4.3.2: Metabolic Lifestyles - Biology

4.3.2: Metabolic Lifestyles - Biology

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Organisms can be identified according to the source of carbon they use for metabolism as well as their energy source. Organisms that convert inorganic carbon dioxide (CO2) into organic carbon compounds are autotrophs. Conversely, heterotrophs rely on more complex organic carbon compounds as nutrients; these are provided to them initially by autotrophs. Many organisms, ranging from humans to many prokaryotes, including the well-studied Escherichia coli, are heterotrophic. All pathogens are heterotrophic because their carbon source is their host.

Organisms can also be identified by the energy source they use. All energy is derived from the transfer of electrons, but the source of electrons differs between various types of organisms. The prefixes photo- (“light”) and chemo- (“chemical”) refer to the energy sources that various organisms use. Those that get their energy for electron transfer from light are phototrophs, whereas chemotrophs obtain energy for electron transfer by breaking chemical bonds. There are two types of chemotrophs: organotrophs and lithotrophs. Organotrophs, including humans, fungi, and many prokaryotes, are chemotrophs that obtain energy from organic compounds. Lithotrophs (“litho” means “rock”) are chemotrophs that get energy from inorganic compounds, including hydrogen sulfide (H2S) and reduced iron. Lithotrophy is unique to the microbial world.

The strategies used to obtain both carbon and energy can be combined for the classification of organisms according to nutritional type. Most organisms are chemoheterotrophs because they use organic molecules as both their electron and carbon sources. Table summarizes this and the other classifications.

Table Classifications of Organisms by Energy and Carbon Source
ClassificationsEnergy SourceCarbon SourceExamples
ChemotrophsChemoautotrophsChemicalInorganicHydrogen-, sulfur-, iron-, nitrogen-, and carbon monoxide-oxidizing bacteria
ChemoheterotrophsChemicalOrganic compoundsAll animals, most fungi, protozoa, and bacteria
PhototrophsPhotoautotrophsLightInorganicAll plants, algae, cyanobacteria, and green and purple sulfur bacteria
PhotoheterotrophsLightOrganic compoundsGreen and purple nonsulfur bacteria, heliobacteria
  • Contributed by OpenStax
  • General Biology at OpenStax CNX

Shrinking to survive: Bacteria adapt to a lifestyle in flux

Summer picnics and barbecues are only a few weeks away! As excited as you are to indulge this summer, Escherichia coli bacteria are eager to feast on the all-you-can-eat buffet they are about to experience in your gut.

However, something unexpected will occur as E. coli cells end their journey through your digestive tract. Without warning, they will find themselves swimming in your toilet bowl, clinging to the last bits of nutrients attached to their bodies. How do these tiny organisms adapt to survive sudden starvation? Scientists at Washington University in St. Louis wondered.

Close examination of nutrient-deprived E. coli under the microscope -- a routine process in a lab that studies bacterial cell size -- revealed cells that looked different, and that these differences are related to their ability to survive.

"Their cytoplasm shrank. As it shrank, the inner membrane pulled away from the outer membrane and left a big space at one end of the cell," said Petra Levin, professor of biology in Arts & Sciences, whose postdoctoral scientist, Corey Westfall, and undergraduate student, Jesse Kao, first made the observation.

The space to which Levin refers, between the bacteria's inner and outer membranes, is called the periplasm. In collaboration with Kerwyn Casey Huang, professor of bioengineering and of microbiology and immunology at Stanford University, and his postdoctoral scientist, Handuo Shi, Levin found an unexpected developmental response to starvation -- one that may be keeping E. coli alive until they find their next buffet.

The work is published this week in the Proceedings of the National Academy of Sciences.

The biologists showed that when E. coli cells lack nutrients, the cytoplasm becomes more dense as its volume decreases, probably because of water loss. At the same time, the periplasm increases in volume as the inner membrane pulls away from the outer membrane.

"Although we don't know for sure yet, we think that the cell is concentrating the nutrients in the cytoplasm so that it can keep running metabolism at a high rate," Levin said. "Perhaps this is an adaptation to E. coli's constantly and rapidly changing lifestyle, in which it knows that each environment is temporary."

The shrinking is reversible, the scientists found. Once they transferred the starving bacteria into a nutrient-rich medium, the inner membrane and the cytoplasm expanded. The bacterial cells rapidly rebounded from starvation, especially when E. coli received their favorite carbon source, glucose. And, importantly, if the Tol-Pal system was intact.

The Tol-Pal system is a critical cellular machinery composed of proteins that connect the outer membrane to the inner membrane. But its function has been understudied. As the inner membrane expands, the Tol-Pal system helps to reconnect it with the outer membrane, the scientists speculate. When the Tol-Pal system was absent, the internal contents of the cells bled out.

"We speculate that Tol-Pal acts as the zipper slider, helping the inner membrane zip into the outer membrane coat during recovery," Levin said.

What happens to the transmembrane proteins, embedded in both the inner and outer membrane, when the inner membrane pulls away from the outer membrane? Do they get ripped apart? Levin and colleagues do not know yet and hope to answer these questions in the future.

Feed your genes: How our genes respond to the foods we eat

What should we eat? Answers abound in the media, all of which rely on their interpretation of recent medical literature to come up with recommendations for the healthiest diet. But what if you could answer this question at a molecular level -- what if you could find out how our genes respond to the foods we eat, and what this does to the cellular processes that make us healthy -- or not? That's precisely what biologists at the Norwegian University of Science and Technology have done.

If you could ask your genes to say what kinds of foods are best for your health, they would have a simple answer: one-third protein, one-third fat and one-third carbohydrates. That's what recent genetic research from the Norwegian University of Science and Technology (NTNU) shows is the best recipe to limit your risk of most lifestyle-related diseases.

Food affects gene expression

NTNU researchers Ingerid Arbo and Hans-Richard Brattbakk have fed slightly overweight people different diets, and studied the effect of this on gene expression. Gene expression refers to the process where information from a gene's DNA sequence is translated into a substance, like a protein, that is used in a cell's structure or function.

"We have found that a diet with 65% carbohydrates, which often is what the average Norwegian eats in some meals, causes a number of classes of genes to work overtime," says Berit Johansen, a professor of biology at NTNU. She supervises the project's doctoral students and has conducted research on gene expression since the 1990s.

"This affects not only the genes that cause inflammation in the body, which was what we originally wanted to study, but also genes associated with development of cardiovascular disease, some cancers, dementia, and type 2 diabetes -- all the major lifestyle-related diseases," she says.

Common dietary advice and chronic disease

These findings undercut most of the underpinnings for the diets you've heard will save you. Dietary advice abounds, and there is a great deal of variation as to how scientifically justified it is. But it is only now that researchers are figuring out the relationship between diet, digestion and the effect on one's health and immune system -- so they can now say not only what kinds of foods are healthiest, but why.

"Both low-carb and high-carb diets are wrong," says Johansen. "But a low-carb diet is closer to the right diet. A healthy diet shouldn't be made up of more than one-third carbohydrates (up to 40 per cent of calories) in each meal, otherwise we stimulate our genes to initiate the activity that creates inflammation in the body."

This is not the kind of inflammation that you would experience as pain or an illness, but instead it is as if you are battling a chronic light flu-like condition. Your skin is slightly redder, your body stores more water, you feel warmer, and you're not on top mentally. Scientists call this metabolic inflammation.

A powdered diet

Johansen and her colleagues conducted two studies. The first was to determine what type of research methods they would use to answer the questions they had. In the pilot study (28 days) five obese men ate real food, while in the second study, 32 slightly overweight men and women (mainly students) ate specially made powdered food.

