Category Archives: Genome

Guh, A. Y. et al. Trends in U.S. Burden of Clostridioides difficile infection and outcomes. N. Engl. J. Med. 382, 13201330 (2020).

Article CAS PubMed PubMed Central Google Scholar

Antibiotic Resistance Threats in the United States (Centers for Disease Control and Prevention, 2019); https://doi.org/10.15620/cdc:82532

Evans, C. T. & Safdar, N. Current trends in the epidemiology and outcomes of Clostridium difficile infection. Clin. Infect. Dis. 60, S66S71 (2015).

Article PubMed Google Scholar

Eyre, D. W. et al. Diverse sources of C. difficile infection identified on whole-genome sequencing. N. Engl. J. Med. 369, 11951205 (2013).

Article CAS PubMed Google Scholar

Walker, A. S. et al. Characterisation of Clostridium difficile hospital ward-based transmission using extensive epidemiological data and molecular typing.PLoS Med. 9, e1001172 (2012).

Article PubMed PubMed Central Google Scholar

Blixt, T. et al. Asymptomatic carriers contribute to nosocomial Clostridium difficile infection: a cohort study of 4508 patients. Gastroenterology 152, 10311041 (2017).

Article PubMed Google Scholar

Galdys, A. L., Curry, S. R. & Harrison, L. H. Asymptomatic Clostridium difficile colonization as a reservoir for Clostridium difficile infection. Expert Rev. Anti Infect. Ther. 12, 967980 (2014).

Article CAS PubMed Google Scholar

Furuya-Kanamori, L. et al. Asymptomatic Clostridium difficile colonization: epidemiology and clinical implications. BMC Infect. Dis. 15, 516 (2015).

Article PubMed PubMed Central Google Scholar

Sethi, A. K., Al-Nassir, W. N., Nerandzic, M. M., Bobulsky, G. S. & Donskey, C. J. Persistence of skin contamination and environmental shedding of Clostridium difficile during and after treatment of C. difficile infection. Infect. Control Hosp. Epidemiol. 31, 2127 (2010).

Article PubMed Google Scholar

McDonald, L. C. & Diekema, D. J. Point-counterpoint: active surveillance for carriers of toxigenic Clostridium difficile should be performed to guide prevention efforts. J. Clin. Microbiol. 56, e00782-18 (2018).

Article PubMed PubMed Central Google Scholar

Baron, S. W. et al. Screening of Clostridioides difficile carriers in an urban academic medical center: understanding implications of disease.Infect. Control Hosp. Epidemiol. 41, 149153 (2020).

PubMed PubMed Central Google Scholar

McDonald, L. C. et al. Clinical practice guidelines for Clostridium difficile infection in adults and children: 2017 update by the Infectious Diseases Society of America (IDSA) and Society for Healthcare Epidemiology of America (SHEA). Clin. Infect. Dis. 66, 987994 (2018).

Article CAS PubMed Google Scholar

Didelot, X. et al. Microevolutionary analysis of Clostridium difficile genomes to investigate transmission. Genome Biol. 13, R118 (2012).

Article PubMed PubMed Central Google Scholar

Surveillance for C. difficile, MRSA, and Other Drug-resistant Infections (Centers for Disease Control and Prevention, 2018); https://www.cdc.gov/nhsn/acute-care-hospital/cdiff-mrsa/index.html

Magill, S. S. et al. Multistate point-prevalence survey of health careassociated infections. N. Engl. J. Med. 370, 11981208 (2014).

Article CAS PubMed PubMed Central Google Scholar

Ponnada, S. et al. Acquisition of Clostridium difficile colonization and infection after transfer from a Veterans Affairs hospital to an affiliated long-term care facility. Infect. Control Hosp. Epidemiol. 38, 10701076 (2017).

Article PubMed Google Scholar

Curry, S. R. et al. Use of multilocus variable number of tandem repeats analysis genotyping to determine the role of asymptomatic carriers in Clostridium difficile transmission. Clin. Infect. Dis. 57, 10941102 (2013).

