Royaume du Maroc
Ministère de l’Education Nationale, de la Formation Professionnelle, de l’Enseignement Supérieure et de la Recherche
UNIVERSITE MOHAMMED V
FACULTE DE MEDECINE ET DE PHARMACIE DE RABAT
MEMOIRE DE MASTER
MASTER DE BIOTECHNOLOGIE MEDICALEOPTION : BIOMEDICALE Thème
Présenté par: Encadré par:
Sofia SEHLI Pr. Hassan GHAZAL Jury de soutenance
Président Azeddine IBRAHIMI Professeur Faculté de Médecine et de Pharmacie, Rabat
Encadrant Hassan GHAZAL Professeur Centre National de la Recherche Scientifique et Technique Examinatrice Imane ALLALI Professeur Faculté des Sciences, Rabat
Examinatrice Rajae CHAHBOUN Professeur Faculté de Médecine et de Pharmacie, Tanger Examinateur Mohammed TIMINOUNI Professeur Institut Pasteur du Maroc, Casablanca
Abstract
Over the next few decades, more than a few hundred million people will have diabetes and obesity worldwide. However, the existing treatment approaches are directed at treating the consequences instead of the causes of undermined metabolism. This approach is not effective and new paradigms need to be established. A study of the genome can not predict or describe more than 10–20 percent of the disease incidence, although improvements in diet and lifestyle behaviour are likely to have a significant effect. In the last decade, the gut Microbiome, linking environmental risk factors and genetic predisposition, constitutes a second source of genomic diversity and is proposed to have an impact on diabetic predisposition and progress in many human and animal populations. In order to assess the gut microbial composition in patients with type 1 and 2 diabetes, 35 amplicon reads datasets from patients and healthy samples have been analysed, using a metagenomic approach. The results showed notable differences in terms of gut microbiome abundance and richness between diabetic patients and healthy individuals which was in concordance with prior research studies. This metagenomics approach is promising for novel predictive biomarkers and innovative therapeutic approaches for diabetes types 1 and 2.
Résumé
Au cours des prochaines décennies, plus de quelques centaines de millions de personnes seront atteintes de diabète et d'obésité dans le monde. Cependant, les approches thérapeutiques actuelles visent à traiter les conséquences plutôt que les causes de l'affaiblissement du métabolisme. Cette approche n'est pas efficace et de nouveaux paradigmes doivent être établis. Une étude du génome ne peut pas prédire ou décrire plus de 10 à 20 % de l'incidence de la maladie, bien que des améliorations du régime alimentaire et du mode de vie aient probablement un effet significatif. Au cours de la dernière décennie, le microbiome intestinal, qui relie les facteurs de risque environnementaux et la prédisposition génétique, constitue une deuxième source de diversité génomique et il est proposé d'avoir un impact sur la prédisposition et les progrès du diabète dans de nombreuses populations humaines et animales. Afin d'évaluer la composition microbienne intestinale chez les patients atteints de diabète de type 1 et 2, 35 ensembles de données de reads d'amplicon provenant de patients et d'échantillons sains ont été analysés, en utilisant une approche métagénomique. Les résultats ont montré des différences notables en termes d'abondance et de richesse microbienne intestinale entre les patients diabétiques et les individus sains, ce qui est conforme aux études de recherche précédentes. Cette approche métagénomique est prometteuse pour de nouveaux biomarqueurs prédictifs et des approches thérapeutiques innovantes pour les diabètes de types 1 et 2.