Participants in the latter study were randomly assigned to go six days on a diet with 65 percent of calories from carbohydrates, with the rest of the calories from protein (15 percent) and fat (20 percent), then a week with no diet. Then came the six days on a diet with half the carbs and twice as much protein and fat as in the first diet. There were blood tests before and after each dieting period.

The amount of food each person ate was calculated so that their weight would remain stable and so that equal portions were consumed evenly over six meals throughout the day.

The researchers had help developing diets from Fedon Lindberg, a medical doctor who specializes in internal medicine and who promotes low-glycaemic diets, Inge Lindseth, an Oslo dietician who specializes in diabetes, and Ann-Kristin de Soysa, a dietician who works with obese patients at St. Olavs Hospital in Trondheim.

"We wanted to know exactly what the subjects were getting in terms of both macro- and micronutrients," says Johansen, -"A tomato doesn't contain a consistent amount of nutrients, or antioxidants, for example. So make sure we had a handle on the health effects, we had to have accurate accounting of nutrients. That's why we chose the powdered diets for the main study."

Solving the control problem

Diet studies that compare different diets with different amounts of fat are often criticized with the argument that it is difference in the amount of omega-3 fatty acids that causes the health effects, not the rest of the food intake.

The researchers addressed this problem by having the same amount of omega-3 and omega-6 in both diets, although the amount of fat in general was different in the diets that were tested. The researchers also avoided another common problem: the natural variation in gene expression between humans.

"Each of our study subjects was able to be his or her own control person, " Johansen says "Every subject was allowed to go on both diets, with a one-week break in between the diets, and half began with one diet, while the rest started with the other diet."

Blood tests were conducted before and after each diet period. All of the measurements of changes in gene expression were done so that each individual's difference in gene expression was compared with that person alone. The results were then compiled.

Johnson says the studies resulted in two important findings. One is the positive effect of many meals throughout the day, and the details about the quality and composition of components in an optimal diet, including omega-3 and omega-6 fatty acids. The second is that a carbohydrate-rich diet, regardless of whether or not a person overeats, has consequences for genes that affect the lifestyle diseases, she says.

A way to measure genetic temperature

Throughout the study, researchers surveyed the extent to which various genes were working normally or overtime. An aggregate measure of the results of all of this genetic activity is called gene expression. It can almost be considered a measurement of the genetic temperature of the body's state of health.

"We are talking about collecting a huge amount of information," says Johansen.

"And it's not like there is a gene for inflammation, for example. So what we look for is whether there are any groups of genes that work overtime. In this study we saw that an entire group of genes that are involved in the development of inflammatory reactions in the body work overtime as a group."

It was not only inflammatory genes that were putting in overtime, as it would turn out. Some clusters of genes that stood out as overactive are linked to the most common lifestyle diseases.

"Genes that are involved in type 2 diabetes, cardiovascular disease, Alzheimer's disease and some forms of cancer respond to diet, and are up-regulated, or activated, by a carbohydrate-rich diet," says Johansen.

Johansen is not a cancer researcher, and is not claiming that it is possible to eliminate your risk of a cancer diagnosis by eating. But she thinks it is worth noting that the genes that we associate with disease risk can be influenced by diet.

"We're not saying that you can prevent or delay the onset of Alzheimer's if you eat right, but it seems sensible to reduce the carbohydrates in our diets," she suggests.

"We need more research on this," Johansen adds. "It seems clear that the composition and quantity of our diets can be key in influencing the symptoms of chronic disease. It is important to distinguish between diet quality and quantity, both clearly have very specific effects."

The body's arms race

Johansen argues that diet is the key to controlling our personal genetic susceptibility to disease. In choosing what we eat, we choose whether we will provide our genes the weapons that cause disease. The immune system operates as if it is the body's surveillance authority and police. When we consume too many carbohydrates and the body is triggered to react, the immune system mobilizes its strength, as if the body were being invaded by bacteria or viruses.

"Genes respond immediately to what they have to work with. It is likely that insulin controls this arms race," Johansen says. "But it's not as simple as the regulation of blood sugar, as many believe. The key lies in insulin's secondary role in a number of other mechanisms. A healthy diet is about eating specific kinds of foods so that that we minimize the body's need to secrete insulin. The secretion of insulin is a defense mechanism in response to too much glucose in the blood, and whether that glucose comes from sugar or from non-sweet carbohydrates such as starches (potatoes, white bread, rice, etc.), doesn't really matter."

Avoid the fat trap!

The professor warns against being caught up in the fat trap. It's simply not good to cut out carbs completely, she says. "The fat/protein trap is just as bad as the carbohydrate trap. It's about the right balance, as always."

She says we must also make sure to eat carbohydrates, proteins and fats in five to six smaller meals, not just for the main meal, at dinner.

"Eating several small and medium-sized meals throughout the day is important. Don't skip breakfast and don't skip dinner. One-third of every meal should be carbohydrates, one-third protein and one-third fat. That's the recipe for keeping inflammatory and other disease-enhancing genes in check," Johansen explains.

Change is quick

Johansen has some encouraging words, however, for those of us who have been eating a high carbohydrate diet. "It took just six days to change the gene expression of each of the volunteers," she says, "so it's easy to get started. But if you want to reduce your likelihood of lifestyle disease, this new diet will have to be a permanent change."

Johansen stressed that researchers obviously do not have all the answers to the relationship between diet and food yet. But the trends in the findings, along with recent scientific literature, make it clear that the recommendation should be for people to change their dietary habits.

Otherwise, an increasing number of people will be afflicted with chronic lifestyle diseases.

The new food balance sheet

Most of us think it is fine to have foods that you can either eat or not eat, whether it comes to carbohydrates or fats. So how will we know what to put on our plates?

Do we have to both count calories and weigh our food now?

"Of course you can be that careful," says Johansen. "But you will come a long way just by making some basic choices. If you cut down on boiled root vegetables such as potatoes and carrots, and replace the white bread with a few whole meal slices, such as rye bread, or bake your own crispbread, you will reduce the amount of bad carbohydrates in your diet quite significantly. Furthermore, remember to eat protein and fat at every meal, including breakfast!"

Salad also contains carbohydrates

Johansen explains that many of us do not realize that all the fruits and vegetables we eat also count as carbohydrates -- and that it's not just sweet carbohydrates that we should watch out for.

"Salad is made up of carbohydrates," says Johansen. "But you have to eat a lot of greens to get a lot of calories. Steamed broccoli is a great alternative to boiled potatoes. Fruit is good, but you have to be careful not to eat large quantities of the high-glycemic fruits at one time. Variety is important."

The best is to cut down on potatoes, rice and pasta, and to allow ourselves some of the good stuff that has long been in the doghouse in the refrigerator.

"Instead of light products, we should eat real mayonnaise and sour cream," Johansen says, "and have real cream in your sauce, and eat oily fish. That said, we should still remember not to eat too much food, either at each meal or during the day. Fat is twice as calorie-rich as carbohydrates and proteins, so we have to keep that in mind when planning the sizes of our portions. Fat is also different. We shouldn't eat too much saturated animal fat, but monounsaturated vegetable fats and polyunsaturated marine fats are good."

Fountain-of-youth genes

Johansen's research also shows that some genes are not up-regulated, but rather the opposite -- they calm down rather than speed up.

"It was interesting to see the reduction in genetic activity, but we were really happy to see which genes were involved. One set of genes is linked to cardiovascular disease. They were down-regulated in response to a balanced diet, as opposed to a carbohydrate-rich diet," she says. Another gene that was significantly differently expressed by the diets that were tested was one that is commonly called "the youth gene" in the international research literature.