Article PubMed PubMed Central Google Scholar

Riggs, M. M. et al. Asymptomatic carriers are a potential source for transmission of epidemic and nonepidemic Clostridium difficile strains among long-term care facility residents. Clin. Infect. Dis. 45, 992998 (2007).

Article PubMed Google Scholar

Sheth, P. M. et al. Evidence of transmission of Clostridium difficile in asymptomatic patients following admission screening in a tertiary care hospital. PLoS ONE 14, e0207138 (2019).

Article CAS PubMed PubMed Central Google Scholar

Kong, L. Y. et al. Clostridium difficile: investigating transmission patterns between infected and colonized patients using whole genome sequencing. Clin. Infect. Dis. 68, 204209 (2019).

Article PubMed Google Scholar

Zacharioudakis, I. M., Zervou, F. N., Pliakos, E. E., Ziakas, P. D. & Mylonakis, E. Colonization with toxinogenic C. difficile upon hospital admission, and risk of infection: a systematic review and meta-analysis. Am. J. Gastroenterol. 110, 381390 (2015).

Article PubMed Google Scholar

Eyre, D. W. et al. Asymptomatic Clostridium difficile colonisation and onward transmission. PLoS ONE 8, e78445 (2013).

Article CAS PubMed PubMed Central Google Scholar

Truong, C. et al. Clostridium difficile rates in asymptomatic and symptomatic hospitalized patients using nucleic acid testing. Diagn. Microbiol. Infect. Dis. 87, 365370 (2017).

Article CAS PubMed Google Scholar

Clabots, C. R., Johnson, S., Olson, M. M., Peterson, L. R. & Gerding, D. N. Acquisition of Clostridium difficile by hospitalized patients: evidence for colonized new admissions as a source of infection. J. Infect. Dis. 166, 561567 (1992).

Article CAS PubMed Google Scholar

Worley, J. et al. Genomic determination of relative risks for Clostridioides difficile infection from asymptomatic carriage in intensive care unit patients.Clin. Infect. Dis. 73, e1727e1736 (2021).

Article CAS PubMed Google Scholar

Brazier, J. S. et al. Screening for carriage and nosocomial acquisition of Clostridium difficile by culture: a study of 284 admissions of elderly patients to six general hospitals in Wales. J. Hosp. Infect. 43, 317319 (1999).

Article CAS PubMed Google Scholar

Lanzas, C. & Dubberke, E. R. Effectiveness of screening hospital admissions to detect asymptomatic carriers of Clostridium difficile: a modeling evaluation. Infect. Control Hosp. Epidemiol. 35, 10431050 (2014).

Article PubMed Google Scholar

Longtin, Y. et al. Effect of detecting and isolating Clostridium difficile carriers at hospital admission on the incidence of C. difficile infections: a quasi-experimental controlled study. JAMA Intern. Med. 176, 796804 (2016).

Article PubMed Google Scholar

Tickler, I. A. et al. Strain types and antimicrobial resistance patterns of Clostridium difficile isolates from the United States, 2011 to 2013. Antimicrob. Agents Chemother. 58, 42144218 (2014).

Article PubMed PubMed Central Google Scholar

Keegan, J. et al. Toxigenic Clostridioides difficile colonization as a risk factor for development of C. difficile infection in solid-organ transplant patients. Infect. Control Hosp. Epidemiol. 42, 287291 (2021).

Article PubMed Google Scholar

Shim, J. K., Johnson, S., Samore, M. H., Bliss, D. Z. & Gerding, D. N. Primary symptomless colonisation by Clostridium difficile and decreased risk of subsequent diarrhoea. Lancet 351, 633636 (1998).

Article CAS PubMed Google Scholar

Goldenberg, J. Z. et al. Probiotics for the prevention of Clostridium difficile-associated diarrhea in adults and children.Cochrane Database Syst. Rev. 12, CD006095 (2017).

PubMed Google Scholar

Mullane, K. M. et al. A randomized, placebo-controlled trial of fidaxomicin for prophylaxis of Clostridium difficile-associated diarrhea in adults undergoing hematopoietic stem cell transplantation. Clin. Infect. Dis. 68, 196203 (2019).

