Principles of Systems Biology, No. 13.

Cell Syst. 2017 Jan 25;4(1):3-6

Authors:

Abstract
This month: CRISPR flexes its massively parallel muscles (see also this month's editorial), two systems-level properties discovered in yeast, and a host of new tools and synthetic engineered systems.

PMID: 28125792 [PubMed - in process]

Selecting the most appropriate time points to profile in high-throughput studies.

Elife. 2017 Jan 26;6:

Authors: Kleyman M, Sefer E, Nicola T, Espinoza C, Chhabra D, Hagood JS, Kaminski N, Ambalavanan N, Bar-Joseph Z

Abstract
Biological systems are increasingly being studied by high throughput profiling of molecular data over time. Determining the set of time points to sample in studies that profile several different types of molecular data is still challenging. Here we present the Time Point Selection (TPS) method that solves this combinatorial problem in a principled and practical way. TPS utilizes expression data from a small set of genes sampled at a high rate. As we show by applying TPS to study mouse lung development, the points selected by TPS can be used to reconstruct an accurate representation for the expression values of the non selected points. Further, even though the selection is only based on gene expression, these points are also appropriate for representing a much larger set of protein, miRNA and DNA methylation changes over time. TPS can thus serve as a key design strategy for high throughput time series experiments.

PMID: 28124972 [PubMed - as supplied by publisher]

Systems biology analysis of drivers underlying hallmarks of cancer cell metabolism.

Sci Rep. 2017 Jan 25;7:41241

Authors: Zielinski DC, Jamshidi N, Corbett AJ, Bordbar A, Thomas A, Palsson BO

Abstract
Malignant transformation is often accompanied by significant metabolic changes. To identify drivers underlying these changes, we calculated metabolic flux states for the NCI60 cell line collection and correlated the variance between metabolic states of these lines with their other properties. The analysis revealed a remarkably consistent structure underlying high flux metabolism. The three primary uptake pathways, glucose, glutamine and serine, are each characterized by three features: (1) metabolite uptake sufficient for the stoichiometric requirement to sustain observed growth, (2) overflow metabolism, which scales with excess nutrient uptake over the basal growth requirement, and (3) redox production, which also scales with nutrient uptake but greatly exceeds the requirement for growth. We discovered that resistance to chemotherapeutic drugs in these lines broadly correlates with the amount of glucose uptake. These results support an interpretation of the Warburg effect and glutamine addiction as features of a growth state that provides resistance to metabolic stress through excess redox and energy production. Furthermore, overflow metabolism observed may indicate that mitochondrial catabolic capacity is a key constraint setting an upper limit on the rate of cofactor production possible. These results provide a greater context within which the metabolic alterations in cancer can be understood.

PMID: 28120890 [PubMed - in process]

Copy Number Variations in Amyotrophic Lateral Sclerosis: Piecing the Mosaic Tiles Together through a Systems Biology Approach.

Mol Neurobiol. 2017 Jan 24;:

Authors: Morello G, Guarnaccia M, Spampinato AG, La Cognata V, D'Agata V, Cavallaro S

Abstract
Amyotrophic lateral sclerosis (ALS) is a devastating and still untreatable motor neuron disease. Despite the molecular mechanisms underlying ALS pathogenesis that are still far from being understood, several studies have suggested the importance of a genetic contribution in both familial and sporadic forms of the disease. In addition to single-nucleotide polymorphisms (SNPs), which account for only a limited number of ALS cases, a consistent number of common and rare copy number variations (CNVs) have been associated to ALS. Most of the CNV-based association studies use a traditional candidate-gene approach that is inadequate for uncovering the genetic architectures of complex traits like ALS. The emergent paradigm of "systems biology" may offer a new perspective to better interpret the wide spectrum of CNVs in ALS, enabling the characterization of the complex network of gene products underlying ALS pathogenesis. In this review, we will explore the landscape of CNVs in ALS, putting specific emphasis on the functional impact of common CNV regions and genes consistently associated with increased risk of developing disease. In addition, we will discuss the potential contribution of multiple rare CNVs in ALS pathogenesis, focusing our attention on the complex mechanisms by which these proteins might impact, individually or in combination, the genetic susceptibility of ALS. The comprehensive detection and functional characterization of common and rare candidate risk CNVs in ALS susceptibility may bring new pieces into the intricate mosaic of ALS pathogenesis, providing interesting and important implications for a more precise molecular biomarker-assisted diagnosis and more effective and personalized treatments.

