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I have done research on the protein (CYP3A4). I have its function, purpose and diseases (breast cancer, prostate cancer, testicular cancer, lung cancer). I am however having trouble finding the mutations of the protein and how it correlates with the diseases, specifically breast and prostate cancer.
I think I understand what you are asking so I will make a little guide to find what you need:
NOTE the mutations you are searching for are probably on gene level so you can search your protein on Uniprot and then use the gene name to search on DisGeNET, you typ in the gene you are searching for:
Then you hit the search button and this will give you a result like this:
You can see that this site uses a sort of text-mining, in other words it is searching for the gene name associated with disease in articles. The score is an indication for how good the text-mining "worked". As mentioned in the manual:
We have developed a score to rank the gene-disease associations according to their level of evidence. DisGeNET gene-disease association score takes into account the number and type of sources (level of curation, model organisms), and the number of publications supporting the association. The score ranges from 0 to 1.
Then you have to click the
browse details…in the down right corner. This will guide you to another summary page like this:
(NOTE I omitted a lot of rows to avoid huge images in the answer, so there will be more rows in your result). Because you were interested in prostate cancer I will click on Prostatic Neoplasms:
You can click on the
3to obtain the 3 articles where the combination of the CYP3A4 and Prostatic Neoplasms was found. This will give you a list like:
As you mentioned in your question
"where do I find such papers"
You can easily click on the PMID this will guide you to the article, for example the first article:
CYP3A4 polymorphisms--potential risk factors for breast and prostate cancer: a HuGE review(link).
I think this will help you to find the papers related to the protein + disease, you can further read the papers to find mutations which cause these diseases.
Protein sector analysis for the clustering of disease-associated mutations
The importance of mutations in disease phenotype has been studied, with information available in databases such as OMIM. However, it remains a research challenge for the possibility of clustering amino acid residues based on an underlying interaction, such as co-evolution, to understand how mutations in these related sites can lead to different disease phenotypes.
This paper presents an integrative approach to identify groups of co-evolving residues, known as protein sectors. By studying a protein family using multiple sequence alignments and statistical coupling analysis, we attempted to determine if it is possible that these groups of residues could be related to disease phenotypes. After the protein sectors were identified, disease-associated residues within these groups of amino acids were mapped to a structure representing the protein family. In this study, we used the proposed pipeline to analyze two test cases of spermine synthase and Rab GDP dissociation inhibitor.
The results suggest that there is a possible link between certain groups of co-evolving residues and different disease phenotypes. The pipeline described in this work could also be used to study other protein families associated with human diseases.
Health Conditions Related to Genetic Changes
Several mutations in the ATP7A gene are responsible for a condition called occipital horn syndrome or X-linked cutis laxa, which is considered a mild form of Menkes syndrome. Occipital horn syndrome is characterized by loose and sagging skin, wedge-shaped calcium deposits in a bone at the base of the skull (the occipital bone), coarse hair, and loose joints.
Most of the mutations that cause occipital horn syndrome reduce but do not eliminate the production of the ATP7A protein. A shortage of this protein impairs the absorption of copper from food and prevents its normal distribution to cells throughout the body. The decreased supply of copper can reduce the activity of numerous copper-containing enzymes, affecting the structure and function of bone, skin, hair, blood vessels, and the nervous system. The reduced activity of these enzymes underlies the characteristic features of occipital horn syndrome.
Researchers have identified more than 150 mutations in the ATP7A gene that cause Menkes syndrome. Many of these mutations delete part of the gene and likely result in a shortened ATP7A protein. Other mutations insert additional DNA building blocks (nucleotides) into the gene or change single nucleotides. All of these mutations prevent the production of functional ATP7A protein. As a result, the absorption of copper from food is impaired, and copper is not supplied to certain enzymes. The abnormal protein may get stuck in the cell membrane and become unable to shuttle back and forth from the Golgi apparatus.
The disrupted activity of the ATP7A protein causes copper to be poorly distributed to cells in the body. Copper accumulates in some tissues, such as the small intestine and kidneys, while the brain and other tissues have unusually low levels. The decreased supply of copper can reduce the activity of numerous copper-containing enzymes, affecting the structure and function of bone, skin, hair, blood vessels, and the nervous system. The signs and symptoms of Menkes syndrome are caused by the reduced activity of these copper-containing enzymes.