Article CAS PubMed Google Scholar

Baunwall, S. M. D. et al. Faecal microbiota transplantation for recurrent Clostridioides difficile infection: an updated systematic review and meta-analysis. EClinicalMedicine 2930, 100642 (2020).

Article PubMed PubMed Central Google Scholar

Tamma, P. D. et al. Association of a safety program for improving antibiotic use with antibiotic use and hospital-onset Clostridioides difficile infection rates among US hospitals. JAMA Netw. Open 4, e210235 (2021).

Article PubMed PubMed Central Google Scholar

Baur, D. et al. Effect of antibiotic stewardship on the incidence of infection and colonisation with antibiotic-resistant bacteria and Clostridium difficile infection: a systematic review and meta-analysis. Lancet Infect. Dis. 17, 9901001 (2017).

Article PubMed Google Scholar

Caroff, D. A., Yokoe, D. S. & Klompas, M. Evolving insights into the epidemiology and control of Clostridium difficile in hospitals. Clin. Infect. Dis. 65, 12321238 (2017).

Article CAS PubMed Google Scholar

Crobach, M. J. T. et al. Understanding Clostridium difficile colonization. Clin. Microbiol. Rev. 31, e00021-17 (2018).

Article CAS PubMed PubMed Central Google Scholar

Behroozian, A. A. et al. Detection of mixed populations of Clostridium difficile from symptomatic patients using capillary-based polymerase chain reaction ribotyping. Infect. Control Hosp. Epidemiol. 34, 961966 (2013).

Article PubMed PubMed Central Google Scholar

Dayananda, P. & Wilcox, M. H. A review of mixed strain clostridium difficile colonization and infection. Front. Microbiol. 10, 692 (2019).

Article PubMed PubMed Central Google Scholar

Sun, J., Mc Millen, T., Babady, N. E. & Kamboj, M. Role of coinfecting strains in recurrent Clostridium difficile infection. Infect. Control Hosp. Epidemiol. 37, 14811484 (2016).

Article PubMed PubMed Central Google Scholar

Seekatz, A. M. et al. Presence of multiple Clostridium difficile strains at primary infection is associated with development of recurrent disease. Anaerobe 53, 7481 (2018).

Article PubMed PubMed Central Google Scholar

Gonzalez-Orta, M. et al. Are many patients diagnosed with healthcare-associated Clostridioides difficile infections colonized with the infecting strain on admission? Clin. Infect. Dis. 69, 18011804 (2019).

Article PubMed Google Scholar

Lin, M. Y. et al. Impact of mandatory infectious disease specialist approval on hospital-onset Clostridioides difficile infection rates and testing appropriateness.Clin. Infect. Dis. 77, 346350 (2023).

Article PubMed Google Scholar

Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 21142120 (2014).

Article CAS PubMed PubMed Central Google Scholar

Hunt, M. et al. ARIBA: rapid antimicrobial resistance genotyping directly from sequencing reads. Microb. Genom. 3, e000131 (2017).

PubMed PubMed Central Google Scholar

Bankevich, A. et al. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J. Comput. Biol. 19, 455477 (2012).

Article CAS PubMed PubMed Central Google Scholar

Seemann, T. Prokka: rapid prokaryotic genome annotation. Bioinformatics 30, 20682069 (2014).

Article CAS PubMed Google Scholar

Crawford, R. D. & Snitkin, E. S. cognac: rapid generation of concatenated gene alignments for phylogenetic inference from large, bacterial whole genome sequencing datasets. BMC Bioinformatics 22, 70 (2021).

Article CAS PubMed PubMed Central Google Scholar

Elliott, B., Androga, G. O., Knight, D. R. & Riley, T. V. Clostridium difficile infection: evolution, phylogeny and molecular epidemiology. Infect. Genet. Evol. 49, 111 (2017).

Article PubMed Google Scholar

Li, H. Aligning sequence reads, clone sequences and assembly contigs with BWAMEM. Preprint at https://doi.org/10.48550/arXiv.1303.3997 (2013).

Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 20782079 (2009).