ِج َﻼِﻌﻟا َقُﺮ ُﻃ ﱠنَأ ىَﺪ َﻋ ، ِﻦِﯿِﯾ َﻼَﻤﻟا ِتﺎَﺌِﻣ َﻎُﻠْﺒَﺘِﻟ ِﺔَﻠِﺒْﻘُﻤﻟا ِﺔَﻠﯿِﻠَﻘﻟا ِدﻮُﻘُﻌﻟا ىَﺪَﻣ ﻰَﻠَﻋ يِِﺮﱠﻜﱡﺴﻟا َو ِﺔَﻨْﻤﱡﺴﻟﺎِﺑ َﻦﯿِﺑﺎ َﺼُﻤﻟا ُداَﺪْﻋَأ َداَد ْﺰَﺗ ْنَأ ِﻊﱠﻗَﻮَﺘُﻤﻟا َﻦِﻣ ..ٍةَﺪﯾِﺪ َﺟ ٍﺔَﯿ ِﺟ َﻼ ِﻋ ٍقُﺮ ُﻃ َﺮﯾِﻮ ْﻄَﺗ ُﺐﱠﻠ َﻄَﺘَﯾ َو ،ٍﺔَﻟﺎﱠﻌَﻓ َﺮْﯿَﻏ ﺎًﻗُﺮ ُﻃ ﺎَﻬُﻠَﻌ ْﺠَﯾ ﺎَﻣ ، ِبﺎَﺒ ْﺳَﻷا َﻻ ِﺞِﺋﺎَﺘﱠﻨﻟا ِﺔَﺠَﻟﺎَﻌُﻣ ﻰَﻟِإ ُفِﺪْﻬَﺗ ِﺔﱠﯿِﻟﺎَﺤﻟا ﺎَﻬَﻟ ُنﻮُﻜَﯾ ْﺪَﻗ ﱢﻲِﺋاَﺬِﻐﻟا ِمﺎ َﻈﱢﻨﻟا ﻲِﻓ ِتﺎَﻨﯿِﺴ ْﺤﱠﺘﻟا ﱠنَأ ِﻦﯿِﺣ ﻲِﻓ ، ِضاَﺮْﻣَﻷا َﻦِﻣ 20%-10 ـِﺑ ﱠﻻِإ ِﺆُﺒَﻨﱠﺘﻟا َﻦِﻣ ُﻦﱢﻜَﻤٌﺗ َﻻ ﱢﻲِﺛاَرِﻮﻟا ِﺮَﺒَﺨﻟا َﺔَﺳاَرِد ﱠنِإ ِﺔَﯿِﺌْﯿَﺒﻟا ِﺮ َﻄ َﺨﻟا َﻞِﻣاَﻮ َﻋ َﻦْﯿَﺑ ُﻂِﺑ ْﺮَﺗ ﻲِﺘﻟا ِﺔَﯿِﺌْﯾَﺰ ُﺠﻟا ِﻚِﻟﺎ َﺴَﻤﻟا َو ِﺔَﯾِﻮَﻌَﻤﻟا ٍةَرﻮُﻠْﻔْﻠِﻟ ﱢﻲِﻨﯿ ِﺠﻟا َىﻮَﺘ ْﺤُﻤﻟا َﺔ َﺳاَرِد ﱠنِﺈَﻓ ، َﻚِﻟَذ َﻊَﻣَو ،ٌﺮﯿِﺒَﻛ ٌﺮﯿِﺛْﺄَﺗ .ﻲِﺛا َرِﻮﻟا ِعُﻮَﻨﱠﺘﻠِﻟ ٍﻲِﻔَﺧ ٍرَﺪ ْﺼَﻤِﻟ ِﺮﯿِﺧَﻷا ِفﺎَﺸِﺘْﻛ ِﻻا ﻰﱠﺘَﺣ ُﺪْﻌَﺑ ًﺔَﻌﱠﻗَﻮَﺘُﻣ ْﻦُﻜَﺗ ْﻢَﻟ ﱢﻲِﺛاَرِﻮﻟا َداَﺪْﻌِﺘِﺳﻻاَو ﺎَﻤِﺑ Amplicons ِﺔَﻨﱢﯿ َﻋ 35 ُﻞﯿِﻠ ْﺤَﺗ ﱠﻢَﺗ ، 2 و 1 ِعْﻮﱠﻨﻟا َﻦِﻣ يِﺮﱠﻜﱡﺴﻟا ﻰ َﺿْﺮَﻣ ىَﺪَﻟ ِﺔَﯾِﻮَﻌَﻤﻟا ِةَرﻮُﻠْﻔﻟا ِﺔَﺒﯿِﻛْﺮَﺘﻟا ِتﺎَﻓ َﻼِﺘ ْﺧا ِﺔَﺳاَرِد ِﻞ ْﺟَأ َﻦِﻣ ٍة َﺮﯿِﺒَﻛ ٍتﺎَﻓ َﻼِﺘ ْﺧا ُﺞِﺋﺎَﺘﱠﻨﻟا ِتَﺮَﻬ ْﻇَأَو .ِﺔَﯿِﻣﻮُﺛ ْﺮ ُﺠﻟا ِتﺎَﻨِﻛﺎ ﱠﺴﻠِﻟ ﻲِﻨﯿ ِﺠﻟا ىَﻮَﺘ ْﺤُﻤﻟا ِﻞﯿِﻠ ْﺤَﺗ ِﻞِﺋﺎ َﺳَو ِﺔ َﻄ ِﺳاَﻮِﺑ َﻦﯿِﻤﯿِﻠ َﺳ ٍصﺎ َﺨ ْﺷَأ ْﻦِﻣ ٍتﺎَﻨﱢﯿ َﻋ َﻚِﻟَذ ﻲِﻓ ِﻞ ْﺟَأ ْﻦِﻣ ِﺪﯾِﺰَﻤﻟا ِﻢْﻬَﻓ ﻰَﻟِإ ُﺔَﺟﺎَﺣ َكﺎَﻨُﻫ ،َﻚِﻟَذ َﻊَﻣَو .ِﺔَﻘِﺑﺎﱠﺴﻟا ِثﺎَﺤْﺑَﻷا َﻊَﻣ ُﻖِﻔﱠﺘَﯾ ﺎَﻤِﺑ ،ٍﺔَﻟﺎَﺣ ﱢﻞُﻛ َﺪْﻨِﻋ ِةَدﻮُﺟْﻮَﻤﻟا ِعاَﻮْﻧَﻷا ِةَدﺎَﯿِﺳ َو ِةَﺮْﻓَو ﻲِﻓ ِﺔَﻤ ِﻈْﻧَﻷا ِل َﻼ ِﺧ ْﻦِﻣ ِﺔَﯾِﻮَﻌَﻤﻟا ِةَرﻮُﻠْﻔﻟا ِﻢ ْﻋَد ﻰَﻠَﻋ ُﺰﱢﻛَﺮَﺗ ٍﺔَﯿِﺟ َﻼِﻋ ٍقُﺮُﻃ ِﺮﯾِﻮ ْﻄَﺗ َو ِﺔَﯾِﺆﱡﺒَﻨﱠﺘﻟا ِﺔَﯾِﻮَﯿَﺤﻟا ِتاَﺮﱢﺷَﺆُﻤﻟا ﻰَﻠَﻋ ًﻻﱠوَأ ،ِءْﻮ ﱠﻀﻟا ِﻂﯿِﻠْﺴَﺗ . ِﺞِﺋﺎَﺘﱠﻨﻟا َﻻ ِبﺎَﺒ ْﺳَﻷا َﻮ ْﺤَﻧ ٍﺔَﻬﱠﺟَﻮُﻣ ٍةَﺪﯾِﺪَﺟ ٍجَﻼِﻋ ِتﺎَﯿِﺠﯿِﺗاَﺮَﺘْﺳا ،ﺎًﯿِﻧﺎَﺛ .ِﺔَﯿِﺋاَﺬِﻐﻟا
Dedication
To
My beloved family who gave me lessons in spiritual things and taught me that even
the largest tasks can be accomplished if it is done one step at a time.
To
My brother and sister for their unconditional love and support.
All my classmates for the shared tears, laughter and moments of happiness.
All the MedBiotech team
Acknowledgements
Many thanks to my master thesis advisor Pr. Hassan Ghazal. His oversight,
direction, and encouragement helped me put this project together and without
whom this would not have been possible. He never forgot to ask me if I was
doing well which was a very thoughtful act of him. Thank you!
I would like to thank Pr. Azeddine Ibrahimi for being an excellent head of the
Medical Biotechnology Laboratory and an upstanding coordinator of the
MedBiotech Master program at the Medical and Pharmacy School of Rabat.
My thanks go also to Pr. Timinouni Mohamed, Pr. Rajae Chahboune and Pr.
Imane Allali for accepting to assess my work. Thank you very much for the time
you've put into both examining this manuscript and taking part in it as
committee members.
Finally, I would like to thank my family for their continued support throughout
the years. And of course My dear friends Abdellah and Nihal that supported me
virtually and emotionally over these 6 months.
