Investigation of the Relationship Between Akkermansia Genomic Diversity in Gut Microbiota and Parkinson’s Disease Dementia

Parkinson hastalığında (PH), genellikle sağlıkla ilişkilendirilen bir bakteri cinsi olan Akkermansia’nın bağırsak mikrobiyotasında artış gösterdiği bilinse de bu artışın nedeni tam olarak anlaşılamamıştır. Bu çalışmada Türkiye’deki PH hastalarında, bağırsak mikrobiyotasındaki muhtemel Akkermansia değişimlerinin belirlenmesi amaçlanmıştır. Bu amaçla, ilk kez shotgun metagenomik ve Akkermansia cinsine özgül bir yeni nesil dizileme (NGS) tekniği kullanılarak PH’de bilişsel bozukluk evreleriyle ilişkili olabilecek belirli Akkermansia suşlarının varlığı ve bu suşlarda bulunan potansiyel genler incelenmiştir. Bu kapsamda Türkiye’de toplanmış dört bağırsak mikrobiyotası örneği -üç demanslı PH (PHD) ve bir bilişsel bozukluğu olmayan sağlıklı kontrol (SK)- shotgun metagenomik dizileme yoluyla analiz edilmiş ve örneklerdeki Akkermansia cinsine ait genomlar yeniden inşa edilmiştir. Bu genomlar, veri tabanlarındaki Akkermansia cinsine ait genomlarla bir araya getirilerek özel bir veri tabanı oluşturulmuş ve Akkermansia cinsine özgül NGS uyumlu primerler bu veri tabanı kullanılarak tasarlanmıştır. Hedef gen bölgesinin çoğaltılması ve cins özgül yeni nesil dizileme için kütüphane hazırlama basamaklarının optimize edilmesinden sonra, 64 PH hastası [32 PHD ve 32 hafif bilişsel bozukluk gösteren PH (PH-MCI)] ile 26 SK’ye ait bağırsak mikrobiyotası örnekleri cins özgül amplikon dizileme ile analiz edilmiştir. Analizler sonucunda, bağırsak mikrobiyotası örneklerinde Akkermansia muciniphila türüne ait oldukları belirlenen yedi suşun varlığı tespit edilmiş ve iki suşun demanslı (PHD) ve demansı olmayan (PH-MCI, HC) gruplar arasındaki dağılımının anlamlı farklılık gösterdiği (p< 0.05) belirlenmiştir. Tespit edilen suşlara ait genomların gen içerikleri, karşılaştırmalı genomik analizler yoluyla incelediğinde yalnızca dağılımı demanslı ve demansı olmayan gruplar arasında anlamlı farklılık gösteren iki suşta bulunan 12 genin varlığı tahmin edilmiştir. Bu genlerin annotasyonları yapıldığında ise daha önce rapor edilmemiş ve işlevi bilinmeyen genler oldukları görülmüştür. Bu çalışmada, ilk kez Türkiye’de toplanmış PH hastalarına ait bağırsak mikrobiyotası örneklerinin shotgun metagenomik analizleri gerçekleştirilmiş, özel olarak Akkermansia cinsinin analizi için cins-özgül bir amplikon dizileme yöntemi geliştirilmiş ve bu yöntem kullanılarak PH’de bilişsel bozukluk evreleriyle ile ilişkili olabilecek Akkermansia suşları ve genleri tespit edilmiştir. Elde edilen sonuçlar, tür ya da suş düzeyindeki farklılıkların araştırılmasının, bağırsak mikrobiyotasındaki PH ile ilişkili değişimlerin daha iyi anlaşılmasına yardımcı olabileceğine işaret etmektedir.Although it is known that the relative abundance of Akkermansia, a bacterial genus commonly associated with health, increases in the gut microbiota of Parkinson’s disease (PD) patients, the exact reason for this increase remains unclear. This study was aimed to identify potential changes in Akkermansia within the gut microbiota of PD patients in Türkiye. For this purpose, shotgun metagenomics and a novel Akkermansia genus-specific amplicon sequencing technique was used to investigate the presence of specific Akkermansia strains associated with cognitive impairment (CI) stages in PD and to examine potential genes within these strains. In this context, four gut microbiota samples from Türkiye -three PD with dementia (PDD) and one healthy control without CI (HC)- were analyzed by shotgun metagenomics and metagenome-assembled genomes assigned to Akkermansia genus were reconstructed. Then, a custom database was created by combining these genomes with the Akkermansia genomes in public databases and next generation sequencing (NGS) compatible primers specific to the genus Akkermansia were designed using this database. After optimization of amplification and library preparation steps for genus-specific next generation sequencing, gut microbiota samples from 64 PD patients [32 PDD and 32 PD with mild CI (PD-MCI)] and 26 HCs were analyzed by genus-specific amplicon sequencing. The results revealed the presence of seven strains assigned to Akkermansia muciniphila in gut microbiota samples, two of which showed significant distribution differences (p< 0.05) between demented (PDD) and non-demented groups (PD-MCI, HC). When gene contents of the detected Akkermansia genomes were examined through comparative genomic analysis, the presence of 12 genes only in Akkermansia genomes specific to non-demented groups were predicted. The annotations of these genes showed that they were not reported before with unknown functions. In this study, for the first time, gut microbiota samples from PD patients in Türkiye were analyzed using shotgun metagenomics, a novel genus-specific amplicon sequencing method was developed specifically for the analysis of Akkermansia genus, and then Akkermansia strains and genes potentially associated with CI stages in PD were identified using this method. The results underscore that investigating the species or strain level differences could help better understanding of the changes associated with PD in the human gut microbiota

