History of the methodology of disease gene identification

A novel deep learning identifier for promoters and their strength using heterogeneous features

Promoters, which are short (50-1500 base-pair) in DNA regions, have emerged to play a critical role in the regulation of gene transcription. Numerous dangerous diseases, likewise cancer, cardiovascular, and inflammatory bowel diseases, are caused by genetic variations in promoters. Consequently, the correct identification and characterization of promoters are significant for the discovery of drugs. However, experimental approaches to recognizing promoters and their strengths are challenging in terms of cost, time, and resources. Therefore, computational techniques are highly desirable for the correct characterization of promoters from unannotated genomic data. Here, we designed a powerful bi-layer deep-learning based predictor named "PROCABLES", which discriminates DNA samples as promoters in the first-phase and strong or weak promoters in the second-phase respectively. The proposed method utilizes five distinct features, such as word2vec, k-spaced nucleotide pairs, trinucleotide propensity-based features, trinucleotide composition, and electron-ion interaction pseudopotentials, to extract the hidden patterns from the DNA sequence. Afterwards, a stacked framework is formed by integrating a convolutional neural network (CNN) with bidirectional long-short-term memory (LSTM) using multi-view attributes to train the proposed model. The PROCABLES model achieved an accuracy of 0.971 and 0.920 and the MCC 0.940 and 0.840 for the first and second-layer using the ten-fold cross-validation test, respectively. The predicted results anticipate that the proposed PROCABLES protocol outperformed the advanced computational predictors targeting promoters and their types. In summary, this research will provide useful hints for the recognition of large-scale promoters in particular and other DNA problems in general.

Can social adversity alter the epigenome, trigger oral disease, and affect future generations?

The nature versus nurture debate has intrigued scientific circles for decades. Although extensive research has established a clear relationship between genetics and disease development, recent evidence has highlighted the insufficiency of attributing adverse health outcomes to genetic factors alone. In fact, it has been suggested that environmental influences, such as socioeconomic position (SEP), may play a much larger role in the development of disease than previously thought, with extensive research suggesting that low SEP is associated with adverse health conditions. In relation to oral health, a higher prevalence of caries (tooth decay) exists among those of low SEP. Although little is known about the biological mechanisms underlying this relationship, epigenetic modifications resulting from environmental influences have been suggested to play an important role. This review explores the intersection of health inequalities and epigenetics, the role of early-life social adversity and its long-term epigenetic impacts, and how those living within the lower hierarchies of the socioeconomic pyramid are indeed at higher risk of developing diseases, particularly in relation to oral health. A deeper understanding of these mechanisms could lead to the development of targeted interventions for individuals of low SEP to improve oral health or identify those who are at higher risk of developing oral disease.

The genetic cause of neurodevelopmental disorders in 30 consanguineous families

Objective

This study aims to clinically and genetically assess 30 unrelated consanguineous Pakistani families from various ethnic backgrounds, all exhibiting features of neurodevelopmental disorders (NDDs).

Methods

We conducted clinical, genetic, biochemical, and molecular analyses on 30 consanguineous families with NDDs enrolled from various regions of Pakistan. The likely molecular causes of primary microcephaly and NDDs were identified. Detailed clinical investigations and molecular diagnoses were performed using whole exome sequencing (WES) of the proband, followed by Sanger sequencing for validation and segregation in the available family members of the affected families.