Table of Content
Introduction
1
Diabetes epidemiology and prevalence
2
Gut microbiota
3
Diabetes and the gut microbiota
4
Metagenomics analysis
5
Shotgun metagenomics 7
Amplicon- based metagenomics 7
Material and Methods
11
Data set
11
Collection of Biological material and DNA extraction
11
Amplicon metagenomics Analysis workflow
12
Data Preprocessing 13
Generating an ASV table 13
Taxonomy Assignment and Phylogeny construction 13
Alpha and Beta diversity 14
Differential taxonomy profiling 15
Results
16
Quality profiles
16
Alpha diversity results
21
Beta diversity results
24
Taxonomy profiling
27
Differential taxonomy profile analysis
27
Discussion
32
Conclusion
33
References
34
List of figures
Figure 1: Summary of a metagenomics workflow Page 6
Figure 2: Metagenome profiling Page 9
Figure 3: DADA2 Metagenomics workflow Page 12 Figure 4: Quality profile plot of the forward sequence reads Page 17
Figure 5: Quality profile plot of the reverse sequence reads Page 18
Figure 6: Quality profile plot after trimming and filtration Page 19
Figure 7: Estimated PCR errors plot Page 20
Figure 8: Box plot of the average Shannon Wiener diversity index Page 21
Figure 9: Box plot of a Simpson’s index diversity Page 22
Figure 10: ACE index box plot Page 23
Figure 11: Non-metric multidimensional scaling according to gender Page 25
Figure 12: Non-metric multidimensional scaling according to age Page 26
Figure 13: Bar plot depicting the microbial abundance at the Family level Page 28 Figure 14: Bar plot depicting the microbial abundance at the Genera level Page 29 Figure 15: Differential taxonomy profile between control and TID subjects Page 30 Figure 16: Differential taxonomy profile between control and TIID subjects Page 31
List of Tables
Table 1: Advantages and limitations of methods used in microbiome research Page 10
Table 2: Metadata presenting the Age and the gender distribution among cohort groups Page 11 Table 3:List of all tools.commands used in this analysis Page 16
List of Abbreviations
ACE: Abundance-based Coverage Estimators ASV: Amplicon Sequence Variant
DADA2: Divisive Amplicon Denoising Algorithm FAPROTAX: Functional Annotation of Prokaryotic Taxa GTR: General Time Reversible
HLA: Human Leukocyte Antigen
KEGG: Kyoto Encyclopedia of Genes and Genomes MSA: Multiple Sequences Alignment
NGS: Next-Generation Sequencing
NMDS: Non-Parametric Multidimensional Scaling NOD: Non-Obese diabetic
OTU: Operational Taxonomic Unit PCR: Polymerase Chain Reaction QC: Quality Control
TIID: Type 2 Diabetes TID: Type 1 Diabetes
I.
Introduction
Diabetes is a complicated metabolic disease distinguished by raised sugar levels in our blood due to insulin secretion deficiency, the lack of insulin activity or both 1. Diabetes can
provoke health complications if it is immoderate, namely stroke, and coronary heart disease2. The International Diabetes Federation (IDF) reported that about 425 million people
around the world had diabetes in 2017 3. This number is expected to rise to 629 million
adults by 2045, a rise of 48 percent 3. Africa is believed to even have 15.9 million inhabitants
with diabetes, a geographic incidence of 3.1 per cent 3. The continent of Africa has the
highest percentage of people with non-diagnosed diabetes, and global forecasts indicate that it will face the highest potential rise in diabetes encumber of about 156 per cent by 20453.
Type 1 diabetes (TID), type 2 diabetes (TIID) and gestational diabetes are the most common classification. TIID is associated with insulin resistance and significant deficiency in insulin secretion4. Total insulin concentration in plasma is generally elevated, but "similar" to the
intensity of insulin resistance, the plasma insulin concentration is inadequate to sustain homeostasis4. Eventually, insulin secretion capability has gradually deteriorated over time in
most patients with TIID4. In certain cases, TID results show an utter deficit in β-cell
activity4, these β-cells are endocrine cells responsible for the synthesis of
anti-hyperglycemic hormone insulin38. Autoimmune destruction of beta-cells is a common
cause, although cases appear to be identified as idiopathic4.
The latest evidence, guided by advancements in both 16s rRNA amplicon sequencing and shotgun metagenomics, has shown that the intestinal microbiome incorporates 100 or even more genes than those of the Human genome 5,6. Such microorganism genes are known to be
essential to metabolic processes involving the host, comprising catabolism of fatty acids, amino acids biosynthesis, along with assisting the secretion of neurotransmitters and hormones5. Over the last decade, numerous studies aimed towards understanding the role of
intestinal microbiota in TIID and glycemic regulation. Prior studies have suggested that intestinal microbiota disruptions can have an impact on the development of TIID 7,8. Le
Chatelier et al.9 and Cotillard et al .10 showed the importance of microbiota diversity in
metabolic processes' regulation. The authors stated correlation between decreased microbiota diversity with not only obesity but also with higher insulin resistance prevalence9,10.
TID is known to be linked with a mutation in the Human leukocyte antigen (HLA) gene 11.
Environmental factors also contribute to the high prevalence of the disease 11. Analysis of
eight infants, of which four with newly formed TIDs and four control subjects, found variations in the composition of intestinal metagenomes between the two groups and decreased variation in TID associated microbiomes 12. Some other research in non-obese
diabetic (NOD) mice has shown that germ-free NOD mice seem to be more prone to diabetes, implying the involvement of intestinal microbiota in the emergence of autoimmune diabetes13.
In order to assess the characteristics of intestinal microbiota in TID and TIID, we propose to analyze the 16s rDNA of 22 North African individuals from Egypt (7 healthy (controls), 7 TID and 6 TIID). This metagenomics study will allow comparison of the composition, abundance and richness of controls, TID and TIID microbiomes of these cases. This will facilitate the discovery of new biomarkers for diagnostic and innovative therapies for diabetes Type 1 and 2 in the North African populations.
1. Diabetes epidemiology and prevalence
Diabetes is one of the leading causes of death in the world and 1.6 million deaths are directly attributed to diabetes each year 14. It is predicted that 422 million people had
diabetes in 2016; this will have increased to 552 million by 2030 15. The amount of people
with TIID is on the climb in each and every country with 80 percent of diabetes patients residing in low-and middle-income countries 16. Diabetes has caused 4.2 million deaths in
2019 and it is predicted that 439 million people will have TIID by 2030 17. The prevalence of
TIID ranges considerably from one geographical area to another due to environmental and life style risk factors18. Few data is available on the prevalence of TIID in Africa as a whole.
Studies analyzing data patterns in Africa show signs of a significant rise in prevalence in both rural and urban environments, affecting both genders equally19. The bulk of the
diabetes burden in Africa seems to be TIID, with fewer than 10 % of individuals of diabetes considered TID16.
Diabetes is a significant and widespread health issue in North Africa. Diabetes prevalence varied from 2.6% in rural Sudan to 20.0% in urban Egypt 48. Diabetes incidence in urban
Africa countries with a prevalence of about 18% and 75% 48. The incidence of chronic
diabetes complications varied significantly from 8.1% to 41.5% for retinopathy, 21% to 22% for albuminuria, 6.7% to 46.3% for nephropathy and 21.9% to 60 percent for neuropathy48.