Metaproteogenomic analysis of saliva samples from Parkinson's disease patients with cognitive impairment

Cognitive impairment (CI) is very common in patients with Parkinson's Disease (PD) and progressively develops on a spectrum from mild cognitive impairment (PD-MCI) to full dementia (PDD). Identification of PD patients at risk of developing cognitive decline, therefore, is unmet need in the clinic to manage the disease. Previous studies reported that oral microbiota of PD patients was altered even at early stages and poor oral hygiene is associated with dementia. However, data from single modalities are often unable to explain complex chronic diseases in the brain and cannot reliably predict the risk of disease progression. Here, we performed integrative metaproteogenomic characterization of salivary microbiota and tested the hypothesis that biological molecules of saliva and saliva microbiota dynamically shift in association with the progression of cognitive decline and harbor discriminatory key signatures across the spectrum of CI in PD. We recruited a cohort of 115 participants in a multi-center study and employed multi-omics factor analysis (MOFA) to integrate amplicon sequencing and metaproteomic analysis to identify signature taxa and proteins in saliva. Our baseline analyses revealed contrasting interplay between the genus Neisseria and Lactobacillus and Ligilactobacillus genera across the spectrum of CI. The group specific signature profiles enabled us to identify bacterial genera and protein groups associated with CI stages in PD. Our study describes compositional dynamics of saliva across the spectrum of CI in PD and paves the way for developing non-invasive biomarker strategies to predict the risk of CI progression in PD.FEMS Research and Training Gran

Axillary microbiota is associated with cognitive impairment in parkinson's disease patients

Cognitive impairment (CI) is among the most common non-motor symptoms of Parkinson's disease (PD), with a substantially negative impact on patient management and outcome. The development and progression of CI exhibits high interindividual variability, which requires better diagnostic and monitoring strategies. PD patients often display sweating disorders resulting from autonomic dysfunction, which has been associated with CI. Because the axillary microbiota is known to change with humidity level and sweat composition, we hypothesized that the axillary microbiota of PD patients shifts in association with CI progression, and thus can be used as a proxy for classification of CI stages in PD. We compared the axillary microbiota compositions of 103 PD patients (55 PD patients with dementia [PDD] and 48 PD patients with mild cognitive impairment [PD-MCI]) and 26 cognitively normal healthy controls (HC). We found that axillary microbiota profiles differentiate HC, PD-MCI, and PDD groups based on differential ranking analysis, and detected an increasing trend in the log ratio of Corynebacterium to Anaerococcus in progression from HC to PDD. In addition, phylogenetic factorization revealed that the depletion of the Anaerococcus, Peptoniphilus, and W5053 genera is associated with PD-MCI and PDD. Moreover, functional predictions suggested significant increases in myo-inositol degradation, ergothioneine biosynthesis, propionate biosynthesis, menaquinone biosynthesis, and the proportion of aerobic bacteria and biofilm formation capacity, in parallel to increasing CI. Our results suggest that alterations in axillary microbiota are associated with CI in PD. Thus, axillary microbiota has the potential to be exploited as a noninvasive tool in the development of novel strategies.Suleyman Yildirim from the Scientific and Technological Research Council of Turkey (TUBITAK

Wheat flour milling yield estimation based on wheat kernel physical properties using artificial neural networks