PMID: 28120152 [PubMed - as supplied by publisher]

Systems Biology Approach in Hypertension Research.

Methods Mol Biol. 2017;1527:69-79

Authors: Delles C, Husi H

Abstract
Systems biology is an approach to study all genes, gene transcripts, proteins, metabolites, and their interactions in specific cells, tissues, organs, or the whole organism. It is based on data derived from high-throughput analytical technologies and bioinformatics tools to analyze these data, and aims to understand the whole system rather than individual aspects of it. Systems biology can be applied to virtually all conditions and diseases and therefore also to hypertension and its underlying vascular disorders. Unlike other methods in this book there is no clear-cut protocol to explain a systems biology approach. We will instead outline some of the most important and common steps in the generation and analysis of systems biology data.

PMID: 28116708 [PubMed - in process]

Development of oriC-based plasmids for Mesoplasma florum.

Appl Environ Microbiol. 2017 Jan 23;:

Authors: Matteau D, Pepin ME, Baby V, Gauthier S, Arango Giraldo M, Knight TF, Rodrigue S

Abstract
The near-minimal bacterium Mesoplasma florum constitutes an attractive model for systems biology and for the development of a simplified cell chassis in synthetic biology. However, the lack of genetic engineering tools for this microorganism has limited our capacity to understand its basic biology and modify its genome. To address this issue, we have evaluated the susceptibility of M. florum to common antibiotics, and developed the first generation of artificial plasmids able to replicate in this bacterium. Selected regions of the predicted M. florum chromosomal origin of replication (oriC) were used to create different plasmid versions that were tested for their transformation frequency and stability. Using polyethylene glycol mediated transformation, we observed that plasmids harbouring both rpmH/dnaA and dnaA/dnaN intergenic regions, interspaced or not with a copy of the dnaA gene, resulted in a frequency of ∼4.1 x 10(-6) transformant per viable cell and were stably maintained throughout multiple generations. In contrast, plasmids containing only one M. florum oriC intergenic region or the heterologous oriC region of Mycoplasma capricolum, Mycoplasma mycoides or Spiroplasma citri failed to produce any detectable transformants. We also developed alternative transformation procedures based on electroporation or conjugation from Escherichia coli, reaching frequencies up to 7.87 x 10(-6) and 8.44 x 10(-7) transformant per viable cell, respectively. Finally, we demonstrated the functionality of antibiotic resistance genes active against tetracycline, puromycin, as well as spectinomycin/streptomycin in M. florum Taken together, these valuable genetic tools will facilitate efforts towards building a M. florum based near-minimal cellular chassis for synthetic biology.
IMPORTANCE: Mesoplasma florum constitutes an attractive model for systems biology, and for the development of a simplified cell chassis in synthetic biology. M. florum is closely related to the mycoides cluster of mycoplasmas that has become a model for whole-genome cloning, genome transplantation, and genome minimization. However, M. florum shows faster growth rates compared to other Mollicutes, has no known pathogenic potential, and possesses a significantly smaller genome that positions this species among some of the simplest free-living organisms. So far, the lack of genetic engineering tools has limited our capacity to understand the basic biology of M. florum in order to modify its genome. To address this issue, we have evaluated the susceptibility of M. florum to common antibiotics, and developed the first artificial plasmids as well as transformation methods for this bacterium. This represents a strong basis for on-going genome engineering efforts using this near-minimal microorganism.

PMID: 28115382 [PubMed - as supplied by publisher]

Integrating Omic Data with a Multiplex Network-based Approach for the Identification of Cancer Subtypes.

IEEE Trans Nanobioscience. 2016 Apr 20;:

Authors: Wang H, Zheng H, Wang J, Wang C, Wu F

Abstract
Comprehensive characterization and identification of cancer subtypes have a number of applications and implications in life science and cancer research. Technologies centered on the integration of omics data hold great promise in this endeavor. This paper proposed a multiplex network-based approach for integrative analysis of heterogeneous omics data. It represents a useful alternative network-based solution to the problem and a significant step forward to the methods in which each type of data is treated independently. It has been tested on the identification of the subtypes of glioblastoma multiforme and breast invasive carcinoma from three omics data. The results obtained have shown that it has achieved the performance comparable to state-of-the-art techniques (Normalized Mutual Information > 0.8). In comparison to traditional systems biology tools, the proposed methodology has several significant advantages. It has the ability to correlate and integrate multiple data levels in a holistic manner which may be useful to facilitate our understanding of the pathogenesis of diseases and to capture the heterogeneity of biological processes and the complexity of phenotypes.