"We haven't actually stumbled on the fountain of youth here," Johansen laughs, "but we should take these results seriously. The important thing for us is, little by little, we are uncovering the mechanisms of disease progression for many of our major lifestyle-related disorders."

Johansen's research has been supported by NTNU and Central Norway Regional Health Authority. Other key partners have been Mette Langaas, a statistician and associate professor of mathematics at NTNU, Dr. Bard Kulseng of the Regional Center for Morbid Obesity at St Olavs Hospital, and Martin Kuiper, a professor of systems biology at NTNU.

Duke Researcher Busts Metabolism Myths in New Book

Herman Pontzer explains where our calories really go, and what studying humanity’s past can teach us about staying healthy today.

Photo by Elena Georgiou, My City /EEA

Duke professor Herman Pontzer has spent his career counting calories. Not because he’s watching his waistline, exactly. But because, as he sees it, “in the economics of life, calories are the currency.” Every minute, everything the body does — growing, moving, fighting infection, even just existing — “all of it takes energy,” Pontzer says.

In his new book, “Burn,” the evolutionary anthropologist recounts the 10-plus years he and his colleagues have spent measuring the metabolisms of people ranging from ultra-athletes to office workers, as well as those of our closest animal relatives, and some of the surprising insights the research has revealed along the way.

Much of his work takes him to Tanzania, where members of the Hadza tribe still get their food the way our ancestors did — by hunting and gathering. By setting out on foot each day to hunt zebra and antelope or forage for berries and tubers, without guns or electricity or domesticated animals to lighten the load, the Hadza get more physical activity each day than most Westerners get in a week.

So they must burn more calories, right? Wrong.

Herman Pontzer, associate professor of evolutionary anthropology at Duke

Pontzer and his colleagues have found that, despite their high activity levels, the Hadza don’t burn more energy per day than sedentary people in the U.S. and Europe.

These and other recent findings are changing the way we understand the links between energy expenditure, exercise and diet. For example, we’ve all been told that if we want to burn more calories and fight fat, we need to work out to boost our metabolism. But Pontzer says it’s not so simple.

“Our metabolic engines were not crafted by millions of years of evolution to guarantee a beach-ready bikini body,” Pontzer says. But rather, our metabolism has been primed “to pack on more fat than any other ape.” What’s more, our metabolism responds to changes in exercise and diet in ways that thwart our efforts to shed pounds.

What this means, Pontzer says, is you can walk 16,000 steps each day like the Hadza and you won’t lose weight. Sure, if you run a marathon tomorrow you’ll burn more energy than you did today. But over time, metabolism responds to changes in activity to keep the total energy you spend in check.

Pontzer’s book is more than a romp through the Krebs cycle. For anyone suffering pandemic-induced pangs of frustrated wanderlust, it’s also filled with adventure. He takes readers on an hours-long trek to watch a Hadza man track a wounded giraffe across the savannah, to the rainforests of Uganda to study climbing chimpanzees, and to the foothills of the Caucasus Mountains to unearth the 1.8 million-year-old remains of some of the first people who trekked out of Africa.

His humor shines through along the way. Even when awoken by a chorus of 300-pound lions just a few hundred yards from his tent, he stops to ponder whether his own stench gives him away, and what he might do if they come for his “soft American carcass, the warm triple crème brie of human flesh.”

Pontzer spoke via email with Duke Today about his book:

Q: What’s the lesson the Hadza and other hunter-gatherers teach us about managing weight and staying healthy?

A: The Hadza stay incredibly fit and healthy throughout their lives, even into their older ages (60’s, 70’s, even 80’s). They don’t develop heart disease, diabetes, obesity, or the other diseases that we in the industrialized world are most likely to suffer from. They also have an incredibly active lifestyle, getting more physical activity in a typical day than most Americans get in a week.

My work with the Hadza showed that, surprisingly, even though they are so physically active, Hadza men and women burn the same number of calories each day as men and women in the U.S. and other industrialized countries. Instead of increasing the calories burned per day, the Hadza physical activity was changing the way they spend their calories — more on activity, less on other, unseen tasks in the body.

The takeaway for us here in the industrialized world is that we need to stay active to stay healthy, but we can’t count on exercise to increase our daily calorie burn. Our bodies adjust, keeping energy expenditure in a narrow range regardless of lifestyle. And that means that we need to focus on diet and the calories we consume in order to manage our weight. At the end of the day, our weight is a matter of calories eaten versus calories burned — and it’s really hard to change the calories we burn!

Q: You’re saying that exercise doesn’t matter? What’s the point, if we can’t eat that donut?

A: All those adjustments our bodies make responding to exercise are really important for our health! When we burn more calories on exercise, our bodies spend less energy on inflammation, stress reactivity (like cortisol), and other things that make us sick.

Q: What’s the biggest misunderstanding about human metabolism?

A: We’re told — through fitness magazines, diet fads, online calorie counters — that the energy we burn each day is under our control: if we exercise more, we’ll burn more calories and burn off fat. It’s not that simple! Your body is a clever, dynamic product of evolution, shifting and adapting to changes in our lifestyle.

Q: In your book you say we’re driven to magical thinking when it comes to calories. What do you mean by that?

A: Because our body is so clever and dynamic, and because humans are just bad at keeping track of what we eat, it’s awfully hard to keep track of the calories we consume and burn each day. That, along with the proliferation of fad diets and get-thin-quick schemes, has led to this idea that “calories don’t matter.” That’s magical thinking. Every ounce of your body — including every calorie of fat you carry — is food you consumed and didn’t burn off. If we want to lose weight, we must eat fewer calories than we burn. It really comes down to that.

Q: Some people say that if the cavemen didn’t eat it, we shouldn’t either. What does research show about what foods are “natural” for humans to eat?

A: There’s no singular, natural human diet. Hunter-gatherers like the Hadza eat a diverse mix of plant and animal foods that varies day to day, month to month, and year to year. There’s even more dietary diversity when we look across populations. Humans are built to thrive on a wide variety of diets — just about everything is on the menu.

That said, the ultra-processed foods we’re inundated with in our modern industrialized world really are unnatural. There are no Twinkies to forage in the wild. Those foods are literally engineered to be overconsumed, with a mix of flavors that overwhelm our brain’s ability to regulate our appetites. Now, it is still possible to lose weight on a Twinkie diet (I’m not recommending it!), if you’re very strict about the calories eaten per day. But we need to be really careful about how we incorporate ultra-processed foods into our daily diets, because they are calorie bombs that drive us to overconsume.

Q: If we could time travel, what would our hunter-gatherer ancestors make of our industrialized diet today?

A: We don’t even need to imagine — We are those hunter-gatherers! Biologically, genetically, we are the same species that we were a hundred thousand years ago, when hunting and gathering were the only game in town. When we’re confronted with modern ultra-processed foods, we struggle. They are engineered to be delicious, and we tend to overconsume.

Q: Has the COVID-19 pandemic brought any of these lessons home for you? What can we do to keep active and watch what we eat, even while working from home?

The pandemic has been a tragedy on so many levels — the loss of life, those suffering with long-term effects, the social and economic impacts. The impact on diet and exercise have been bad as well, for many of us. Stress eating is a real phenomenon, and the stress and emotional toll of the pandemic — along with having easy access to the snacks in our kitchen — have led many to gain weight. Physical activity seems to have declined for many. There aren’t easy answers, but we should try to make a point to get active every day. And we can help ourselves make better decisions about food by keeping ultra-processed foods out of our houses. You can’t plow through a bag of chips if you don’t have chips in your cupboard.

Q: You’ve measured the energy costs of activities ranging from taking a breath to doing an Ironman. What is one of the more extreme or surprising calorie-burning activities that you’ve measured, or would like to measure, in humans or some other animal?