According to the International Diabetes Federation (IDF) (Update of 25th February 2020), Morocco is among the 21 stcountries and territories of the IDF Middle East and North Africa
(MENA)49. Globally, 463 million people have diabetes and 55 million people in the MENA
region, will increase rapidly to attain 108 million by 204549.
2. Gut microbiota
The Human microbiota comprises 10-100 trillion symbiotic microbial cells that are harbored by each individual, mainly bacteria in the intestine; the human microbiome contains genes that these cells bear20. Microbiome projects have been initiated globally with the purpose of
recognizing the functions of these symbiotic organisms and their effects on Human health and disease20.
Microbes that live in the Human intestines are important contributors to host metabolism and are considered possible sources of novel therapeutics 21. While this sentence can be seen
as evident in 2018, the universality of this definition is less apparent 21. Unquestionably, by
dint of the emergence of genetic tools and metagenomic revolution over the last fifteen years, we are now in a position to study the composition and role of microbiomes from various parts of the body and relate them to possible pathogens, concerns or even to a specific onset of clinical symptoms21. Additionally to bacteria, other primary
microorganisms, including archaea, viruses, phages, yeast and fungi, can be found in the intestines21. These microorganisms are likely regulating the function of the host. Most
notably, the intestinal microbiota, have been researched in detail and may be as significant as bacteria21. Archaea, virome, phagosome and mycobiome thus give an additional aspect to
the study of host-microorganism interactions 21. As more of an example, phages not only
outweigh the number of bacteria, but they are also new players who act as an important part of these dynamic interactions21. The intestinal microbiota has been involved in the
modulation of people's wellbeing and metabolism. This microbial "organ" has indeed been associated with chronic diseases such as diabetes and has also been included to affect systemic functions, especially immunity21.
To better understand the role that intestinal microbes play in health and disease, researchers from around the globe are exploring what makes a 'healthy' intestinal microbiota 22. There
are, simply put, thousands (~1952)39 of different bacterial species in the intestine, some
pathogenic and some beneficial22. Computational biologists argue that the collection of
microbiome data would enable a 'deep phenotyping' approach that might change drug discovery22. The research of certain probiotic microorganisms that support health yields
biological knowledge which might enhance the development of new medicines 22. Numerous
diseases are already believed to be affected by gut flora processes 22. These include for
example cancer, autoimmune diseases such as multiple sclerosis, autism spectrum disorder, and diabetes22.
3. Diabetes and the gut microbiota
Many studies have been done to grasp the difference between microbiome composition and richness among diabetic people and healthy ones. Variations in the composition of microorganism species in the microbiota may anticipate the progression towards diabetes 23.
After going through 259 articles including pieces of information about the Gut microbiota and diabetes, Grigorescu and Dumitrascu concluded that there are variations in the composition of the intestinal microbiota in healthy subjects; whilst patients with TIID had a substantial reduction in Clostridium populations and a rise in the numbers of Lactobacillus and Bifidobacterium23. They also highlighted that early variations in the composition of
intestinal microbiota (larger amount of Bifidobacteria) serve as diagnostic indicators for the progression of TIID predisposed individuals23.
Evidently, intestinal microbiota analyzes across various populations have demonstrated the production of distinctive enterotypes dependent on the relative abundance of the relevant genera: Bacteroides, Prevotella, and Ruminococcus24. Other experiments have shown that
enterotypes are mainly dominated by the ratio of the two dominant genera: Bacteroides and Prevotella24. The Bacteroides and Prevotella ratios tended to be determined by diet patterns.
Prevotella enterotype was correlated with elevated carbohydrates and simple sugar while the Bacteroides enterotype was linked with animal protein, a variety of amino acids and saturated fats24.
In this line of evidence, recent findings from a major meta-analysis of enterotypes in the human intestine indicated that intestinal bacterial species exhibit a smooth gradient of main
genera instead of drop into discrete clusters 25. The intestinal microbiota may be defined in
terms of different enterotypes that can be of significant importance to health, particularly the microorganisms that influence the host’s vulnerability to infection or disease 25. Intriguingly,
enterotypes have been shown to be closely correlated with diet in healthy subjects, especially protein and animal fat (Bacteroides enterotype) and carbohydrate (Prevotella enterotype)25. It has recently been proposed that children with TID have Bacteroides
enterotypes, whereas their healthy counterparts have Prevotella enterotypes 25.
4.
Metagenomics analysis
Metagenomics is characterized as a direct genetic study of genomes within an environmental sample. Initially, the field began with the cloning of environmental DNA, followed by the screening of functional expression, but was then rapidly supplemented by direct random environmental DNA shotguns sequencing26.
Metagenomics offers insight to the functional gene makeup of microbial populations and thus provides a significantly wider overview than phylogenetic surveys, most of which are focused primarily on the diversity of a single gene, such as the 16S rRNA gene 26.
Metagenomics itself offers genetic knowledge on possibly novel biocatalysts or enzymes, the genomic linkages between activity and phylogeny for uncultivated organisms, and the evolutionary profiles of population structure and function 26. It can also be complemented by
metatranscriptomic, metaproteomic or metabolomics approaches to the classification of expressed activities26.
Sample processing is perhaps the most critical step in every metagenomic study. DNA collected should be reflective of all the cells present in the sample and appropriate concentrations of good nucleic acids quality must be produced for subsequent production and sequencing of the library 26. Processing involves particular procedures for each type of
sample and numerous rigorous methods for DNA extraction exist26.
A common shotgun metagenomics research consists of five stages following the initial design of the study: (1) the collection, preparation and samples sequencing; (2) pre-processing of read sequences; (3) analysis of the taxonomical, functional and genomic characteristics profile of the microbiome; (4) post-processing study including statistical and biological analysis; and (5) validation (Figure 1)27. Various experimental and computational
confounding options27. Given the obvious simplicity, shotgun metagenomics has limitations
due to possible laboratory biases as well as the difficulty of computational analyzes and their interpretations27.
Figure 1: Summary of a metagenomics workflow 27. Step (I): design of the research and
experimental procedure. Step (II): pre-processing. Computational quality control (QC) steps eliminate underlying sequence biases or artifacts such as removing sequence adapters, quality trimming, removal of sequencing duplicates (e.g. using FastQC, Trimmomatic). Foreign or non-targeted DNA sequences are however filtered, and samples are subsampled to normalize reading numbers when comparing the diversity of taxa or functions. Step (III): a review of sequences. This could require a mixture of 'read-based' and 'assembly-based' methods depending on the experimental goals. Step (IV): the post-processing process. Numerous multivariate statistical methods can be used to analyze the results. Step (V): the validation process.