PMID: 28113909 [PubMed - as supplied by publisher]

A Systems Approach to Physiologic Evolution: From Micelles to Consciousness.

J Cell Physiol. 2017 Jan 23;:

Authors: Torday JS, Miller WB

Abstract
A systems approach to evolutionary biology offers the promise of an improved understanding of the fundamental principles of life through the effective integration of many biologic disciplines. It is presented that any critical integrative approach to evolutionary development involves a paradigmatic shift in perspective, more than just the engagement of a large number of disciplines. Critical to this differing viewpoint is the recognition that all biological processes originate from the unicellular state and remain permanently anchored to that phase throughout evolutionary development despite their macroscopic appearances. Multicellular eukaryotic development can therefore be viewed as a series of connected responses to epiphenomena that proceeds from that base in continuous iterative maintenance of collective cellular homeostatic equipoise juxtaposed against an ever-changing and challenging environment. By following this trajectory of multicellular eukaryotic evolution from within unicellular First Principles of Physiology forward, the mechanistic nature of complex physiology can be identified through a step-wise analysis of a continuous arc of vertebrate evolution based upon serial exaptations. This article is protected by copyright. All rights reserved.

PMID: 28112403 [PubMed - as supplied by publisher]

Genetics of schizophrenia: A consensus paper of the WFSBP Task Force on Genetics.

World J Biol Psychiatry. 2017 Jan 23;:1-14

Authors: Giegling I, Hosak L, Mössner R, Serretti A, Bellivier F, Claes S, Collier DA, Corrales A, DeLisi LE, Gallo C, Gill M, Kennedy JL, Leboyer M, Maier W, Marquez M, Massat I, Mors O, Muglia P, Nöthen MM, Ospina-Duque J, Owen MJ, Propping P, Shi Y, St Clair D, Thibaut F, Cichon S, Mendlewicz J, O'Donovan MC, Rujescu D

Abstract
OBJECTIVES: Schizophrenia is a severe psychiatric disease affecting about 1% of the general population. The relative contribution of genetic factors has been estimated to be up to 80%. The mode of inheritance is complex, non-Mendelian, and in most cases involving the combined action of large numbers of genes.
METHODS: This review summarises recent efforts to identify genetic variants associated with schizophrenia detected, e.g., through genome-wide association studies, studies on copy-number variants or next-generation sequencing.
RESULTS: A large, new body of evidence on genetics of schizophrenia has accumulated over recent years. Many new robustly associated genetic loci have been detected. Furthermore, there is consensus that at least a dozen microdeletions and microduplications contribute to the disease. Genetic overlap between schizophrenia, other psychiatric disorders, and neurodevelopmental syndromes raised new questions regarding the current classification of psychiatric and neurodevelopmental diseases.
CONCLUSIONS: Future studies will address especially the functional characterisation of genetic variants. This will hopefully open the doors to our understanding of the pathophysiology of schizophrenia and other related diseases. Complementary, integrated systems biology approaches to genomics, transcriptomics, proteomics and metabolomics may also play crucial roles in enabling a precision medicine approach to the treatment of individual patients.

PMID: 28112043 [PubMed - as supplied by publisher]

Epigenetics in Epilepsy.

Neurosci Lett. 2017 Jan 19;:

Authors: Kobow K, Blümcke I

Abstract
Approximately 50 million people have epilepsy, making it the most common chronic and severe neurological disease worldwide, with increased risk of mortality and psychological and socioeconomic consequences impairing quality of life. More than 30% of patients with epilepsy have inadequate control of their seizures with drug therapy. Any structural brain lesion can provoke epilepsy. However, progression of seizure activity as well as the development of drug-resistance remains difficult to predict, irrespective of the underlying epileptogenic condition, i.e., traumatic brain injury, developmental brain lesions, brain tumors or genetic inheritance. Mutated DNA sequences in genes encoding for ion channels or neurotransmitter receptors have been identified in hereditary focal or generalized epilepsies, but genotype-phenotype correlations are poor, arguing for additional factors determining the effect of a genetic predisposition. The dynamics of epigenetic mechanisms (e.g. DNA methylation, histone modifications, chromatin remodelling, and non-coding RNAs) provide likely explanations for common features in epilepsy and other complex diseases, including late onset, parent-of-origin effects, discordance of monozygotic twins, and fluctuation of symptoms. In addition, many focal epilepsies, including focal cortical dysplasias (FCDs), glio-neuronal tumors (e.g. ganglioglioma), or temporal lobe epilepsy with hippocampal sclerosis (TLE-HS), do not seem to primarily associate with hereditary traits, suggesting other pathogenic mechanisms. Herein we will discuss the many faces of the epigenetic machinery, which provides powerful tools and mechanisms to propagate epileptogenicity and likely also contribute to the epileptogenic memory in chronic and difficult-to-treat epilepsies.

PMID: 28111355 [PubMed - as supplied by publisher]

The Application of Principal Component Analysis to Drug Discovery and Biomedical Data.

Drug Discov Today. 2017 Jan 19;:

Authors: Giuliani A

Abstract
There is a neat distinction between general purpose statistical techniques and quantitative models developed for specific problems .Principal Component Analysis (PCA) blurs this distinction: while being a general purpose statistical technique, it implies a peculiar style of reasoning. PCA is a 'hypothesis generating' tool creating a statistical mechanics frame for biological systems modeling without the need for strong a priori theoretical assumptions. This makes PCA of utmost importance for approaching drug discovery by a systemic perspective overcoming too narrow reductionist approaches.

PMID: 28111329 [PubMed - as supplied by publisher]

Resource Reallocation in Bacteria by Reengineering the Gene Expression Machinery.

Trends Microbiol. 2017 Jan 16;:

Authors: de Jong H, Geiselmann J, Ropers D

Abstract
Bacteria have evolved complex regulatory networks to control the activity of transcription and translation, and thus the growth rate, over a range of environmental conditions. Reengineering RNA polymerase and ribosomes allows modifying naturally evolved regulatory networks and thereby profoundly reorganizing the manner in which bacteria allocate resources to different cellular functions. This opens new opportunities for our fundamental understanding of microbial physiology and for a variety of applications. Recent breakthroughs in genome engineering and the miniaturization and automation of culturing methods have offered new perspectives for the reengineering of the transcription and translation machinery in bacteria as well as the development of novel in vitro and in vivo gene expression systems. We review different examples from the unifying perspective of resource reallocation, and discuss the impact of these approaches for microbial systems biology and biotechnological applications.

PMID: 28110800 [PubMed - as supplied by publisher]

Targeted In-Depth Quantification of Signaling Using Label-Free Mass Spectrometry.

Methods Enzymol. 2017;585:245-268

Authors: Cutillas PR

Abstract
Protein phosphorylation encodes information on the activity of kinase-driven signaling pathways that regulate cell biology. This chapter discusses an approach, named TIQUAS (targeted in-depth quantification of signaling), to quantify cell signaling comprehensively and without bias. The workflow-based on mass spectrometry (MS) and computational science-consists of targeting the analysis of phosphopeptides previously identified by shotgun liquid chromatography tandem MS (LC-MS/MS) across the samples that are being compared. TIQUAS therefore takes advantage of concepts derived from both targeted (data-independent) and data-dependent acquisition methods; phosphorylation sites are quantified in all experimental samples regardless of whether or not these phosphopeptides were identified by MS/MS in all runs. As a result, datasets are obtained containing quantitative information on several thousand phosphorylation sites in as many samples and replicates as required in the experimental design, and these rich datasets are devoid of a significant number of missing data points. This chapter discussed the biochemical, analytical, and computational procedures required to apply the approach and for obtaining a biological interpretation of the data in the context of our understanding of cell signaling regulation and kinase-substrate relationships.

PMID: 28109432 [PubMed - in process]

Reannotation of Genomes by Means of Proteomics Data.