A: With colleagues from Japan, I measured the energy cost of a heartbeat – a tricky bit of metabolic measurement! Turns out each beat of your heart burns about 1/300 th of a kilocalorie! Amazing how efficient our bodies can be.

Q: What is something people have questions about that we just don’t know the answer to yet? What would it take to find out?

A: Right now we’re excited about measuring the adjustments our bodies make when we increase our exercise: how exactly does burning more energy on physical activity impact our immune system, our stress response, our reproductive system? It will take a long-term study of exercise to see how these systems change over time.

Robin Smith – University Communications

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Materials and methods

RNA isolation and transcriptome sequencing

Axenic cultures of Rhynchopus humris strain YPF1608 and Sulcionema specki strain YPF1618 were recently generated [18]. Hemistasia phaeocysticola strain YPF1303 was provided by Akinori Yabuki (JAMSTEC, Yokosuka, Japan). An axenic culture of Trypanoplasma borreli strain Tt-JH was isolated from a tench (Tinca tinca) [176] and kindly provided by Hanka Pecková (Institute of Parasitology). The RNA from three diplonemid species was isolated using Nucleospin RNA isolation kit (Macherey Nagel). The transcriptomic libraries of the diplonemids H. phaeocysticola (Hemistasiidae), R. humris, and S. specki (Diplonemidae) and the kinetoplastid T. borreli (Parabodonida) were prepared and sequenced on the Illumina HiSeq 4000 platform using the standard TruSeq protocol, resulting in

51 million paired-end unprocessed reads of 100 nt in length, respectively.

Clonal cultures of free-living eukaryovorous Prokinetoplatina strains PhM-4 and PhF-6 were isolated from brackish waters of Turkey and freshwaters of Vietnam, respectively. Total RNA was extracted using an RNAqueous-Micro Kit (Invitrogen, Cat. No. AM1931) and converted into cDNA using the Smart-Seq2 protocol [177]. Transcriptome sequencing was performed on the Illumina HiSeq 2500 platform with read lengths of 100 bp using the KAPA stranded RNA-seq kit (Roche) to construct paired-end libraries.

Assembling the collection of transcriptomes and genomes

Transcriptomic reads of H. phaeocysticola, R. humris, S. specki, and T. borreli were subjected to adapter and quality trimming using Trimmomatic v.0.36 [178] with the following settings: maximal mismatch count, 2 palindrome clip threshold, 20 simple clip threshold, 10 minimal quality required to keep a base, 3 window size, 4 required quality, 15 and minimal length of reads to be kept, 75 nt. Transcriptome assemblies were generated using Trinity v.2.2.0 with minimal contig length set to 200 nt, with the “normalize_max_read_cov” option set to 50 for R. humris, and with the other parameters set at the default values [179].

Transcriptomic reads of PhM-4 and PhF-6 were quality trimmed with Trimmomatic-0.32 [178] with a maximum of two mismatches allowed, a sliding window size of 4 and minimum quality of 20, and a minimum length of 35. Trinity version 2.0.6 was used to assemble the dataset, using default values [179]. Transcriptome assembly steps were done in conjunction with an extensive prey sequence decontamination process (below).

The transcriptome libraries of Rhabdomonas costata strain PANT2 (Euglenida) were prepared from 4 μg of total RNA according to the standard TruSeq Stranded mRNA Sample Preparation Guide. Libraries were sequenced on an Illumina MiSeq instrument (Illumina, San Diego, CA, USA) using 150 base-length read chemistry in a paired-end mode. Reads were assembled by Trinity v2.0.6 into 93,852 contigs.

The assembled transcriptomes of Neobodo designis (Kinetoplastea, Neobodonida) and Eutreptiella gymnastica (Euglenida) were downloaded from the Marine Microbial Eukaryote Transcriptome Sequencing Project database (MMETSP) [11]. We used the transcriptome assembly of Euglena gracilis strain Z generated by Ebenezer et al. and that of Azumiobodo hoyamushi generated by Yazaki and colleagues [15, 180]. Redundant transcripts were filtered out from all the transcriptome assemblies using the CD-HIT-EST software v.4.6.7 [181] with the sequence identity threshold of 90%. Prediction of coding regions within transcripts was performed using Transdecoder v.3.0.0 [182] under the default settings, and the resulting files with protein sequences were used for further analyses. Completeness of the transcriptome and genome assemblies was assessed using the BUSCO v.3 software [53] and the “eukaryota_obd9” database containing a set of 303 universal eukaryotic single-copy orthologs.

Reference genome and transcriptome assemblies and sets of annotated proteins were downloaded from publicly available sources listed in Additional file 1: Table S1. For bodonids (i.e., Prokinetoplastina, Neo-, Para-, and Eubodonida), all genomes and transcriptomes publicly available at the time of the manuscript preparation were used. For trypanosomatids, five representative genome sequences were selected, two belonging to distantly related monoxenous (=one host) species (P. confusum and L. pyrrhocoris) and three to dixenous organisms (T. brucei, T. grayi, and L. major), switching between two hosts in their life cycles. Recently, T. grayi from crocodiles and P. confusum parasitizing mosquitoes were demonstrated to be slowly evolving trypanosomatids, preserving the highest number of ancestral genes [48]. L. major and L. pyrrhocoris, belonging to the subfamily Leishmaniinae, are characterized by different lifestyles [183]. T. brucei and L. major belong among the most extensively studied trypanosomatids and have high-quality genome assemblies and annotations available. The latter is also true for L. pyrrhocoris [51]

Decontamination of the R. costata, N. designis, and Prokinetoplastina spp. transcriptomes

The culture of R. costata was non-axenic, and accordingly, the presence of transcripts belonging to contaminating species was detected using a BLASTN search against the SILVA database with an E value cut-off of 10 −20 [184]. The best-scoring contaminants represented β- and γ-proteobacterial small-subunit (SSU) rRNA sequences. The following decontamination procedure was applied in order to get rid of the bacterial sequences: (i) a BLASTX search against the NCBI nr database using R. costata transcripts as queries with an E value cut-off of 10 −20 (ii) the BLAST results were sorted according to the bitscore and only 20 best hits were retained for each R. costata query sequence (iii) the best-scoring hits were annotated as “bacterial”, “eukaryotic”, and “other” (iv) transcript sequences were considered to be of bacterial origin and excluded from further analyses if more than 60% of best hits were bacterial according to the results of classification at the previous step. The decontamination procedure described above and prediction of coding regions within the transcripts of non-bacterial origin has produced a dataset of 36,019 protein sequences, with 3679 proteins removed as bacterial contaminants.

A BLASTN search against the SILVA database using N. designis transcripts as queries with an E value cut-off of 10 −20 revealed the presence of SSU rRNA sequences belonging only to a γ-proteobacterium of the genus Alteromonas. Since no other contaminants were identified, we downloaded all available genomes of Alteromonas spp. from the NCBI database and used them as a database for filtering out putative bacterial sequences from the N. designis transcriptome using BLASTN with an E value cut-off of 10 −5 . The contamination level was low, and this procedure resulted in removal of just 22 putative bacterial contigs from the transcriptome assembly.