4.1. Shotgun metagenomics
Over the last 10 years, metagenomic shotgun sequencing has moved increasingly from classical Sanger sequencing technology to next-generation sequencing (NGS). Nevertheless, Sanger sequencing is now believed to be the best standard for sequencing due to its low error rate, and reads with long length (> 700 bp) 27. All of these things would enhance
assembly results for shotgun data, and thus Sanger sequences can still be relevant if the generation of close-to-complete genomes in low-diversity environments is the objective 27.
Amongst NGS technologies, in addition to the 454 / Roche (California?, USA), the Illumina (California, USA) systems have now been widely extended to metagenomics samples27.
Yet, there are some limitations and challenges of metagenomic shotgun analyses that need to be taken into consideration 27. These limitations include the price which is still very high
for large numbers of metagenomes. Profiling of the functional classes present in a metagenome can be hampered by the absence of confirmed annotations for multiple genes27. 4.2. Amplicon- based metagenomics
The framework through amplicon and metagenomic research has been developed over the last decade with the growth of sequencing methods that have both advantages and limitations (Figure 2)28. Even so, the strategies and methods of microbiome research have
grown rapidly over the last few years. For instance, a proposal was made to substitute operational taxonomic units (OTUs) with amplicon sequence variants (ASVs) in marker gene-based amplicon data analysis28. In parallel, novel approaches for taxonomic
classification, machine learning and integrated multi-omics research have recently been suggested28.
The very first step in amplicon analysis is to convert raw reads (in fastq format) into a feature table which describes what features are present in your sequence 40. The first line of
the feature table comprises a basic piece of information as follows “ >Feature Sequence_ID ”, the sequence identifier (Sequence_ID) must match the label utilized to determine each table's corresponding sequence within the nucleotide fasta file40. Often, raw reads are
paired-end and generated from the Illumina sequencing technology28. Firstly, the raw
amplicon paired-end reads are arranged according to their barcode sequences (demultiplexing)28. Then the paired reads are combined to acquire amplicon sequences, and
the barcode and primers are omitted. Usually, a quality-control step is required to eliminate low-quality amplicon sequences28. In the amplicon analysis, representative sequences picked
as species proxies is a crucial step 28. Two main approaches known for selecting
representative sequences are namely OTU clustering and ASV denoising. UPARSE41
algorithm clusters sequences with a 97% similarity to OTUs. That being said, this approach does not distinguish slight distinctions between organisms or strains 28. DADA2 is a newly
conceived denoising algorithm that outputs ASVs as more reliably representative sequences28. At last, a feature table (OTU / ASV table) can be generated by quantifying the
frequency of the feature sequences (primarily any unit of observation, it can be an OTU, a sequence variant, a gene, a metabolite, etc,...) from every sample. At the same time, the feature sequences may be assigned to taxonomy, usually at the kingdom, phylum, class, order, family, genus, and species levels, offering a dimensional perspective upon the microbiota28.
Generally, 16S rDNA amplicon sequences may be utilized to only gain pieces of information about the gut microbiome taxonomic composition28. Even so, a range of
existing software packages have been developed to predict potential functional information28. The principle behind this prediction is to relate the 16S rDNA sequences or
taxonomy information with functional descriptions in literature 28. PICRUSt, which is
founded on the OTU table of the Greengenes 16S rRNA gene database, may be utilized to estimate the metagenomic functional composition using the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways28. The recently created PICRUSt2 software package
(https:/github.com/picrust/picrust2) can explicitly predict metagenomic functions with a random OTU / ASV table. The R package Tax4Fun can predict KEGG functional capabilities of microbiota based on the SILVA database of aligned ribosomal RNA (rRNA) gene sequences within the Bacteria, Archaea and Eukarya domains28,50. The functional
annotation of prokaryotic taxa (FAPROTAX) pipeline performs functional annotation based on published metabolic and ecological functions namely nitrate respiration, iron respiration, plant pathogen, and animal parasites or symbionts, rendering it beneficial for environmental, agricultural, and animal microbiome research28. BugBase is an extended database of
Greengenes utilized in phenotypes predicting like oxygen tolerance, Gram staining, and pathogenic potential; this latter database is mainly used in medical research28.
Figure 2: Metagenome profiling: shotgun metagenome with an assembly-based profiling and a 16s amplicon-based profiling27
Table 1: Advantages and limitations of methods used in microbiome research28
A: Introduction of methods at diverse levels of analysis. At the molecular level, microbiome research can be classified into 3 types: microbes, DNA, and mRNA. Corresponding analysis methods include culturome, amplicon, metagenome, metavirome, and metatranscriptome analysis. B: The pros and cons of different approaches studies of microbiomes
Method Advantages Limitations
Culturome - High throughput - Targeted selection - Provides microbial isolates - Expensive - Laborious - Influenced by the environment Amplicon (16s/18s) - Quick analysis - Low biomass requirement - Applicable to samples contaminated by host DNA
- PCR and primer biases - Resolution limited to
genus level
- False positive in low biomass samples
Metagenome - Taxonomic resolution to species or strain level - Functional potential - Uncultured microbial genome - Expensive - Time consuming in analysis - Host derived contamination
Virome - Can identify RNA or
DNA viruses - Quick diagnosis
- Most expensive - Difficult to analyse - Severe host derived
contamination Metatranscriptome - Can identify live
microbes
- Can evaluate microbial activity
- Transcript level responses
- Complex sample collection and analysis - Expensive and complex in
sequencing
- Host mRNA or rRNA contamination
II.
Material and Methods
This study was performed using the High Performance Computing (https://www.marwan.ma/index.php/services/hpc) server of the Moroccan Academic and Research Wide Area Network (MARWAN) department at the National Center for Scientific and Technical Research in Rabat (CNRST).
1. Data set
All data used in this study are publicly available in the NCBI Sequence Read Archive (SRA) data repository. A total of 35 (7 controls, 15 TID and 13 TIID) samples of North African individuals (Egypt) with different ages and genders (Table 1). Describe these data (16SRNA genes, etc… ). The hypervariable region V4 of each DNA sample was sequenced using Illumina Miseq (California, USA), generating paired-end reads with an average size of 250 bases for each, and the average read amount of 266000 reads (see Annexes: Table 3).