Wheat is a basicfood raw material for the majority of people around the world as wheat-based products provide an important part of the daily energy intake in many countries. Wheat is generally milled into flour prior to use in the bakery industry. Flour yield is one of the major quality criteria in wheat milling. Flour yield determination requires large amounts of samples, costly machines, grinding applications that require a long working time and a considerable amount of workload.In this study, Artificial Neural Network(ANN) approach has been employed to predict flour milling yield. The ANN was designed in the Matlabusing such wheat physical properties as hectoliter weight, thousand-kernel weight, kernel size distribution, and grain hardness. Flour yields and four different kernel physical features (hectoliter weight, thousand-kernel weight, kernel size distribution, and grain hardness) were first collected from 2400 wheat samples through the conventional methods. The ANN was trained using 85% of 2400 yield data and tested with the remaining 15% data. In the training of the ANN, various models have been investigated to find the best ANN structure. Additionally, two datasets with and without grain hardness have been employed to determine the effect of grain hardness on the prediction performance of the ANN model. It was found that grain hardnesswhichreduced the MAE values from 2.3333 to 2.2611 and RMSE values from 3.0775 to 2.9146gave better result. The results proved that the developed ANN model can be used to estimateflour yield using wheat physical properties

Metaproteogenomic analysis of saliva samples from Parkinson's disease patients with cognitive impairment

Cognitive impairment (CI) is very common in patients with Parkinson's Disease (PD) and progressively develops on a spectrum from mild cognitive impairment (PD-MCI) to full dementia (PDD). Identification of PD patients at risk of developing cognitive decline, therefore, is unmet need in the clinic to manage the disease. Previous studies reported that oral microbiota of PD patients was altered even at early stages and poor oral hygiene is associated with dementia. However, data from single modalities are often unable to explain complex chronic diseases in the brain and cannot reliably predict the risk of disease progression. Here, we performed integrative metaproteogenomic characterization of salivary microbiota and tested the hypothesis that biological molecules of saliva and saliva microbiota dynamically shift in association with the progression of cognitive decline and harbor discriminatory key signatures across the spectrum of CI in PD. We recruited a cohort of 115 participants in a multi-center study and employed multi-omics factor analysis (MOFA) to integrate amplicon sequencing and metaproteomic analysis to identify signature taxa and proteins in saliva. Our baseline analyses revealed contrasting interplay between the genus Neisseria and Lactobacillus and Ligilactobacillus genera across the spectrum of CI. The group specific signature profiles enabled us to identify bacterial genera and protein groups associated with CI stages in PD. Our study describes compositional dynamics of saliva across the spectrum of CI in PD and paves the way for developing non-invasive biomarker strategies to predict the risk of CI progression in PD

QTL mapping for grain zinc and iron concentrations and zinc efficiency in a tetraploid and hexaploid wheat mapping populations

Background and aims Zinc (Zn) and iron (Fe) deficiencies are the most important forms of malnutrition globally, and caused mainly by low dietary intake. Wheat, a major staple food crop, is inherently low in these micronutrients. Identifying new QTLs for high grain Zn (GZn) and Fe (GFe) will contribute to improved micronutrient density in wheat grain. Methods Using two recently developed RIL mapping populations derived from a wild progenitor of a tetraploid population “Saricanak98 × MM5/4” and an hexaploid population “Adana99 × 70,711”, multi-locational field experiments were conducted over 2 years to identify genomic regions associated with high grain Zn (GZn) and grain Fe (GFe) concentrations. Additionally, a greenhouse experiment was conducted by growing the “Saricanak98 × MM5/4” population in a Zn-deficient calcareous soil to determine the markers involved in Zn efficiency (ZnEff) of the genotypes (expressed as the ratio of shoot dry weight under Zn deficiency to Zn fertilization) and its relation to GZn. The populations were genotyped by using DArT markers. Results Quantitative trait loci (QTL) for high GFe and GZn concentrations in wheat grains were mapped in the both RIL mapping populations. Two major QTLs for increasing GZn were stably detected on chromosomes 1B and 6B of the tetra- and hexaploid mapping populations, and a GZn QTL on chromosome 2B co-located with grain GFe, suggesting simultaneous improvement of GFe and GZn is possible. In the greenhouse experiment, the RILs exhibited substantial genotypic variation for Zn efficiency ratio, ranging from 31 % to 90 %. Two QTL for Zn efficiency were identified on chromosomes 6A and 6B. There was no association between Zn efficiency and grain Zn concentration among the genotypes. The results clearly show that Zn efficiency and Zn accumulation in grain are governed by different genetic mechanisms. Conclusion Identification of some consistent genomic regions such as 1B and 6B across two different mapping populations suggest these genomic regions might be the useful regions for further marker development and use in biofortification breeding programs