As PhM-4 and PhF-6 are grown with the bodonids Procryptobia sorokini, and Parabodo caudatus as prey, respectively, we minimized contamination of the PhM-4 and PhF-6 datasets through an extensive bioinformatic decontamination procedure. This includes decontamination steps that took place before and after assembly of the PhM-4 and PhF-6 datasets. Before assembly of PhM-4 and PhF-6, we assembled 2 × 300 bp PE transcriptome reads from monoeukaryotic P. sorokini and P. caudatus prey cultures, along with 100 bp PE HiSeq 2000/2500 datasets derived from previously published datasets [185] in which other species preyed upon either P. sorokini or P. caudatus (i.e., cultures that were heavily contaminated by the same prey species). RNA-seq reads from PhM-4 and PhF-6 datasets were mapped to the assemblies containing P. sorokini or P. caudatus contigs, respectively, using Bowtie2 version 2.1.0 [186]. Reads that mapped to the prey assemblies (along with their mates, if only one read mapped) were discarded. The resulting unmapped reads were used to generate crude PhF-6 and PhM-4 transcriptome assemblies. To identify further prey-derived contamination, we used crude PhF-6 and PhM-4 assemblies to query the assembled transcriptomes of either P. caudatus or P. sorokini via megablast version 2.2.30 [187]. We considered a contig as a putative contaminant if it was ≥ 95% identical to sequences in the prey assemblies over a span of at least 75 bp. In the case of PhF-6, which was more extensively contaminated by prey than PhM-4, we added an additional step of mapping raw Illumina HiSeq2000 and MiSeq reads containing P. caudatus to the PhF-6 assembly contigs with mapped reads were discarded. Potential cross-contamination from species multiplexed on the same HiSeq 2500 run was removed using the script from the BBMap package [188], with the options minc = 3, minp = 20, minr = 15, and minl = 350.

Gene family inference and phylogenetic tree construction

Orthologous groups (OGs) containing proteins from 19 species (Additional file 1: Table S1) were inferred using OrthoFinder v.1.1.8 [189] under default settings. The heterolobosean Naegleria gruberi was used as an outgroup. For phylogenetic tree construction, OGs containing only one protein in each species were analyzed (52 OGs in total). Protein sequences of R. costata were additionally compared against the NCBI nr database with a relaxed E value cut-off of 10 −10 in order to exclude any sequences of potential bacterial origin, which were not filtered out as described in the previous section with a more stringent E value cut-off of 10 −20 , but no contaminating sequences were identified. Inferred amino acid sequences of each gene were aligned using the L-INS-i algorithm in MAFFT v.7.310 [190]. The average percent identity within each OG was calculated using the alistat script from the HMMER package v.3.1 [77]. Twenty OGs demonstrating average percent identity within the group of > 50% were used for the phylogenomic analysis. The percent identity threshold was applied since our previous experience with euglenozoan phylogenomics [51, 191] shows that excluding highly divergent sequences improves the resolution of both maximum-likelihood and Bayesian trees. The protein alignments were trimmed using Gblocks v.0.91b with relaxed parameters (-b3 = 8, -b4 = 2, -b5 = h) and then concatenated, producing an alignment containing 6371 characters. A maximum-likelihood tree was inferred using IQ-TREE v.1.5.3 with the LG+F+I+G4 model and 1000 bootstrap replicates [192, 193]. A Bayesian phylogenetic tree was constructed using PhyloBayes-MPI v.1.7b [194] under the GTR-CAT model with four discrete gamma categories. Four independent Markov Chain Monte Carlo chains were run for

8000 cycles, and all chains converged on the topology shown in Fig. 1. The initial 20% of cycles were discarded as a burn-in, and sampling every 5 cycles was used for inference of the final consensus tree visualized using FigTree v.1.4.3 [195].

Analysis of metabolic pathways

For the analysis of metabolic capacities, an automatic assignment of KEGG Orthology (KO) identifiers to the proteins of the species of interest (Additional file 1: Table S1) was conducted using BlastKOALA v.2.1 [55]. The search was performed against a non-redundant pangenomic database of prokaryotes at the genus level and eukaryotes at the family level. KEGG Mapper v.2.8 was used for reconstruction of metabolic pathways and their comparison [196]. An enzyme was considered to be present in a particular group (diplonemids, euglenids, or kinetoplastids) if it was identified in at least two organisms belonging to that group (or in one species in the case of Prokinetoplastina). In certain cases, for verifying the original functional annotations, additional BLAST and/or Hidden Markov model-based (HMM) searches were performed with an E value cut-offs of 10 −20 and 10 −5 , respectively, unless other parameters are specified. The number of metabolic proteins reported for a species is equal to the number of unique KO identifiers falling into the KEGG category “metabolism” assigned to the proteins encoded in the genome/transcriptome of that species. The term “metabolic proteins” is used herein to refer to the proteins belonging to the KEGG category “metabolism.” The analysis of protein sharing was performed using UpSetR package [197]. The unpaired t test was applied when necessary to test statistical significance of the observed differences in average number of unique KEGG identifiers across species groups.

For the comparison of metabolic capabilities of euglenozoans with those of other protists, high-quality genome assemblies of 16 free-living heterotrophic and 17 parasitic/symbiotic organisms were downloaded from the NCBI Genomes database (Additional file 1: Table S2). Assemblies demonstrating BUSCO coverage more than 75% for free-living species and 45% for parasites and symbionts were considered of high quality and analyzed using BlastKOALA v.2.1 as described for euglenozoans. A shared loss of a metabolic protein in kinetoplastids and ciliates was inferred if a protein was absent in both groups, while being present in at least three species of the free-living heterotrophic protists from other groups listed in Additional file 1: Table S2.

Species clustering using the Uniform Manifold Approximation and Projection algorithm

Uniform Manifold Approximation and Projection (UMAP) is a novel general-purpose non-linear algorithm for dimensionality reduction [60]. The UMAP algorithm implemented in the uwot v0.1.3 R package [60] was applied to pairwise distances between 2181-dimensional vectors (presence/absence data for metabolic KO identifiers) for 19 species. First, we tried to find optimal values of key UMAP parameters that are suitable for recovering both local and global structure. The following setting combinations were tested: (1) the Euclidean or Hamming distance metrics, (2) number of nearest neighbors from 2 to 18, and (3) for each number of nearest neighbors, minimal distance between points in the 2D embedding was varied from 0 to 0.9 in 0.1 increments. The Euclidean and Hamming distance metrics yielded similar results, and the latter was selected as more appropriate for binary data. After inspecting all the resulting 2D embeddings, 3 was selected as the optimal number of nearest neighbors and 0 as the optimal minimal distance. Next, we ran 20 iterations of the algorithm with different random seeds generating both 2D and 3D embeddings of the multidimensional data structure. This was done to check whether the clustering remains stable across iterations. Results of 10 iterations are shown for both 2D (Additional file 6: Fig. S5) and 3D embeddings (Additional file 7: Fig. S6). The latter embeddings were visualized using the plot3D R package.

Fatty acid biosynthesis

For the analysis of elongase repertoire, four proteins of T. brucei (TbELO1–4) described by Lee et al. [106] were used as a query in BLASTP search with an E value cut-off of 10 −20 against the euglenozoan protein database. Phylogenetic trees were reconstructed using IQ-TREE with automatic model selection and 1000 bootstrap replicates for two datasets: (i) euglenozoan proteins only and (ii) euglenozoan sequences along with functionally characterized elongases from several other organisms (Additional file 14: File S1 Additional file 15: File S2) [109, 198,199,200,201]. For the identification of fatty acid synthase (FAS) I and II, proteins of Saccharomyces cerevisiae and Homo sapiens were used as queries with an E value cut-off of 10 −10 [202, 203]. FAS I enzyme was considered to be present if at least three functional domains were identified on the same transcript.

