cell lines and their combination, respectively. In contrast to the results from targeted protein analyses, the indicative concordance for the transcriptomic response was much stronger with greatest values of 55, 63 and 76% for the single cell lines and their combination, respectively. Likewise, for those cases where no effect was seen in vivo, no adverse indications were seen in vitro in 80, 91 and 78% of cases, respectively. For protein analysis, this value ranged from 78 to 86% and was 50% for the combined analysis of protein and transcriptional data. It should be noted, however, that these values decreased when the evaluation criteria were less strict (medium or strong instead of very strong).

In the present study, the pathways triggered by non-cytotoxic concentrations of six pesticide active substances were examined, employing targeted protein and transcriptomics analyses in the liver cell line HepaRG and the kidney cell line RPTEC. Utilizing evaluation matrices and prediction software tools, the observed cellular responses were interpreted and compared with outcomes from established in vivo experiments, in order to assess the relevance of our in vitro model systems in predicting the impact of pesticide exposure on human hepatic and renal cellular function. The primary emphasis of this investigation did not lie in delineating discrete effects attributable to individual substances; rather, it centered on discerning the predictive capacity of the system and serving as a case study to highlight the current challenges in the regulatory adoption of NAMs.

When targeted protein data were used to predict in vivo impacts in rodents, the best result was achieved by the combined analysis and setting the evaluation criteria to medium effects (47%). Regarding the indicative concordance based on transcriptional data, medium effects in HepaRG cells seemed the most promising resulting in a 55% match. This is notable given the systemic as well as species differences between the corresponding test systems. It also highlights that the “gold standard”, i.e., the reference standard used for comparison, is in fact not necessarily indisputable (Trevethan 2017 ). Various studies pointed at the shortcomings of traditional animal studies, such as interspecies concordance, poor reproducibility and unsatisfactory extrapolation to humans (Goodman 2018 ; Karmaus et al. 2022 ; Luijten et al. 2020 ; Ly Pham et al. 2020; Smirnova et al. 2018 ; Wang and Gray 2015 ). One example illustrating the difficulties in extrapolating data from rodents to humans is the question whether Cyproconazole causes neoplasms in the liver. Here, animal studies with CD-1 mice showed statistically significant positive trends for hepatocellular adenomas and combined tumors in male mice (EFSA 2010 ; Hester et al. 2012 ). Ensuing studies identified CAR activation by Cyproconazole as the underlying Mode of Action (MoA) (Peffer et al. 2007 ). Marx-Stoelting et al. ( 2017 ) investigated effects of Cyproconazole in mice with humanized CAR and PXR and demonstrated increased sensitivity of rodents to CAR agonist-induced effects, compared to humanized mice. In line with these observations the Joint FAO/WHO Meeting on Pesticide Residues (JMPR) concluded that Cyproconazole is unlikely to pose a carcinogenic risk to humans (JMPR 2010 ). Likewise, Cyproconazole was not considered to cause neoplasms in the liver when analyzed for this study. However, such detailed analysis of a substance’s MoA is scarce.

Another important factor impeding the comparison of in vitro and in vivo data are the different ontologies. The need for harmonized ontologies and reporting formats of in vivo data has been expressed by many researchers in the field of in silico toxicology and has been addressed in multiple projects (Hardy et al. 2012 ; Sanz et al. 2017 ). For example, uncertainty arises as to the reason if and why an effect for a particular organ is possibly not reported. Depending on the case and study in question, this might be because absent effects were simply not explicitly reported as negative, or because other organ toxicities occurred at lower doses and hence data for the remaining organs were omitted or not assessed, or because the focus of the study was another organ (Smirnova et al. 2018 ). While this does not pose a problem for when such studies are used for risk assessment, it does affect the comparison with in vitro results. Another major obstacle is the retrospective conclusive combination of large and comprehensive sets of mechanistic data in vitro with systemic and histopathological observations in vivo. This issue has recently been picked up by on-going European ONTOX project Footnote 5 (“ontology-driven and artificial intelligence-based repeated dose toxicity testing of chemicals for next generation risk assessment “) and has led the consortium to reverse the strategy and build NAMs to predict systemic repeated dose toxicity effects to enable human risk assessment when combined with exposure assessment (Vinken et al. 2021 ). A recent publication by Jiang et al. ( 2023 ) as part of the ONTOX project identified transcriptomic signatures of drug-induced intrahepatic cholestasis with potential future use as prediction model. However, not all pathologies have been analyzed so far, and those that have were often only studied for a limited number of chemicals, limiting their transferability. Hence, this study relied on the use of computational tools such as IPA, GO enrichment and KEGG analysis, to draw functional conclusions from transcriptomics data. While IPA results in categorized diseases or functions annotations, KEGG and GO analyses display enriched ontologies. Therefore, while KEGG and GO results were too ambiguous to be related to distinct in vivo observations, it was feasible to combine IPA results with in vivo observations. It is noteworthy that even though GO enrichment and KEGG analysis seem fairly similar, the results varied widely between the predictions from the various software tools. Soh et al. ( 2010 ) analyzed consistency, comprehensiveness, and compatibility of pathway databases and made several crucial findings such as the inconsistency of associated genes across different databases pertaining to the same biological pathway. Furthermore, common biological pathways shared across different databases were frequently labeled with names that provided limited indication of their interrelationships. Chen et al. ( 2023 ) demonstrated that using the same gene list with different analysis methods may result in non-concordant overrepresented, enriched or perturbed pathways. Taken together, these considerations may explain the divergent findings from the different transcriptomics analyses in the present study. Additionally, these findings underscore the challenges associated with integrating pathway data from diverse sources and emphasize the need for standardized and cohesive representation of biological pathways in databases.