MedlinePlus Genetics provides information about Charcot-Marie-Tooth disease
There are currently 170 mutations in the database ( Table 1) representing 403 patients in 273 unrelated families. About half of the mutations (104) appear in Btk, the most extensively studied kinase with disease-causing alterations ( 2). A significant number of mutations have also been found in the Irk, Kit, Met and Ret kinases. Most of the kinases contain mutational hotspots, sites where mutations have been identified several times. In Btk and in many other diseases such sites are in CpG dinucleotides, which usually have pyrimidines 5′ and purines on their 3′ side ( 18). The total number of different mutations in Ret is modest, but one site harbours by far the greatest number of mutations, M918. The M918T mutation which accounts for about all MEN2B cases, has been shown to occur on the paternal chromosome ( 19). The actual number of unrelated Ret families is not possible to determine based on the published articles. The same also applies to some other cases. The number of families include only those that can be verified from literature.
The distribution of mutations in Btk is similar to many other diseases with respect to mutation types. In the other kinases, missense mutations dominate and, e.g., nonsense mutations are clearly underrepresented. This can be because of several reasons, e.g., due to detection methods used. In Jak3, a number of mutations affect the inactive pseudokinase domain. The majority of these are deletions, which affect the folding of the whole protein, but there are also some missense mutations.
The structural basis for the kinase related diseases have been studied in the case of Irk ( 20), Btk ( 12, 21, 22) and Jak3 (Vihinen et al., in preparation) by using crystal structures and molecular modelling. Some of the mutations affect the conserved hallmark residues, but there are also numerous alterations in non-conserved residues. The structural consequences range from impaired folding to catalytic or substrate binding mutations to alterations in sites forming contacts with other domains or ligands.
Researchers Find That a ‘Silent’ Gene Mutation Can Change the Function of an Anticancer Drug Pump
A genetic mutation that does not cause a change in the amino acid sequence of the resulting protein can still alter the protein’s expected function, according to a new study conducted at the National Cancer Institute (NCI), part of the National Institutes of Health (NIH). The study shows that mutations involving only single chemical bases in a gene known as the multidrug resistance gene (MDR1) that do not affect the protein sequence of the MDR1 gene product can still alter the protein’s ability to bind certain drugs. Changes in drug binding may ultimately affect the response to treatment with many types of drugs, including those used in chemotherapy. The results of this study appear online in Science Express on December 21, 2006*.
The genetic mutations examined in this research are known as single nucleotide polymorphisms (SNPs) and are very common. Some SNPs do not change the DNA’s coding sequence, so these types of so-called ‘silent’ mutations were not thought to change the function of the resulting proteins.
“This study provides an exception to the silent SNP paradigm by showing that some minor mutations can profoundly affect normal cell activity,” said NCI Director John E. Niederhuber, M.D. “These results may not only change our thinking about mechanisms of drug resistance, but may also cause us to reassess our whole understanding of SNPs in general, and what role they play in disease.”
Despite success in treating some cancers with chemotherapy, many tumors are naturally resistant to anticancer drugs or become resistant to chemotherapy after many rounds of treatment. Researchers at NCI and elsewhere have discovered one way that cancer cells become resistant to anticancer drugs: they expel drug molecules using pumps embedded in the cellular membrane. One of these pumps, called P-glycoprotein (P-gp), is the protein product of the MDR1 gene and contributes to drug resistance in about 50 percent of human cancers. P-gp prevents the accumulation of powerful anticancer drugs, such as etoposide and Taxol, in tumor cells. The same pump is also involved in determining how many different drugs, including anticancer drugs, are taken up or expelled from the cell.
In this study, researchers led by Michael M. Gottesman, M.D., head of the Laboratory of Cell Biology within NCI's Center for Cancer Research, demonstrated that SNPs in the MDR1 gene result in a pump with an altered ability to interact with certain drugs and pump inhibitor molecules. In order to show that SNPs can actually affect pump activity, the researchers genetically engineered cells in the laboratory to contain either normal MDR1 or a copy of the MDR1 gene that contains one or more SNPs. Then, they used fluorescent dyes to track pump function based on the accumulation of dye in the cell or the export of dye out of the cell with and without various inhibitors of P-gp. This showed that although the SNPs did not change the expected P-gp protein sequence, the presence of one particular SNP, when in combination with one or two other SNPs that frequently occur with it, resulted in less effective pump activity for some drugs than normal P-gp without the SNP.