Table 1. Metadata presenting the Age and the gender distribution among cohort groups
TID: Type 1 Diabetes, TIID: Type 2 Diabetes
2. Collection of Biological material and DNA extraction
Sample collection was carried out on-site in Egypt 50. Concisely, fecal samples were obtained
promptly after defecation and put in a pre prepared saline solution Eppendorf tube 50. These
isolates were stored at – 20 °C until they were analyzed. DNA was isolated from about 0.3~0.5 g from every stool sample utilizing the QIAamp DNA Stool Mini Kit (Qiagen, Hilden, Germany) as instructed by the manufacturer 50. The concentration and consistency of
Subject TID TIID Healthy Controls
Male 5 9 2
Female 10 4 5
Age 32-62 32-62 24-57
DNA was analyzed using Qubit 2.0 fluorometer (Invitrogen, California USA) and gel electrophoresis50.
3. Amplicon metagenomics Analysis workflow
In order to construct a “sample-by-sequence” feature table from the raw reads, to assign taxonomy and create the phylogenetic tree relating the sample sequences, DADA2 platform was used to analyse the 16S rRNA amplicons dataset. Phyloseq uses the “ Amplicon Sequence Variant” (ASV) table to calculate the proportion of each taxon and sort them then calculate the “alpha diversity” defined as the variance within a specific sample. The Phyloseq package is a tool to import, store, analyze, and graphically display complex phylogenetic sequencing data that has already been clustered into Operational Taxonomic Units (OTUs) or ASVs. Decipher is a tool for Multi Sequence Alignment (MSA) and phylogeny42. It helps create a distance phylogeny tree, which is used as an input in
Phyloseq43 to calculate the “beta diversity” defined as how samples differ from each other.
DESeq244was also used to calculate the beta diversity using the Phyloseq results. DESeq2
performs an internal normalization where geometric mean is calculated for each gene across all samples. The counts for a gene in every sample is therefore split by this mean. The median of these ratios in a sample represents the size factor of that sample. This process adjusts biases for library size and RNA composition, which may occur for example when only a few number of genes are indeed highly expressed in one experiment condition but not in the other.
utilizing quality scores to trim. The dereplication outputs unique sequences and their abundance ASVs. The construction of the Phyloseq object is similar to a Biom file and the second filtrations eliminate abundance reads rarefaction
2.1. Data Preprocessing
Before analyzing the sequences, the raw data obtained from the sequencer need to be preprocessed which comprises the removal of adapters, duplicates, and contaminations. Quality profile of each sample was plotted using “PlotQualityProfile” command from DADA2 package. This command generates a heatmap plot of “quality per position”, with information about the read length distribution. Data with low quality was trimmed using the “FilterAndTrim'' command from DADA2 R package, using a score quality threshold of 20 and removing Phix sequences defined as nontailed bacteriophage with a single DNA strand which are used to control the Illumina sequencing runs due to their small and well known genome45, and adjusting the read length to be above 160 bp. The PCR error rate was
estimated for each sequence and, using DADA2 inference, the aforementioned errors were removed. Replicated sequences were removed from the output files, and the forward and reverse sequences were merged (Figure 3).
2.2. Generating an ASV table
The ASV table is a feature table of amplicon sequence variants, a matrix of rows corresponding to samples and columns corresponding to ASVs, wherein the value within each entry is the couple of times the ASV has been found in that sample 29. This table is
similar to the standard OTU table, but for higher precision, i.e. exact amplicon sequence variants instead of (generally 95%) sequence read clusters 29. Merged sequences were used to
generate an ASV table resulting in 35 different ASVs, then chimeric sequences were removed from the table which represented 5% of the whole table.
2.3. Taxonomy Assignment and Phylogeny construction
Taxonomy assignments were rendered by searching for input sequences against a fasta database of prior-assigned reference sequences 30. All matches that complement the query are
obtained within 0.5 percent identity of the best match 30. A taxonomy assignment shall be
To assign taxonomy to the ASV table, the Ribosomal Database Project (RDP; http://rdp.cme.msu.edu/) database was used which resulted in a table utilized for the multiple sequence alignment with “Decipher” tool. Therefore, a distance matrix was generated using “phangorn”,a package for phylogenetic reconstruction and analysis in the R language, then a “neighbor joining” was performed which is a bottom-up clustering method whereas each observation is assigned to its own cluster for the creation of phylogenetic trees46. Furthermore, a maximum likelihood “General Time Reversible” (GTR) was
estimated using the same package with a model of GTR 46, and a phylogeny tree was
constructed.
2.4. Alpha and Beta diversity
Alpha diversity is the variance within a specific sample. Often measured as a single number from 0 which equals no diversity to infinity, or in a few times as a percentile, this is what is meant when we look at the microbiome results and ask about diversity 31. Alpha diversity
refers to the diversity within a particular area or ecosystem and is frequently expressed by the number of species (i.e., species richness) within that environment31.
Whilst Beta diversity is how samples differ from each other. Many researchers are focused in the variations between sites on the body, or microbiomes across geographic locations 31.
Beta diversity is typically the thinking behind “clustering” algorithms that try to show differences or similarities amongst samples 31. Examining the differences in species diversity
between two ecosystems means measuring the beta diversity31. Thus, by counting the total
number of species that are unique to each of the ecosystems being compared 31. All diversity
measures take into consideration two dimensions of a community namely the number of distinct entities in a sample, and the scope of abundances for each one31.
The phylogenetic tree and ASV table were fed to “Phyloseq” in order to create a Phyloseq object, This latter was used to calculate the alpha diversity using Shannon, Abundance-based Coverage Estimator) of species richness (ACE), and Simpson indices. The “Shannon wiener” and “Simpson” indices include the measurement of individual evenness distribution in communities, ACE refers to the abundance of each taxon. Then a Beta diversity plot was generated as a Non-parametric Multidimensional Scaling (NMDS) plot, which represents the plot distances between samples.
2.5. Differential taxonomy profiling
Taxonomic profiling raises the question "Who is there?", and provides an overview of the taxonomic composition in each studied sample 32. Besides the characterization of the taxa
present in the sample, the abundance of species is also determined in this analysis method 32.
As a result, the taxonomic profile would include both a list of identified taxa and their approximate relative abundances and also the different diversity indices (Figure 2)32.
The development of a taxonomic profile is not a small undertaking, since metagenomic samples include the genetic material of millions of distinct organisms from thousands of different species32. All molecules are sequenced together, meaning that the reads we get
from a sequencer may arrive from all of these species 32. Short reads length, high similarity
in the sequence of genes and genomes, variations in length, low DNA output in certain cases, or lack of bioinformatics resources and databases, all make the issue of microbial detection and taxonomic profiling very complicated32.
In order to study the differences between controls and diabetic patients, differential taxonomy profiles and the taxonomy table were generated using the DESeq2 package. Table 3 summaries all the bioinformatics tools, commands, and databases that have been used in this analysis workflow.