Results

WES identified likely disease-causing homozygous variants in 30 unrelated consanguineous families. Six families presented newly described variants in known NDD-related genes: ABAT (c.1439 T > G; p.Phe480Cys) [OMIM613163], SLC12A6 (c.2865_2865insT; p.Glu955Asnfs*5) [OMIM 218000], SHANK3 (c.1305-3_1,305-2delTT; p.Gln29-_Gly305del) [OMIM 606232], BCKDK (c.356_356insC; p.Gly119Alafs*24) [OMIM 614923], DDHD2 (c.2065G > T; p.Asp689Tyr) [OMIM 615033], ERCC2 (c.1255G > A; p.Glu419Lys) [OMIM 610756]. Additionally, 12 families had previously reported disease-causing variants associated with different types of NDDs: ATRX (c.109C > T; p.Arg37*) [OMIM 309580], GPR56 [ADGRG1] (c.1423C > T; p.Arg475*) [OMIM 606854], NAGLU (c.1694G > A; p.Arg565Gln) [OMIM 252920], DOLK (c.3G > A; p.Met1Ile) [OMIM 610768], GPT2 (c.815C > T; p.Ser272Leu) [OMIM 616281], DYNC1I2 (c.607 + 1G > A; p.?) [OMIM 618492], FBXL3 (c.885delT; p.Leu295Phefs25*) [OMIM 606220], LINGO1 (c.869G > A; p.Arg290His) [OMIM 618103], and ASPM (c.3978G > A; Trp1326*, c.9557C > G; p.Ser3186*, c.6994C > T; p.Arg2332*) [OMIM 608716]. All the identified variants showed segregation compatible with autosomal recessive inheritance.

Conclusion

In the present study, we observed a high frequency of ASPM variants in the genetic analysis of 30 consanguineous families exhibiting features of NDDs, particularly those associated with autosomal recessive primary microcephaly. These findings contribute to studies on genotype-phenotype correlation, genetic counseling for families, and a deeper understanding of human brain function and development.

Decoding the gene-disease associations in type 2 diabetes: A curated dataset for text mining-based classification

Type 2 Diabetes (T2D) exerts a substantial impact on mortality rates. According to 2023 statistics, more than half a billion individuals are experiencing the effects of T2D, making it one of the top 10 leading contributors to worldwide deaths. Multiple factors contribute to the onset of T2D, such as obesity, poor diet and lifestyle, the mutation in specific genes and many more. Among the various factors that contribute to the development of T2D, genetics is a pivotal aspect. Due to the significant influence of genes in the initiation and advancement of various phases of T2D, our focus lies on exploring the association between T2D and genes. In the present article, we have curated Standard disease gene association data which contains evidence or reference sentences which contain this disease gene association information, which is further classified into 4 classes: Yes, No, Ambiguous and X each pertaining to Positive, Negative, Ambiguous and Not related disease-gene associations respectively. For the purpose of this work, we downloaded T2D related abstracts from PubMed using EDirect and further pre-processed this abstract data to extract Reference Sentences Data. This data was later double-fold manually validated to compile this disease gene association data. The data produced in this article serves as reference data for the training text mining-based biological literature classifiers. Classifiers will further be used to predict classes of published literature, not just for T2D, but can also be expanded beyond to encompass a wide range of disease and their complications. The compilation of positively linked genes derived from these predictions can then be utilized for in-depth system-level analysis of T2D.

The discovery of archaea: from observed anomaly to consequential restructuring of the phylogenetic tree

Observational and experimental discoveries of new factual entities such as objects, systems, or processes, are major contributors to some advances in the life sciences. Yet, whereas discovery of theories was extensively deliberated by philosophers of science, very little philosophical attention was paid to the discovery of factual entities. This paper examines historical and philosophical aspects of the experimental discovery by Carl Woese of archaea, prokaryotes that comprise one of the three principal domains of the phylogenetic tree. Borrowing Kuhn's terminology, this discovery of a major biological entity was made during a 'normal science' project of building molecular taxonomy for prokaryotes. Unexpectedly, however, an observed anomaly instigated the discovery of archaea. Substantiation of the existence of the new archaeal entity and consequent reconstruction of the phylogenetic tree prompted replacement of a long-held model of a prokarya and eukarya bipartite tree of life by a new model of a tripartite tree comprising of bacteria, archaea, and eukarya. This paper explores the history and philosophical implications of the progression of Woese's project from normal science to anomaly-instigated model-changing discovery. It is also shown that the consequential discoveries of RNA splicing and of ribozymes were similarly prompted by unexpected irregularities during normal science activities. It is thus submitted that some discoveries of factual biological entities are triggered by unforeseen observational or experimental anomalies.