The P-gp protein sequences and production levels were normal in both the cells that received the normal MDR1 gene and those that received the mutant versions. Therefore, in order to determine how the SNPs affected pump function, Chava Kimchi-Sarfaty, Ph.D., lead author of the study, and co-workers used an antibody that could distinguish between different P-gp structural conformations. They found significant differences in antibody binding consistent with the existence of different protein conformations in the products of MDR1 genes with or without the SNPs. These results indicate that the shape of a protein is determined by more than its amino acid — or primary — sequence.
Like all proteins, P-gp is comprised of amino acid building blocks. While making P-gp, the cell’s protein synthesizing machinery knows exactly which amino acids to put together and in which order by reading a copy of the MDR1 gene coding sequence. DNA consists of a sequence of chemical bases, and the code for individual amino acids is represented by specific sets of three adjacent DNA bases called codons. The SNP that Gottesman and his colleagues studied had only one changed base in one codon of the MDR1 gene. Since several different codons can contain the code for the same amino acid, this SNP only altered the gene by converting one common codon to a rare one, but did not change the amino acid for which it coded.
“We think that this SNP affected protein function because it forced the cell to read a different DNA codon than it usually does,” said Gottesman. “While the same exact protein sequence eventually got made, this slight change might slow the folding rhythm, resulting in an altered protein conformation, which in turn affects function.”
Since silent SNPs are frequently found in nature, their biological role has largely been overlooked. However, this study raises the possibility that even ‘silent’ mutations could contribute to the development of cancer and many other diseases.
For more information about cancer, please visit the NCI Web site at http://www.cancer.gov, or call NCI's Cancer Information Service at 1-800-4-CANCER (1-800-422-6237).
About the National Institutes of Health (NIH): NIH, the nation's medical research agency, includes 27 Institutes and Centers and is a component of the U.S. Department of Health and Human Services. NIH is the primary federal agency conducting and supporting basic, clinical, and translational medical research, and is investigating the causes, treatments, and cures for both common and rare diseases. For more information about NIH and its programs, visit www.nih.gov.
NIH&hellipTurning Discovery Into Health ®
* Kimchi-Sarfaty C, Mi Oh J, Kim I-W, Sauna ZE, Calcagno AM, Ambudkar SV, Gottesman MM. A "silent" polymorphism in the MDR1 gene changes substrate specificity. Science Express, December 21, 2006.
Construction of Datasets
In this study, we performed an evaluation of eight tools for prediction of the effects of mutations on protein function and combined six of them into the consensus classifier PredictSNP (for explanation of employed evaluation metrics see Supporting text S1). The proper benchmark dataset is of prime importance for the evaluation of prediction tools since overlaps between the composition of the benchmark dataset and the training datasets of a tool would result into overly optimistic performance evaluation of such tool , . These overlaps can also hinder the construction of consensus classifier as an unwarranted degree of significance could be given to the tools with overlap between datasets . For these reasons, we strived to secure the full independence of the PredictSNP benchmark dataset for unbiased evaluation of selected tools and proper training of our consensus classifier. The same care was also taken when preparing both testing datasets for the comparison of performance of PredictSNP consensus classifier, its constituent tools and other consensus classifiers.
The independent benchmark dataset was combined from five redundant datasets by removing all duplicates and subtracting all mutations present at the positions used in the training of the evaluated tools or in any of the two testing datasets (Figure 1). This procedure resulted in the PredictSNP benchmark dataset of 43,882 mutations (24,082 neutral and 19,800 deleterious) in the 10,085 protein sequences (Dataset S1). Complementary OVERFIT dataset was compiled from mutations present in the training sets of evaluated tools (Dataset S2). This dataset contained 32,776 mutations (15,081 neutral and 17,695 deleterious) in the 6,889 protein sequences.
The Genetics Analysis of Molecular Pathogenesis for Alzheimer&aposs Disease
Introduction: The molecular pathogenesis of Alzheimer's disease (AD) is still not clear, and the relationship between gene expression profile for different brain regions has not been studied.