Table 3. List of all tools.commands used in this analysis
III.
Results
1.
Quality profiles
The heat map quality plot of the raw reads shows that the quality score of the forward reads is higher than 30, and all reads have the same length ( Figure 4). The reverse sequence reads
as well and has the same length, but the quantiles of the bases above position 150 were all below a quality score of 20, which requires a trimming step ( Figure 5). After trimming and
filtration, all bases’ quality score was adjusted to be higher than 30 ( Figure 6). The estimated PCR errors plot shows that the error frequency of changing base to base can be equal or lower than 10 -3for bases with a quality score of 30, which still need to be adjusted
(filtered) (Figure 7).
Tool/Command name Role/Function Reference/website
DADA2 Picking ASVs
https://www.ncbi.nlm.nih.gov/pmc/ articles/PMC4927377/
Decipher Multi Sequence Alignment
(MSA) and phylogeny https://bmcbioinformatics.biomedce ntral.com/articles/10.1186/s12859-0 15-0749-z
Phyloseq Taxonomy profiling and
diversity https://journals.plos.org/plosone/arti cle?id=10.1371/journal.pone.00612 17
DESeq2 Differential taxonomy
profiling https://genomebiology.biomedcentr al.com/articles/10.1186/s13059-014 -0550-8
Figure 4: Quality profile plot of the forward sequence reads obtained from the Illumina sequencing output of 16S rRNA amplicons from one stool sample of a diabetic patient;
Cycle: PCR cycles; Quality score: the estimated quality per base for each cycle
In grey-scale is a heat map of the frequency of each quality score at each base position. The mean quality score at each position is shown by the green line and the quartiles of the quality score distribution by the orange lines. The red line indicates the scaled amount of reads that reach at at the very least that position.
Figure 5: Quality profile plot of the reverse sequence reads obtained from the Illumina sequencing output of 16S rRNA amplicons from one stool sample of a diabetic patient;
In grey-scale is a heat map of the frequency of each quality score at each base position. The
mean quality score at each position is shown by the green line and the quartiles of the quality score distribution by the orange lines. The red line indicates the scaled amount of reads that reach at at the very least that position.
Figure 6: Quality profile plot after trimming and filtration obtained from the Illumina sequencing output of 16S rRNA amplicons from one stool sample of a diabetic patient ;
in grey-scale is a heat map of the frequency of each quality score at each base position. The mean quality score at each position is shown by the green line and the quartiles of the quality score distribution by the orange lines. The red line indicates the scaled amount of reads that reach at at the very least that position.
Figure 7: Estimated PCR errors plot obtained from the Illumina sequencing output of 16S rDNA amplicons from one sample stool DNA of a diabetic patient. These plots
depict the error frequency of changing base to base and the error rates for each possible transition (A→C, A→G, …) are shown.
Legend: Points are the observed error rates for each consensus quality score. The black line
shows the estimated error rates after convergence of the machine-learning algorithm.
Explanation: Here the estimated error rates (black line) are a good fit to the observed rates (points), meaning that we can proceed with confidence.
2.
Alpha diversity results
Alpha diversity refers to the average species diversity in a habitat or specific area, herein the alpha diversity refers to the richness and evenness of existing species within our samples. This latter can be measured by the Shannon Wiener, Simpson and ACE indices.
The Shannon wiener and Simpson indices show that the diversity of the Type II diabetes in the patients’ cohort samples is higher than the healthy and Type I diabetes patients ( Figure
8, Figure 9). The smaller the Simpson or the greater the Shannon indices, the higher the microbiome diversity of a sample. Whilst the ACE index showed that the species abundance in Type I diabetes in patients cohort samples is higher ( Figure 10); the greater the ACE indices, the higher the expected species richness of the microbiome of a sample.
Figure 8: Box plot of the average Shannon Wiener (alpha) diversity index of 16s rRNA amplicons from 35 stool samples of type 1 diabetic patients (green), type 2 diabetic patients (blue) and healthy controls represented in red ; each box represents
quartiles, the horizontal line within the boxes represents the median whilst the vertical lines that are outside the boxes are the outliers.
The Shannon index is based on the species richness that is simply the number of species in a community. Here we can see clearly that the Shannon Wiener index in TID and healthy control have nearly the same species richness in the microbiome samples.
Figure 9: Box plot of a Simpson’s index (alpha) diversity of 16s rRNA amplicons from 35 stool samples of type 1 diabetic patients (green), type 2 diabetic patients (blue) and healthy controls represented in red ; each box represents quartiles, the horizontal line within the boxes represents the median whilst the vertical lines that are outside the boxes are the outliers.
The smaller the Simpson index, the higher is the diversity of the microbiome of a sample. This indicates that samples obtained from the 16s rRNA amplicons of type 2 diabetic patients were more diverse than the others. The Simpson index is based on the evenness of the species within a sample which shows that the healthy controls and TID have nearly the same species evenness rate.
Figure 10: ACE index Alpha diversity box plot of 16s rRNA amplicons from 35 stool samples of type 1 diabetic patients (green), type 2 diabetic patients (blue) and healthy controls represented in red ; each box represents quartiles, the horizontal line within the boxes represents the median whilst the vertical lines that are outside the boxes are the outliers.
The ACE index showed that the species abundance in Type I diabetes in patients' cohort samples is higher; the greater the ACE indices, the higher the expected species richness of the microbiome of a sample.
3.
Beta diversity results
Beta diversity refers to the variation of microbial communities between samples and to how different is the microbial composition in one environment compared to another.
Non-Metric Distance Scaling (NMDS) plots used to determine the beta diversity in our diabetic cohort samples show a large disparity in terms of distances between samples depending on the case status of each patient, whereas the gender does not affect the distances (Figure 11). Distance metrics are dissimilarity measures, meaning that they are zero when samples are identical and have larger values when the samples are different. Distribution of samples in NMDS plot shows that the distance between samples increases depending on individual age (Figure 12).
Figure 11: Non-metric multidimensional scaling (axes NMDS1 vs. NMDS2) according to gender of the 16s rRNA obtained from 35 microbiome samples comprising healthy cases, TID and TIID. The NMDS test was performed to see if the data is normalized.
The shape of each point represents the case of the patient, colors represent the gender. We can see from the graph that the color points are largely dispersed which means that the gender does not affect distances between samples.
Figure 12: Non-metric multidimensional scaling (axes NMDS1 vs. NMDS2) according to age of the 16s rRNA obtained from 35 microbiome samples comprising healthy cases, TID and TIID. The shape of each point refers to the patient's case, colors represent the age.