Methods Enzymol. 2017;585:201-216

Authors: Armengaud J

Abstract
Omics approaches have become popular in biology as powerful discovery tools, and currently gain in interest for diagnostic applications. Establishing the accurate genome sequence of any organism is easy, but the outcome of its annotation by means of automatic pipelines remains imprecise. Some protein-encoding genes may be missed as soon as they are specific and poorly conserved in a given taxon, while important to explain the specific traits of the organism. Translational starts are also poorly predicted in a relatively important number of cases, thus impacting the protein sequence database used in proteomics, comparative genomics, and systems biology. The use of high-throughput proteomics data to improve genome annotation is an attractive option to obtain a more comprehensive molecular picture of a given organism. Here, protocols for reannotating prokaryote genomes are described based on shotgun proteomics and derivatization of protein N-termini with a positively charged reagent coupled to high-resolution tandem mass spectrometry.

PMID: 28109430 [PubMed - in process]

Parameter estimation in large-scale systems biology models: a parallel and self-adaptive cooperative strategy.

BMC Bioinformatics. 2017 Jan 21;18(1):52

Authors: Penas DR, González P, Egea JA, Doallo R, Banga JR

Abstract
BACKGROUND: The development of large-scale kinetic models is one of the current key issues in computational systems biology and bioinformatics. Here we consider the problem of parameter estimation in nonlinear dynamic models. Global optimization methods can be used to solve this type of problems but the associated computational cost is very large. Moreover, many of these methods need the tuning of a number of adjustable search parameters, requiring a number of initial exploratory runs and therefore further increasing the computation times. Here we present a novel parallel method, self-adaptive cooperative enhanced scatter search (saCeSS), to accelerate the solution of this class of problems. The method is based on the scatter search optimization metaheuristic and incorporates several key new mechanisms: (i) asynchronous cooperation between parallel processes, (ii) coarse and fine-grained parallelism, and (iii) self-tuning strategies.
RESULTS: The performance and robustness of saCeSS is illustrated by solving a set of challenging parameter estimation problems, including medium and large-scale kinetic models of the bacterium E. coli, bakerés yeast S. cerevisiae, the vinegar fly D. melanogaster, Chinese Hamster Ovary cells, and a generic signal transduction network. The results consistently show that saCeSS is a robust and efficient method, allowing very significant reduction of computation times with respect to several previous state of the art methods (from days to minutes, in several cases) even when only a small number of processors is used.
CONCLUSIONS: The new parallel cooperative method presented here allows the solution of medium and large scale parameter estimation problems in reasonable computation times and with small hardware requirements. Further, the method includes self-tuning mechanisms which facilitate its use by non-experts. We believe that this new method can play a key role in the development of large-scale and even whole-cell dynamic models.

PMID: 28109249 [PubMed - in process]

Integrated omics analysis of specialized metabolism in medicinal plants.

Plant J. 2017 Jan 21;:

Authors: Rai A, Saito K, Yamazaki M

Abstract
Medicinal plants are the rich source of highly diverse specialized metabolites with important pharmacological properties. Until recently, plant biologists were limited in their approach to explore biosynthetic pathways of these metabolites, mainly due to the scarcity of plant genomics resources. Recent advancements in the high-throughput large-scale analytical methods have enabled plant biologists to discover biosynthetic pathways for important plant-based medicinal metabolites. Reduced cost to generate omics datasets, and the development of computational tools for their analysis and integration have led to the elucidation of biosynthetic pathways of several plant origin bio-active metabolites. These discoveries have inspired synthetic biology approaches to develop microbial systems producing plant origin bioactive metabolites, an alternative for sustainable source of medicinally important chemicals. Since the world population is increasing at a rapid pace and so is the demand for medicinal compounds, understanding the complete biosynthesis of specialized metabolites becomes important. Here, we have reviewed the contributions of major omics approaches and their integration towards our understanding of biosynthetic pathways of bioactive metabolites. We have briefly discussed different approaches to integrate omics datasets to extract biological relevant knowledge, and application of omics datasets in the construction and reconstruction of metabolic models. This article is protected by copyright. All rights reserved.

PMID: 28109168 [PubMed - as supplied by publisher]

A Circulating microRNA Signature Predicts Age-Based Development of Lymphoma.