Analysis of trypanothione metabolism

Genes encoding the enzymes of the trypanothione biosynthetic pathway were considered to be present in a genome or transcriptome when the following conditions were fulfilled: (i) a protein could be identified by BLAST with an E value cut-off of 10 −20 and/or a corresponding KEGG ID was assigned to a protein and (ii) p-distances between a reference protein and a putative hit calculated using MEGA v.7 did not exceed 0.7 or a different threshold specified in Additional file 13: Tables S41-S51 [204]. Additionally, the presence of a splice leader (SL) sequence was checked in the case of transcriptomic data, requiring a match with a minimal length of 12 nt. When a protein of interest could not be identified among predicted proteins, additional BLAST searches with raw transcriptome/genome sequences as a database were performed using an E value threshold of 10 −10 . For glutathionylspermidine (GspS) and trypanothione synthetases (TryS), as well as trypanothione (TR), glutathione (GR), and thioredoxin (TrxR) reductases, HMM-based searches using the HMMER package v.3.1 [77] were performed in addition to BLAST searches. An HMM model for GspS was generated using the Pfam seed alignment PF03738, and HMM models for other enzymes were obtained based on alignments of annotated sequences from the KEGG database. Two groups of proteins, GspS + TryS and TR represent related proteins, share a certain degree of sequence similarity and could be aligned (Additional file 13: Tables S50 and S51). For the identification of GspS/TryS homologues outside Euglenozoa, TryS of T. brucei was used as a query in a BLASTP search against the NCBI nr database (E value 10 −20 ) and 1000 best hits for two groups, prokaryotes (group I) and other organisms (excluding Euglenozoa group II), were obtained and combined into one file. Then, the sequences were filtered using CD-HIT-EST software v.4.6.7 [181] with 98% protein identity threshold. For the TR/GR/TrxR phylogeny, the corresponding protein sequences of Emiliania huxleyi, Homo sapiens, and trypanosomatids Blechomonas ayalai, Endotrypanum monterogeii, and T. cruzi were used as a reference. Sequences were aligned using Muscle v.3.8.31 with default parameters [205]. The resulting alignments were trimmed using trimAl v.1.4.rev22 with the “-strict” option [206]. Maximum-likelihood trees for both protein groups were build using IQ-TREE v.1.5.3 with 1000 and 100 bootstrap replicates, for reductases and synthases, respectively and the LG+I+G4 model (automatically selected). Bayesian trees were inferred using MrBayes v.3.2.6 with the models of rate heterogeneity across sites chosen based on IQ-TREE results, while models of amino acid substitutions were assessed during the analysis (mixed amino acid model prior). The resulting model was WAG+I+G4 for both synthetases and reductases. The analysis was run for one million generations with sampling every 100th of them and discarding the first 25% of samples as a burn-in.

Identification of the DNA pre-replication complex subunits

Identification of the pre-replication complex (pre-RC) complex subunits was a multi-step procedure. Initially, BLAST searches with the reference sequences listed in Additional file 16: Table S52 as queries and an E value threshold of 10 −5 against databases of annotated transcripts/genomes of the euglenozoans and protists belonging to other groups (Additional file 1: Table S2) were performed. If a target protein could not be identified, an HMM-based method was employed. Pre-computed models for the proteins of interest were downloaded from the Pfam database when available (Additional file 16: Table S52), or a new model was generated based on a protein alignment constructed using Muscle v.3.8.31 [205, 207]. When none or just a few euglenozoan proteins were identified, another round of HMM-based searches was performed. For that purpose, full-length reference sequences present in the seed alignment were downloaded from the Pfam database, and, when possible, high-scoring hits in euglenozoans and reference protists were added to the seed alignment (E value < 1 −20 , preferably only full-length sequences with predicted domains). For HMM model construction, both trimmed and untrimmed alignments were used, and the search results were compared. Alignment trimming was accomplished in trimAl v.1.4.rev22 with the “-gappyout” option [206]. Visual inspection of phylogenetic trees constructed using IQ-TREE with automatic model selection and 1000 fast bootstrap replicates was performed to facilitate annotation of related sequences [192, 193].

Maximum-likelihood and Bayesian trees for the minichromosome maintenance (MCM) complex subunits 2–9 were inferred as described for the TR/GR/TrxR proteins, with the LG+F+I+G4 and WAG+I+G4 models, respectively. Only BLAST hits with p-distances ≤ 0.75 were considered. The trees were rooted using archaeal MCM sequences belonging to Haloferax volcanii (ADE04992), Methanoculleus sp. MAB1 (CVK32523.1), Nanoarchaeum equitans (NP_963571.1), and Sulfolobus acidocaldarius (WP_011277765.1).

Putative homologues of the winged-helix initiator protein were searched using an HMM model build based on an alignment of 35 archaeal sequences downloaded from the NCBI Protein database.

Analysis of putative lateral gene transfer (LGT) events

For the analysis of putative LGT events, the protein sequences encoded by the genes of interest were used as a query in a BLASTP search against the NCBI nr database (E value 10 −20 ) and 1000 best hits for each, prokaryotes and other organisms (excluding Euglenozoa), were obtained. The resulting sequences were filtered using CD-HIT-EST software v.4.6.7 [181] with 90–98% protein identity threshold (depending on the protein identity levels). Sequences were aligned using Muscle v.3.8.31 with default parameters [205], and the resulting alignment was trimmed with trimAl v.1.4.rev22 [206] and used for phylogenetic analyses. Maximum-likelihood and Bayesian trees were inferred as described for trypanothione biosynthetic enzymes with the automatically selected LG+I+G4 model and 100 standard bootstrap replicates (for maximum-likelihood analysis). The trees were visualized in FigTree v.1.4.3 [195].

Identification of the kinetochore machinery elements

For the identification of putative centromeric histones H3 (cenH3), all available sequences of the canonical histone H3 (caH3) and its variants were downloaded from HistoneDB v.2.0 [208] and used as a BLAST query against transcripts, genomes, and predicted proteins of Euglenozoa with an E value threshold of 10 −5 . A hit was considered as a cenH3 candidate if it satisfied the following criteria: (i) at least one amino acid insertion in the loop 1 of the histone fold domain, (ii) divergent N-terminal tail, (iii) absence of the conserved glutamine residue in the α1 helix of the histone fold domain, and (iv) presence of a divergent histone fold domain [160]. Trypanosomatid-specific histone H3 variant (H3V) sequences were identified based on the presence of all of the following features: (i) a divergent N-terminal tail, (ii) absence of the conserved glutamine residue in the α1 helix of the histone fold domain, and (iii) absence of insertions in the loop 1 of the histone fold domain [209]. Distinguishing between putative caH3 and replication-independent histone variant H3.3, differing by only a few amino acids in opisthokonts [210], was out of scope of the current study, and the corresponding sequences were annotated as caH3/H3.3 (Additional file 9: Table S40).

Pre-computed HMMs for other conventional kinetochore components with the IDs specified in Additional file 16: Table S53 were downloaded from the Pfam database, and several rounds of HMM-based searches were performed as described for the DNA pre-replication complex subunits. Additionally, sequences of conventional kinetochore proteins identified by van Hooff and colleagues [158] in multiple eukaryotic lineages were used for building new HMMs, thus overcoming the bias towards overrepresentation of opisthokont sequences in the Pfam database. Only the most conserved components of the conventional kinetochore machinery were considered in our analyses, including the Ndc80 complex (Ndc80, Nuf2, Spc24, and Spc25 subunits), Knl1, the Mis12 complex (Mis12, Nnf1, Dsn1, and Nsl1), and CenpC.