Discovery of novel genetic syndromes in Latin America: Opportunities and challenges

Latin America (LatAm) has a rich and historically significant role in delineating both novel and well-documented genetic disorders. However, the ongoing advancements in the field of human genetics pose challenges to the relatively slow adaption of LatAm in the field. Here, we describe past and present contributions of LatAm to the discovery of novel genetic disorders, often referred as novel gene-disease associations (NGDA). We also describe the current methodologies for discovery of NGDA, taking into account the latest developments in genomics. We provide an overview of opportunities and challenges for NGDA research in LatAm considering the steps currently performed to identify and validate such associations. Given the multiple and diverse needs of populations and countries in LatAm, it is imperative to foster collaborations amongst patients, indigenous people, clinicians and scientists. Such collaborative effort is essential for sustaining and enhancing the LatAm´s contributions to the field of NGDA.

Biallelic variants in NSUN6 cause an autosomal recessive neurodevelopmental disorder

PURPOSE

5-methylcytosine RNA modifications are driven by NSUN methyltransferases. Although variants in NSUN2 and NSUN3 were associated with neurodevelopmental diseases, the physiological role of NSUN6 modifications on transfer RNAs and messenger RNAs remained elusive.

METHODS

We combined exome sequencing of consanguineous families with functional characterization to identify a new neurodevelopmental disorder gene.

RESULTS

We identified 3 unrelated consanguineous families with deleterious homozygous variants in NSUN6. Two of these variants are predicted to be loss-of-function. One maps to the first exon and is predicted to lead to the absence of NSUN6 via nonsense-mediated decay, whereas we showed that the other maps to the last exon and encodes a protein that does not fold correctly. Likewise, we demonstrated that the missense variant identified in the third family has lost its enzymatic activity and is unable to bind the methyl donor S-adenosyl-L-methionine. The affected individuals present with developmental delay, intellectual disability, motor delay, and behavioral anomalies. Homozygous ablation of the NSUN6 ortholog in Drosophila led to locomotion and learning impairment.

CONCLUSION

Our data provide evidence that biallelic pathogenic variants in NSUN6 cause one form of autosomal recessive intellectual disability, establishing another link between RNA modification and cognition.

Inferring Retinal Degeneration-Related Genes Based on Xgboost

Retinal Degeneration (RD) is an inherited retinal disease characterized by degeneration of rods and cones photoreceptor cells and degeneration of retinal pigment epithelial cells. The age of onset and disease progression of RD are related to genes and environment. At present, research has discovered five genes closely related to RD. They are RHO, PDE6B, MERTK, RLBP1, RPGR, and researchers have developed corresponding gene therapy methods. Gene therapy uses vectors to transfer therapeutic genes, genetically modify target cells, and correct or replace disease-causing RD genes. Therefore, identifying the pathogenic genes of RD will play an important role in the development of treatment methods for the disease. However, the traditional methods of identifying RD-related genes are mostly based on animal experiments, and currently only a small number of RD-related genes have been identified. With the increase of biological data, Xgboost is purposed in this article to identify RP-related genes. Xgboost adds a regular term to control the complexity of the model, hence using Xgboost to find out true RD-related genes from complex and massive genes is suitable. The problem of overfitting can be avoided to some extent. To verify the power of Xgboost to identify RD-related genes, we did 10-cross validation and compared with three traditional methods: Random Forest, Back Propagation network, Support Vector Machine. The accuracy of Xgboost is 99.13% and AUC is much higher than other three methods. Therefore, this article can provide technical support for efficient identification of RD-related genes and help researchers have a deeper the understanding of the genetic characteristics of RD.