PLoS One. 2017;12(1):e0170521

Authors: Beheshti A, Vanderburg C, McDonald JT, Ramkumar C, Kadungure T, Zhang H, Gartenhaus RB, Evens AM

Abstract
Extensive epidemiological data have demonstrated an exponential rise in the incidence of non-Hodgkin lymphoma (NHL) that is associated with increasing age. The molecular etiology of this remains largely unknown, which impacts the effectiveness of treatment for patients. We proposed that age-dependent circulating microRNA (miRNA) signatures in the host influence diffuse large B cell lymphoma (DLBCL) development. Our objective was to examine tumor development in an age-based DLBCL system using an inventive systems biology approach. We harnessed a novel murine model of spontaneous DLBCL initiation (Smurf2-deficient) at two age groups: 3 and 15 months old. All Smurf2-deficient mice develop visible DLBCL tumor starting at 15 months of age. Total miRNA was isolated from serum, bone marrow and spleen and were collected for all age groups for Smurf2-deficient mice and age-matched wild-type C57BL/6 mice. Using systems biology techniques, we identified a list of 10 circulating miRNAs being regulated in both the spleen and bone marrow that were present in DLBCL forming mice starting at 3 months of age that were not present in the control mice. Furthermore, this miRNA signature was found to occur circulating in the blood and it strongly impacted JUN and MYC oncogenic signaling. In addition, quantification of the miRNA signature was performed via Droplet Digital PCR technology. It was discovered that a key miRNA signature circulates throughout a host prior to the formation of a tumor starting at 3 months old, which becomes further modulated by age and yielded calculation of a 'carcinogenic risk score'. This novel age-based circulating miRNA signature may potentially be leveraged as a DLBCL risk profile at a young age to predict future lymphoma development or disease progression as well as for potential innovative miRNA-based targeted therapeutic strategies in lymphoma.

PMID: 28107482 [PubMed - in process]

Parenclitic Network Analysis of Methylation Data for Cancer Identification.

PLoS One. 2017;12(1):e0169661

Authors: Karsakov A, Bartlett T, Ryblov A, Meyerov I, Ivanchenko M, Zaikin A

Abstract
We make use of ideas from the theory of complex networks to implement a machine learning classification of human DNA methylation data, that carry signatures of cancer development. The data were obtained from patients with various kinds of cancers and represented as parenclictic networks, wherein nodes correspond to genes, and edges are weighted according to pairwise variation from control group subjects. We demonstrate that for the 10 types of cancer under study, it is possible to obtain a high performance of binary classification between cancer-positive and negative samples based on network measures. Remarkably, an accuracy as high as 93-99% is achieved with only 12 network topology indices, in a dramatic reduction of complexity from the original 15295 gene methylation levels. Moreover, it was found that the parenclictic networks are scale-free in cancer-negative subjects, and deviate from the power-law node degree distribution in cancer. The node centrality ranking and arising modular structure could provide insights into the systems biology of cancer.

PMID: 28107365 [PubMed - in process]

Selected proceedings of Machine Learning in Systems Biology: MLSB 2016.

BMC Bioinformatics. 2016 Dec 13;17(Suppl 16):437

Authors: van Dijk AD, Lähdesmäki H, de Ridder D, Rousu J

PMID: 28105910 [PubMed - in process]

Exploring the Missing Links between Dietary Habits and Diseases.

IEEE Trans Nanobioscience. 2017 Jan 16;:

Authors: Bhattacharyya M, Maity S, Bandyopadhyay S

Abstract
Disease dietomics is an emerging area of systems biology that attempts to explore the connections between the dietary habits and diseases. Some of the topical studies highlight that foods might have different impacts over an organism either in progressing a disease (negative association) or in fighting against it (positive association). The association of foods with different diseases can be put together to build a network that might provide a global view of the entire system. Again, such disease-food networks might emerge in a more complex form while considering the disease subtypes individually. Some foods might have positive association with a particular subtype of a disease, whereas it might have no association or negative association with another subtype of the same disease. Therefore, the subtypes might have completely different network patterns. On the other hand, the same food may be helpful for a disease and harmful for another disease or even for a subtype. Analyzing such disease-food networks in different forms might give us important information about the relations between different diseases. In this study, we have analyzed a large-scale disease-food network comprising 162 different diseases and 455 types of foods for gaining knowledge about the connection between these diseases and their subtypes. We have measured the similarity between diseases based on their patterns of association with foods. In addition to observing a high similarity between several disease sub types, particularly for cancer, we have found strong relations between constipation-dysphagia and cancer-cardiovascular disease, which are rarely known. Tendency of occurrence of different diseases can be predicted based on such information.

PMID: 28103559 [PubMed - as supplied by publisher]