For the identification of the kinetoplastid kinetochore proteins (KKTs), sequences annotated as KKTs were downloaded from the TriTryp database release 41, combined with the homologues identified in the eubodonid Bodo saltans [38], aligned using Muscle v.3.8.31 with default parameters [205], and used for HMM building and subsequent searches. Hits were annotated as putative KKTs when they met all of the following criteria: (i) HMM hit E value ≤ 10 −5 , (ii) p-distances calculated using MEGA v.7 did not exceed 0.8 or a different threshold specified in Additional file 13: Tables S13-S31 [204], and (iii) hit coordinates extending beyond predicted borders of highly conserved domains known to be present in proteins with unrelated functions. In the case of KKT2, 3, 10, and 19, HMM-based searches returned many hits due to the presence of widespread kinase domains [38, 162], and in order to facilitate annotation process, only two best hits for each species were taken for phylogenetic tree inference in IQ-TREE v.1.5.3 with 1000 fast bootstrap replicates (Additional file 17: File S3 Additional file 18: File S4 Additional file 19: File S5). Distinguishing between KKT10 and KKT19 proved to be a complicated task due to a very high degree of sequence similarity, and therefore, tentative annotation was performed based on the p-distances to the corresponding sequences in B. saltans.

Kinetoplastid kinetochore-interacting proteins (KKIPs) of T. brucei [163] were used as a BLAST query against the TriTryp database release 41 with an E value threshold of 10 −20 . Retrieved sequences were aligned and p-distances were calculated as described above. Hits with p-distances ≤ 0.8 to the homologues in T. brucei were aligned and used for HMM-based searches. The hits were filtered as described for the KKT proteins. For the phosphatase domain-containing KKIP7, only the hits with an E value ≤ 10 −100 and p-distances ≤ 0.65 to the reference trypanosomatid sequences (Additional file 13: Tables S32-S38) were subjected to the phylogenetic analysis using IQ-TREE v.1.5.3 with 1000 fast bootstrap replicates (Additional file 20: File S6).

Basal Metabolic Rate (BMR): Definition, Factors and Significance

Basal metabolic rate is the energy released when the subject is at complete mental and physical rest i.e. in a room with comfortable temperature and humidity, awake and sitting in a reclining position, 10-12 hours after the last meal. It is essentially the minimum energy required to maintain the heart rate, respiration, kidney function etc.

The B.M.R. of an average Indian man is 1750-1900 Kcal/day. In terms of oxygen consumption it would amount to about 15 litre/hr. Heavily built persons have higher BMRs, but the BMR per unit body weight is higher in the smaller built individuals ex. although the BMR of a man as given above is higher than that of a boy of 15 kg body weight that spends about 800 Kcal/day for its basal metabolism, the BMR per kg/day of man is about 30 Kcal, while that of the boy is about 53 Kcal/kg/day.

The variable that correlates most with the BMR is the surface area of the body. Thus in case of both boy and man the BMR is around 1000 Kcal/m 2 body surface/day.

In case of human beings body surface area can be calculated by the following formula:

S = 0.007184 x W 0.425 x h 0.725

S = surface area in sq metres

Factors Influencing BMR:

There are many factors that affect the BMR. These include body temperature, age, sex, race, emotional state, climate and circulating levels of hormones like catecholamine’s (epinephrine and norepinephrine) and those secreted by the thyroid gland.

1. Genetics (Race):

Some people are born with faster metabolism and some with slower metabolism. Indians and Chinese seem to have a lower BMR than the Europeans. This may as well be due to dietary differences between these races. Higher BMR exists in individuals living in tropical climates. Ex. Singapore.

Men have a greater muscle mass and a lower body fat percentage. Thus men have a higher basal metabolic rate than women. The BMR of females declines more rapidly between the ages of 5 and 17 than that of males.

BMR reduces with age i.e. it is inversely proportional to age. Children have higher BMR than adults. After 20 years, it drops about 2 per cent, per decade.

The heavier the weight, the higher the BMR, ex. the metabolic rate of obese women is 25 percent higher than that of thin women.

5. Body surface area:

This is a reflection of the height and weight. The greater the body surface area factor, the higher the BMR. Tall, thin people have higher BMRs. When a tall person is compared with a short person of equal weight, then if they both follow a diet calorie-controlled to maintain the weight of the taller person, the shorter person may gain up to 15 pounds in a year.

6. Body fat percentage:

The lower the body fat percentage, the higher the BMR. The lower body fat percentage in the male body is one reason why men generally have a 10-15% higher BMR than women.

Starvation or serious abrupt calorie-reduction can dramatically reduce BMR by up to 30%. Restrictive low-calorie weight loss diets may cause BMR to drop as much as 20%. BMR of strict vegetarians is 11% lower than that of meat eaters.

8. Body temperature/health:

For every increase of 0.5° C in internal temperature of the body, the BMR increases by about 7 percent. The chemical reactions in the body actually occur more quickly at higher temperatures. So a patient with a fever of 42° C (about 4° C above normal) would have an increase of about 50 percent in BMR. An increase in body temperature as a result of fever increases the BMR by 14-15% per degree centigrade which evidently, is due to the increased rate of metabolic reactions of the body.

9. External temperature:

Temperature outside the body also affects basal metabolic rate. Exposure to cold temperature causes an increase in the BMR, so as to create the extra heat needed to maintain the body’s internal temperature. A short exposure to hot temperature has little effect on the body’s metabolism as it is compensated mainly by increased heat loss. But prolonged exposure to heat can raise BMR.

Thyroxine is a key BMR-regulator which speeds up the metabolic activity of the body. The more thyroxine produced, the higher the BMR. If too much thyroxine is produced (thyrotoxicosis) BMR can actually double. If too little thyroxine is produced (myxoedema) BMR may shrink to 30-40 percent of normal rate. Like thyroxine, adrenaline also increases the BMR but to a lesser extent. Anxiety and tension may not show on the face but they do produce an increased tensing of the muscles and release of norepinephrine even though the subject is seemingly quiet. Both these factors tend to increase the metabolic rate.

Physical exercise not only influences body weight by burning calories, it also helps raise the BMR by building extra lean tissue. (Lean tissue is more metabolically demanding than fat tissue.) So more calories are burnt even when sleeping.

The BMR is not changed during pregnancy. The higher value of BMR in late pregnancy is due to the BMR of the foetus.

Significance of BMR:

1. The determination of BMR is the principal guide for diagnosis and treatment of thyroid disorders.

2. If BMR is less than 10% of the normal, it indicates moderate hypothyroidism. In severe hypothyroidism, the BMR may be decreased to 40 to 50 percent below normal.

3. BMR aids to know the total amount of food or calories required to maintain body weight.

4. The BMR is low in starvation, under nutrition, hypothalamic disorders, Addison’s disease and lipoid nephrosis.

5. The BMR is above normal in fever, diabetes insipidus, leukemia and polycythemia.

How accurate is metabolic testing?

In general, metabolic testing via indirect calorimetry is a reliable way to obtain information about your body that you might not otherwise have. But it&rsquos also important to remember that a lot of the testing accuracy comes down to the equipment being used and who&rsquos doing the testing and interpreting the results&mdashmore on that in just a sec.

Be wary of body composition tests that claim to predict your RMR and stick with indirect calorimetry, when possible. &ldquoThere are body composition tests like hand-held dynamometers or scales that try to predict RMR, [but those] are prediction values and some more accurate than others,&rdquo Donoghue explains. &ldquoIndirect calorimetry, when done correctly, has the least amount of error."

A 2017 review of data in the Indian Journal of Endocrinology and Metabolism concluded that while predictive methods of testing are &ldquoquestionable,&rdquo indirect calorimetry is a valuable resource for calculating nutrition needs and managing chronic health conditions.

New research on Alzheimer’s Disease shows ‘lifestyle origin at least in some degree’

Ph.D. student Erin Saito enters data into a computer in the lab of Professor Benjamin Bikman.