Compared to the transcriptomic data, protein analyses from HepaRG cells and RPTEC cells resulted in a comparatively low indicative concordance. This challenges the notion that protein analysis may be superior in prediction (Wu et al. 2023 ). One likely explanation is that proteins often reflect molecular functions and adverse effects more accurately, and diseases frequently involve dysregulated post-translational modifications, which are challenging to detect and may be poorly correlated with mRNA levels (Kannaiyan and Mahadevan 2018 ; Kelly et al. 2010 ; Zhao et al. 2020 ). However, due to the relatively low number of protein markers as compared to the number of mRNA markers, the targeted transcriptomics analysis is associated with a higher likelihood of finding a match. In the gene transcription analysis with ensuing IPA evaluation, 370 genes were analysed for HepaRG. In contrast, the protein analysis conducted in this study focussed on 8 proteins or modifications, each indicative of a particular cellular function, that were analysed at two time points after incubation of cells with two concentrations of the test substances. Consequently, a cellular response to a stressor over time can be observed, such as the different levels of cleaved PARP after 36 h and 72 h of incubation with Cyproconazole in HepaRG cells. While elevated levels of this apoptosis indicator were noted after 36 h, reduced levels were observed after 72 h. Possible explanations for this include a cellular feedback mechanism or an advanced stage of apoptosis.

Another central observation is that combination of cell lines and methods significantly increases indicative concordance (up to 88%). In the case of targeted protein analysis, combination of results led to an overall value of 47%, compared to approximately 20% for each cell line. Similar trends were observed for transcriptomic data with 76% indicative concordance for combined results, albeit decreasing the cases where an in vivo negative effect corresponded to no adverse indication seen in vitro , as the total number of positive in vitro effects was increased. Nonetheless, the idea that including omics data in regulatory process will unreasonably increase positive findings and lead to overprotectiveness can be challenged as strengthening the evaluation criteria lead to a reversion of this trend. The shortcomings of stand-alone in vitro tests to replace animal experiments have long been known. For example, single tests do not cover all possible outcomes of interest or all modes of action possibly causing a toxicological effect (Hartung et al. 2013 ; Rovida et al. 2015 ). In the present study, reported in vivo effects such as lesions of biliary epithelium or inflammation of the liver may not be fully represented by a single hepatic cell line. Hence, regulatory toxicologists strive to implement so-called integrated testing strategies (ITS) (Caloni et al. 2022 ). Results from projects in the fields of embryonic, developmental and reproductive, or acute oral toxicity have shown that test batteries increase the predictive value over individual assays (Piersma et al. 2013 ; Prieto et al. 2013 ; Sogorb et al. 2014 ). To share these novel methodologies in ITS for safety evaluations in the regulatory context, the OECD Integrated Approaches for Testing and Assessment (IATA) Case Studies Project offers a platform where comprehensive information on case studies, such as consideration documents capturing learnings and lessons from the review experience, can be found. Footnote 6

While this publication’s scope did not extend to establishing a conclusive ITS for liver and kidney toxicity, it serves as a valuable starting point for future analyses in this direction and offers ongoing assistance and insights. Moving forward, it could prove beneficial when exploring testing protocols that integrate protein and transcriptomics analyses, enhancing the comprehensiveness of safety evaluations in this domain.

Data availability

The data sets generated during the current study are available in the Jochum-et-al-2024 GitHub repository, https://github.com/KristinaJochum/Jochum-et-al-2024 .

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Conceptualization: Oliver Poetz, Albert Braeuning, Philip Marx-Stoelting, Tewes Tralau; methodology: Kristina Jochum, Philip Marx-Stoelting, Oliver Poetz; formal analysis and investigation: Kristina Jochum, Andrea Miccoli, Cornelia Sommersdorf; writing—original draft preparation: Kristina Jochum, Philip Marx-Stoelting; writing—review and editing: Andrea Miccoli, Cornelia Sommersdorf, Oliver Poetz, Albert Braeuning, Tewes Tralau, Philip Marx-Stoelting; funding acquisition: Tewes Tralau, Philip Marx-Stoelting; resources: Tewes Tralau, Philip Marx-Stoelting, Oliver Poetz.

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Oliver Poetz is a shareholder of SIGNATOPE GmbH. Cornelia Sommersdorf is an employee at SIGNATOPE GmbH. SIGNATOPE offers assay development and service using immunoassay technology.

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Jochum, K., Miccoli, A., Sommersdorf, C. et al. Comparative case study on NAMs: towards enhancing specific target organ toxicity analysis. Arch Toxicol (2024). https://doi.org/10.1007/s00204-024-03839-7

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