Objective: Bioinformatic analysis at the genetic level has become the best way for the pathogenesis research of AD, which can analyze the abovementioned relationship.
Methods: In this study, the datasets of AD were obtained from the Gene Expression Omnibus (GEO), and Qlucore Omics Explorer (QOE) software was used to screen differentially expressed genes of GSE36980 and GSE9770 and verify gene expression of GSE63060. The Gene Ontology (GO) function enrichment analysis of these selected genes was conducted by Database for Annotation, Visualization, and Integrated Discovery (DAVID), and then the gene/protein interaction network was established by STRING to find the related proteins. R language was used for drafting maps and plots.
Results: There were 20 differentially expressed genes related to AD selected from GSE36980 (p = 6.2e-6, q = 2.9422e-4) and GSE9770 (p = 3.3e-4, q = 0.016606). Their expression levels of the AD group were lower than those in the control group and varied among different brain regions. Cellular morphogenesis and establishment or maintenance of cell polarity were enriched, and LRRTM1 and RASAL1 were identified by the integration network. Moreover, the analysis of GSE63060 verified the expression level of LRRTM1 and RASAL1 in Alzheimer's patients, which was much lower than that in normal people aged >65 years.
Conclusions: The pathogenesis of AD at molecular levels may link to cell membrane structures and signal transduction hence, a list of 20 genes, including LRRTM1 and RASAL1,potentially are important for the discovery of treatment target or molecular marker of AD.
Keywords: Alzheimer’s disease Bioinformatic analysis LRRTM1 Membrane structures RASAL1 Signal transduction.
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Computational modeling can predict mutation hotspots in SARS-CoV-2 spike protein
Computational modeling shows that mutations on SARS-CoV-2's spike protein that enhance the virus’ ability to bind to the ACE2 receptor occur in two clusters or mutation "hotspots." Credit: Hin Hark Gan and Kristin Gunsalus, NYU's Department of Biology
SARS-CoV-2 has evolved to acquire mutations on the spike protein—the part of the virus that protrudes from its surface and latches onto cells to infect them—that enhance the coronavirus' ability to bind to human cells or evade antibodies. A new study from the Centers for Genomics and Systems Biology at New York University and NYU Abu Dhabi uses computational modeling to assess the biological significance of spike protein mutations, uncovering versions of the virus that bind more tightly or resist antibodies and offering a promising public health surveillance tool.
The study, which appears in the Journal of Molecular Biology, also suggests that these mutations on the spike protein are a key reason for the virus' rapid spread in parts of the world.
New and more transmissible COVID-19 variants have emerged in recent months, fueling surges of cases in countries like India and Brazil. As a public health measure, rapid surveillance methods are needed to determine the biological effects of variants and to help anticipate emerging infectious viral strains. But monitoring new variants is no small task genome sequencing shows that the SARS-CoV-2 spike protein alone, for example, has about 5,000 possible variants.
"Screening such a large set of variants poses a tremendous challenge for conventional experimental methods," said Hin Hark Gan, a senior research scientist at NYU's Center for Genomics and Systems Biology and the study's lead author. "An advantage of computer-based modeling is that a hundred mutations can be readily assessed in a few days."
Gan and his colleagues turned to a computational method that models how the SARS-CoV-2 spike protein recognizes the ACE2 receptor—a protein on the surface of many types of cells—to gain entry into host cells. Studies of coronaviruses indicate that spike-ACE2 recognition is the basis for infection.
The researchers focused on screening the mutations located where the spike protein and ACE2 receptor meet. They assessed 1,003 mutation combinations in the spike and ACE2 proteins, including those resulting in the fast-spreading spike variants that have originated in Brazil, South Africa, the U.K., and India.
Their systematic assessment of variants uncovered that spike mutations that bind tightly to the ACE2 receptor occur in two clusters or mutation "hotspots" on the binding interface. These hotspots are located in structurally flexible regions, indicating that mutations that increase binding effectively reprogrammed the spike conformation to enhance its recognition of the ACE2 receptor.
The researchers also looked at single, double, and triple mutations in the critical spike interface region, which make up some of the recently emerged infectious variants. Their modeling analysis suggests the spike variants S477N, N501Y, and S477N + E484K and E484K + N501Y—fast-spreading double mutants found in Brazil, South Africa, the U.S., and U.K.—have increased binding to the ACE2 receptor relative to the original coronavirus that emerged in Wuhan.