The lighter the blue color is, the older is the patient; The NMDS values increase with the age of the patient.
4.
Taxonomy profiling
The identification and abundance of microorganisms found in the microbial population is typically the first step in unravelling the biology of these organisms and is alluded to as taxonomic profiling. More technically, taxonomic profiling is the computational task of implying that taxonomic clades are occupying a given microbial population and in what quantities (relative abundances)47.
The taxonomy plots depict a notable difference between the composition of TID, TIID and healthy subjects microbiomes of 16S rRNA microbiome samples in terms of richness which is is the number of different species represented in a certain community, and abundance (the number of individuals per species) (Figure 13 and 14).
5.
Differential taxonomy profile analysis
In order to study the differences between controls and diabetic patients, differential taxonomy profiles and the taxonomy table were generated using the DESeq2 package. DESeq2 analysis results indicate that with the change from a healthy status to a T1D status, the Genera Terrisporobacter and Coprococcus from the Families Peptostreptococcaceae and Lanchospiracea respectively appear distinctly from other Genera (Figure 15); while the change to TIID induces the appearance of Gemmiger, Escherichia, and Coproccus Genera, from Families Ruminococcaceae, Enterobacteriaceae, and Lanchospireacea respectively characterizing the TIID isolates (Figure 16).
Figure 13: Bar plot depicting the microbial abundance at the Family level within the control, TID and TIID 16S microbiomes samples
We can see from the plot that some microorganisms families are more abundant from a case than another, especially in type 2 diabetes. The Succinivibrionaceae Family is only present in the control isolates; Whilst the Lachnospiraceae is only observed within the TID isolates; The Enterobacteriaceae is not present in the TIID isolates.
Control: Healthy controls
Figure 14: Bar plot depicting the microbial abundance at the Genera level within the control, TID and TIID 16S microbiomes samples
We can see from the plot that some microorganisms Genera are more abundant from a case than another, especially in type 2 and 1 diabetes. The Akkermansia and Roseburia Genera are only present in TID and TIID; The Escherichia Genus is highly abundant in TID, while the Megasphaera Genus is more abundant in TIID. Prevotella Genus is largely abundant in both patients with type 1 and type 2 diabetes
Control: Healthy controls
Figure 15: Differential taxonomy profiling of the microbial Families and Genera between control and type 1 diabetic patients’ obtained from the 16s rRNA microbiome samples.
Each coloured point corresponds to a specific Family that is mentioned on the right side of the Figure.
From the plot we can distinguish the Terrisporobacter Genus from the Porphyromonadaceae Family in a higher log2FC value which means that this latter is more abundant in the TID isolates; The Coprococcus Genus from the Lachnospiraceae is also a marker of the TID samples.
Figure 16: Differential taxonomy profiling of the microbial Families and Genera between control and type 2 diabetic patients obtained from the 16s rRNA microbiome samples.
Each coloured point corresponds to a specific Family that is mentioned on the right side of the Figure.
From the plot, we can distinguish the Gemmiger, Escherichia, and Coproccus Genera, from the Ruminococcaceae, Enterobacteriaceae, and Lanchospireacea Families respectively characterizing the TIID isolates.
IV.
Discussion
Our results were in accordance with earlier findings, as the TIID and TID groups from our cohort displayed a large Genera abundance of opportunistic pathogens such as Prevotella, Roseuria, Escherichia/Shigella and Clostridium (Figure 14). Whilst, the Coproccus genus was exclusively found within the TIID group which suggests that this genus may perhaps be related to hyperglycemia.
These concordant findings include studies that have analysed microbial profiles in TIID. Tilg et al. 33 stated that TIID gut dysbiosis was distinguished by a decrease in Roseburia
intestinalis and F. Prausnitzii suggests a clear correlation between the altered composition
of the microbiota and the inflammatory condition of patients with TIID 33. In addition, a
study conducted by Sircana et al. 34 documented variations between associated microbiota
composition in healthy subjects and others with TIID 34. These alterations in the intestinal
environment may induce inflammation, modify the gut permeability, and alter the metabolism of short-chain fatty acids, bile acids, including metabolites that function in synergy on metabolic control systems that lead to insulin resistance 34. Initiatives that keep
stability in the intestine tend to have positive effects and enhance glycemic regulation. In comparison to prior research, a study undertaken by Salah et al. 34 on Egyptian people found
that obesity and diabetes are linked with an abundant population of Firmicutes and Bacteroidetes34. This may be interpreted by the sort of diet, the high consumption of
carbohydrates and the high populations of Firmicutes and Bacteroidetes are associated, whereas the high fat diet is linked with greater percentage of Firmicutes only 34. Further
research performed by Qin et al. 35indicated a mild level of dysbiosis in diabetic patients
relative to controls, including a decrease in the abundance of different Firmicutes; bacteria containing butyrate, such as Clostridiales, Eubacterium rectale, Faecalibacterium
prausnitzii, and Roseburiain testinalis. Additionally, further opportunistic pathogens have
been reported, such as Clostridium hathewayi, Clostridium ramosum, Clostridium
symbiosum, Bacteroides caccae, Escherichia coli and Eggerthella lenta. Recent studies
indicate a correlation between TID and intestinal microbiota 36. In a rat experiment, Brugman
et al.36 found that, prior to the initiation of TID, the composition of the intestinal microbiota
was considerably variable amongst rats that ultimately developed TID and rats that haven't. Likewise, Roesch et al. 37 reported a marked decline in the abundance of Lactobacillus,
Bryantella, Bifidobacterium, and Turicibacter taxa in diabetic rats, while the abundance of Bacteroides, Eubacterium, and Ruminococcus rose.
Conclusion
The human microbiome has attracted much interest throughout the last 15 years. Whilst intestinal microbes have been studied over several decades, research into the function of human intestinal microorganisms has drawn much interest outside classical infectious diseases. For instance, various studies have documented changes in the intestinal microbiota amid not only obesity, liver disease, cancer and neurodegenerative diseases but also diabetes. Human intestinal microbiota is perceived as a possible source of novel therapeutics in terms of probiotics, prebiotics and fecal transplantation.
Our results confirmed the interaction between Gut Microbiota and Diabetes Types 1 and 2 in a North African population, similarly to other European and North American populations. In particular, the following genera Akkermansia, Roseburia, Escherichia and Prevotella have been systematically over-represented in the Gut Microbiome from Diabetes patients in all populations. However, there were genera that were unique to Egyptian Diabetics patients such as the Coproccus and the Akkermansia Genera.
These studies encourage to enlarge and extend the cohort to other North African countries to confirm these preliminary findings towards a population-specific therapeutics.
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