Ph.D. student Erin Saito enters data into a computer in the lab of Professor Benjamin Bikman.

For years, research to pin down the underlying cause of Alzheimer’s Disease has been focused on plaque found to be building up in the brain in AD patients. But treatments targeted at breaking down that buildup have been ineffective in restoring cognitive function, suggesting that the buildup may be a side effect of AD and not the cause itself.

A new study from a team of BYU researchers finds novel cellular-level support for an alternate theory that is growing in strength: Alzheimer’s could actually be a result of metabolic dysfunction in the brain. In other words, there is growing evidence that diet and lifestyle are at the heart of Alzheimer’s Disease.

“Alzheimer’s Disease is increasingly being referred to as insulin resistance of the brain or Type 3 Diabetes,” said senior study author Benjamin Bikman, a professor of physiology and developmental biology at BYU. “Our research shows there is likely a lifestyle origin to the disease, at least to some degree.”

For the new study, published in academic journal Alzheimer’s & Dementia , the BYU research team examined RNA sequences in 240 post-mortem Alzheimer’s Disease-impacted brains. They were looking specifically at the gene expression of nervous system support cells during two types of metabolism: glucose metabolism, where carbohydrates are broken down to provide energy, and something called ketolytic metabolism.

Ketolytic metabolism involves the brain creating energy from ketones, molecules made in our body when the hormone insulin is low and we are burning relatively higher amounts of fat. The popular “Keto Diet” is named after the process since that low-carb, high-protein diet lowers insulin levels and causes the body to burn fat instead of carbs and produce ketones.

The researchers found widespread glucose metabolism impairment in those nervous system support cells of the brains of former Alzheimer’s Disease patients, but limited ketolytic metabolism impairment. The finding is significant because the brain is like a hybrid engine, with the ability to get its fuel from glucose or ketones, but in the Alzheimer’s brains studied, there appears to be a fundamental genetic deficit in the brain’s ability to use glucose.

“We’ve turned the hybrid engine of our brains into a mono-fuel system that just fails to thrive,” Bikman said. “And so, the brain, which is progressively becoming deficient in its ability to use glucose, is now crying out for help it’s starving in the midst of plenty. The body is swimming in a sea of glucose, but the brain just can’t use it.

“The inability to use glucose increases the value of ketones. However, because the average person is eating insulin-spiking foods so frequently, there’s never any ketones available to the brain,” Bikman added. “I look at these findings as a problem we’ve created and that we’re making worse.”

Previous research has observed that the brains of people with AD have a quantifiable reduction in the ability to take in and use glucose, but this paper is the first to show it actually happens at the cellular level. It’s a significant contribution to the growing paradigm shift in regards to the scientific view of the causes of Alzheimer’s.

And since ketolytic metabolism seems to keep working fine in people with AD, even when glucose metabolism gives out, the paper concludes that treatments involving ketones may be able to support brain metabolism and slow the cognitive decline associated with the disease.

Study authors, which include fellow principal investigator and BYU professor Justin Miller and BYU professor John Kauwe (also now president of BYU-Hawaii), suggest future research investigate metabolic dysfunction in Alzheimer’s Disease brains should target oligodendrocytes because genes involved in ketolysis and glycolysis are both differentially expressed in that cell type in AD brains.

This study was a collaboration with Washington University School of Medicine in St. Louis, who provided the BYU research team with access to various brain banks, including Mayo Clinic, Mount Sinai, and a brain bank at Washington University.

Protist Life Cycles and Habitats

Protists live in a wide variety of habitats, including most bodies of water, as parasites in both plants and animals, and on dead organisms.

Learning Objectives

Describe the habitats and life cycles of various protists

Key Takeaways

Key Points

  • Slime molds are categorized on the basis of their life cycles into plasmodial or cellular types, both of which end their life cycle in the form of dispersed spores.
  • Plasmodial slime molds form a single-celled, multinucleate mass, whereas cellular slime molds form an aggregated mass of separate amoebas that are able to migrate as a unified whole.
  • Slimes molds feed primarily on bacteria and fungi and contribute to the decomposition of dead plants.

Key Terms

  • haploid: of a cell having a single set of unpaired chromosomes
  • sporangia: an enclosure in which spores are formed (also called a fruiting body)
  • plasmodium: a mass of cytoplasm, containing many nuclei, created by the aggregation of amoeboid cells of slime molds during their vegetative phase
  • diploid: of a cell, having a pair of each type of chromosome, one of the pair being derived from the ovum and the other from the spermatozoon

Life Cycle of Slime Molds

Protist life cycles range from simple to extremely elaborate. Certain parasitic protists have complicated life cycles and must infect different host species at different developmental stages to complete their life cycle. Some protists are unicellular in the haploid form and multicellular in the diploid form, which is a strategy also employed by animals. Other protists have multicellular stages in both haploid and diploid forms, a strategy called alternation of generations that is also used by plants.

Plasmodial slime molds

The slime molds are categorized on the basis of their life cycles into plasmodial or cellular types. Plasmodial slime molds are composed of large, multinucleate cells and move along surfaces like an amorphous blob of slime during their feeding stage. The slime mold glides along, lifting and engulfing food particles, especially bacteria. Upon maturation, the plasmodium takes on a net-like appearance with the ability to form fruiting bodies, or sporangia, during times of stress. Meiosis produces haploid spores within the sporangia. Spores disseminate through the air or water to potentially land in more favorable environments. If this occurs, the spores germinate to form amoeboid or flagellate haploid cells that can combine with each other and produce a diploid zygotic slime mold to complete the life cycle.

Plasmodial slime mold life cycle: Haploid spores develop into amoeboid or flagellated forms, which are then fertilized to form a diploid, multinucleate mass called a plasmodium. This plasmodium is net-like and, upon maturation, forms a sporangium on top of a stalk. The sporangium forms haploid spores through meiosis, after which the spores disseminate, germinate, and begin the life cycle anew. The brightly-colored plasmodium in the inset photo is a single-celled, multinucleate mass.

Cellular slime molds

The cellular slime molds function as independent amoeboid cells when nutrients are abundant. When food is depleted, cellular slime molds aggregate into a mass of cells that behaves as a single unit called a slug. Some cells in the slug contribute to a 2–3-millimeter stalk, which dries up and dies in the process. Cells atop the stalk form an asexual fruiting body that contains haploid spores. As with plasmodial slime molds, the spores are disseminated and can germinate if they land in a moist environment. One representative genus of the cellular slime molds is Dictyostelium, which commonly exists in the damp soil of forests.

Cellular slime mold life cycle: Cellular slime molds may engage in two forms of life cycles: as solitary amoebas when nutrients are abundant or as aggregated amoebas (inset photo) when nutrients are scarce. In aggregate form, some individuals contribute to the formation of a stalk, on top of which sits a fruiting body full of spores that disseminate and germinate in the proper moist environment.

Habitats of Various Protists

There are over 100,000 described living species of protists. Nearly all protists exist in some type of aquatic environment, including freshwater and marine environments, damp soil, and even snow. Paramecia are a common example of aquatic protists. Due to their abundance and ease of use as research organisms, they are often subjects of study in classrooms and laboratories. In addition to aquatic protists, several protist species are parasites that infect animals or plants and, therefore, live in their hosts. Amoebas can be human parasites and can cause dysentery while inhabiting the small intestine. Other protist species live on dead organisms or their wastes and contribute to their decay. Approximately 1000 species of slime mold thrive on bacteria and fungi within rotting trees and other plants in forests around the world, contributing to the life cycle of these ecosystems.

Watch the video: MLAI Methods for Metabolic Modeling in RTMs (February 2023).