Gan and colleagues observed that the E484K and E484Q mutations found in some recent fast-spreading variants are not only predicted to bind more strongly to ACE2 but have also been shown to confer antibody resistance. Neutralizing antibodies are produced in response to viral infection and target different sites on the spike protein to prevent the virus from invading host cells. This prompted the researchers to look at another factor contributing to viral transmission: antibody resistance of individual spike mutations.
In particular, the variant circulating in India has two mutations in the spike interface region: L452R and E484Q. This variant is not predicted to bind to the ACE2 receptor more tightly than the virus that originated in Wuhan, likely because the individual mutations have opposing effects (the L452R mutation binds less easily while the E484Q mutation binds more easily). Strikingly, however, both of these mutations are strong antibody evaders, a scenario not found in other recent variants.
"As more of antibody target sites become resistant to antibodies due to viral mutations, the efficacy of existing antibodies and vaccines may diminish," added Gan. "This scenario is a likely cause for the rapid spread of the variant in India."
The study not only provides explanations for the coronavirus' rapid spread—both mutations that enhance binding to human cells and help evade antibodies—but also points to a promising predictive tool in the ongoing public health fight against SARS-CoV-2.
"Our computational modeling method can be used as a real-time surveillance tool to screen for emerging infectious COVID-19 variants. It allows for a more timely response to emerging outbreaks and could be used to guide the development of new vaccines," said Kristin C. Gunsalus, professor of biology at NYU, faculty director of bioinformatics at NYU Abu Dhabi, and the study's senior author.
Frequenting Sequencing: How Genetics Teaches Us Cilia Biology
Genetic diseases are awesome educators. Studying rare syndromes often unearths new biologic mechanisms and therapeutic directions. In pulmonary medicine, cystic fibrosis has been the seasoned professor, instructing us on protein folding, drug development, and how to meld cutting-edge care with research. Primary ciliary dyskinesia (PCD) may be similarly informative. This disease of faulty cilia is credibly the newly minted instructor. PCD tells us much about the challenges of disease diagnosis, and by studying its genetics, we may be able to uncover new cellular mechanisms. In this issue of the Journal, Zietkiewicz and colleagues (pp. 440–449) describe patients bearing mutations in CFAP300, which was discovered in a large population of patients with PCD in Poland (1). Together with recent data from two other groups (2, 3), this study may facilitate our understanding of how motor proteins are sorted and delivered along the length of the cilia.
Motile cilia generate force to move fluid for airway clearance, reproduction, cerebral spinal fluid circulation, and the establishment of left–right asymmetry during development. Consequently, patients with PCD typically suffer from chronic rhinosinusitis, otitis media, and lung infections, leading to bronchiectasis. Infertility also occurs and cardiac defects are not infrequent.
Not surprisingly, it is difficult to establish a diagnosis of PCD, so the American Thoracic Society and European Respiratory Society provide guidelines (4, 5). The American Thoracic Society recommends the use of four criteria: 1) unexplained respiratory distress in full-term newborns, 2) year-round wet cough, 3) year-round nasal congestion beginning at ≤6 months of age, and 4) an organ laterality defect. Meeting two of these criteria provides a specificity of 72%, and meeting all four provides a specificity of 99%. As a next step, sampling nasal nitric oxide levels for a low level can bolster the diagnosis (6). Beyond this, one can examine ciliary ultrastructure and waveform by transmission EM (TEM) (7) and videomicroscopy, respectively however, the results are challenging to interpret and false negatives are possible (5).
Enter diagnostic genetics. PCD is an autosomal-recessive and genetically heterogeneous disease that is now attributed to mutations in over 40 different genes (8, 9). This number is bound to grow, considering that beating cilia require many moving parts, all at risk of mutation. Initially, attention was directed toward mutations in the ciliary motors, megadalton-sized dynein proteins precisely spaced at 96-nm intervals along the cilia microtubules. The motors are part of multimeric complexes called “dynein arms” that are positioned on the inner and outer edges of the ciliary microtubules. These inner and outer dynein arms (IDA and ODA, respectively) generate the characteristic waveform. Mutation of a protein within the IDA or ODA can cause the loss of that structure on transmission EM (7). However, some patients are mutant in a single gene yet are missing both dynein arms. It was in such patients that Zietkiewicz and colleagues sought to establish a diagnosis of PCD.
So far, mutations in about a dozen genes have been shown to result in loss of both IDA and ODA in individuals with PCD. These genes, which are known as dynein axonemal assembly factors, code for proteins that are present only in the cytoplasm and never in the cilia (10). They include DNAAF1, DNAAF2, DNAAF3, DYX1C1 (DNAAF4), HEATR2 (DNAAF5), SPAG1, ZMYND10, LRRC6, C21ORF59, and PIH1D3. Emerging proteomics data suggest that these factors chaperone IDA and ODA components to engage the professional folding protein, HSP90, to assemble the “arms.” The mechanisms for interaction have not been elucidated, but clues come from their puncta-like presence in the cytoplasm (10).
To explore this issue, Huizar and colleagues expressed fluorescently labeled assembly proteins in Xenopus embryos (11). The cytoplasm of the multiciliated cells of the larvae lit up with multiple puncta, each of which included the assembly proteins, HSP90, and dynein proteins. The particles were distinct from the Golgi and endosomes, and demonstrated characteristics of a phase-separated structure. Time-lapse imaging of fluorescent recovery after photobleaching showed the rapid exchange of assembly factors in and out of the liquid-phase particle, while the dynein proteins stayed put. How the assembled arms then engage the ciliary intraflagellar transport (IFT) system for delivery to specific locations along the cilium remains unresolved.
Again, diagnostic genetics may provide insight. Zietkiewicz and colleagues used whole-exome sequencing to study a cohort of 120 patients with PCD who were missing the IDA and ODA. One specific mutation, a deletion that introduces an early truncation of CFAP300, was present in 17 patients from 15 families (1). This is an extraordinarily large number of affected subjects with a shared mutation (without apparent consanguinity). By contrast, large European PCD research groups studying the CFAP300 mutation found this Slavic allele in only two of eight subjects (from Germany and Italy) (2, 3). This Slavic founder effect indicates the impact of a regional population on the presence of a particular mutation. In the United States, high numbers of patients with PCD due to a single mutation are found in Amish-Mennonite communities (12) and regions with many Ashkenazi Jews (13). The Slavic founder gene will likely turn up in PCD cases within U.S. Slavic immigrant communities.
What is the function of CFAP300? Absent ODA/IDA and evidence that CFAP300 binds the assembly protein DNAAF2 suggest an assembly role. Antibodies to CFAP300 are not available. However, immunofluorescence staining for IDA and ODA proteins in CFAP300 mutant cells suggests a transport role. All three reports (1–3) show that in mutant cells, ODA motors concentrate in the cell’s apical domain, possibly as a result of failed CFAP300-dependent transport. In contrast, the IDA proteins are either reduced or absent in the mutant (possibly degraded), or, in the Slavic mutation, restricted to the proximal portion of the cilia, suggesting that different mutations have unique effects. Moreover, the separate positions of IDA and ODA in the mutant cells point to ODA/IDA sorting pathways. The strongest data supporting a transport role come from model organisms, as CFAP300 is highly conserved evolutionarily. Fassad and colleagues showed that the orthologous CFAP300 protein is present in cilia of Chlamydomonas and Paramecium in a pattern typical of IFT (2).
Collectively, these reports suggest that CFAP300 transports ciliary cargo from the cytoplasm to the cilia, possibly as an adapter that engages IFT proteins (IFT46) for delivery along the cilia (2, 3) ( Figure 1 ). Surprisingly, only two such IFT adapters are known (14, 15). It is likely we will find specific transporters to organize the cargoes along the cilia. The continued use of genetic diagnoses will provide satisfaction to patients, advance our understanding of disease, and teach us about cilia biology.
Figure 1. Possible sites of CFAP300 activity during cilia assembly. Recent reports suggest that CFAP300 plays a role at multiple sites during motile cilia assembly, including 1) during cytoplasmic dynein assembly, bound to assembly factor DNAAF2 2) transport across the basal body 3) transfer of cargo to the intraflagellar transport (IFT) trains by binding IFT46 and 4) transport of motors throughout the growing cilia. CFAP300 = cilia and flagella associated protein 300.