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Preparation of partners to collect samples

Partners registered for participation by contributing isolates or DNA samples to the study. Material was sent to partners according to their registered participation format. This included material for sample collection, metadata registration, DNA extraction and sample shipment to Denmark. Specific protocols were provided, according to the registered participation format and a video for partners sampling isolates was made available via the TWIW web application and YouTube.

Partners were in charge of navigating national guidelines and regulations regarding ethical approval (such as institutional review boards, ethical review boards or other) of their participation in the study. The Danish National Scientific Ethics Committee was consulted with regards to The Technical University of Denmark leading the study, and based on their assessment of the study protocol, the committee concluded that the samples were not human and therefore the study did not require ethical approval. No patient material was transferred with the samples, and no patient identifiers were shared with the project. Only minimal metadata pertaining to the infection and bacterial isolates or their DNA were sampled.

Partners collected samples according to their availability to do so, during 2020. Due to the obstacles presented by the Covid-19 pandemic, ability to participate and carry out sampling was prioritised over sampling during a specific time (original study design and planning targeted sampling during March 2020).

Approximately 60 samples were collected at each individual diagnostic unit over a week. TableS1 lists the participating units with their study ID, country and city of origin, the month of collection, the amount of samples sent, whether the samples received were isolates or DNA and whether the unit made alterations to the sampling protocol. The 60 samples were to be randomly selected at the diagnostic units over the course of a week. Targeting sampling over all weekdays served the purpose of avoiding logistical bias from the internal logistics of the diagnostic unit. Targeting random sampling served the purpose of not targeting specific species or sample source types (i.e. urine samples, blood samples). Partners did prospective random sampling by estimating how many samples to collect every day over the course of a week, in order to collect approximately 60 samples over a week. Due to lack of diagnostic activities related to bacterial infections, a number of units prolonged the sampling time where simply all samples were included in the study, until 60 samples were acquired or sampling was halted due to other reasons.

Coal swabs were used to swab from the plates on which the pathogen was cultured a video illustrating the isolate sampling procedure can be viewed via this link. Parafilm was strapped around the lid of the coal swab for extra sealing. Coal swabs were kept dark, at 4 C or room temperature if 4 C storage was not available. Swabs were stored until shipment was possible for partners.

For partners extracting DNA, material corresponding to the DNA extraction kit and methodology used at DTU was provided to partners (DTU DNA extraction procedure is described under DNA extraction and library preparation). Partners were asked to provide at least 50l of eluted DNA, or at least 80l if the measured concentrations were <6ng/l.

Metadata sheets were provided for all partners, together with labels with printed sample names, unique to each sampling location. Labels were for application on the samples (coal swabs or tubes with DNA) and pertaining metadata sheets. Metadata sheets were for use in a laboratory setting, where metadata could not be recorded electronically from other lab records. The collected metadata was subsequently submitted electronically via Survey Monkey or in excel format for most partners. Few partners sent only the handwritten metadata sheets. The metadata variables are listed in Table1. Under no circumstances were internal patient identifiers (ids) or other references to individuals shared for the project.

Isolates were shipped as UN3373 biological sample category B. All coal swabs were put into absorptive pockets and into a zip lock bag labelled UN3373. The bag was placed in a shipment box labelled UN3373, together with any metadata sheets (these were also submitted electronically for the majority of samples). Shipment was performed by DHL, as Medical Express or ordinary parcel, depending on the options for the departure location. A single parcel was shipped by World Courier, from Mozambique to Denmark.

DNA samples were stored in Eppendorf tubes and sealed again with Parafilm. The tubes were placed in an 84-compartment foldable freezer box and placed in a bubble-wrap envelope. All DNA samples were shipped as ordinary parcels or letters, without cold chain.

Upon arrival in Denmark, samples were logged together with received metadata. Validation of the metadata was performed prior to database submission. Validation of metadata is explained in detail under Technical Validation. Logging entailed entering sample names (as written on the labels provided to partners), registration of unique sample ids, original as well as validated metadata and processing information with regards to culturing and freezing of isolates. Once validated, all information resulting from logging samples and their metadata was submitted to the MySQL database.

Isolates received on coal swabs were cultured on blood agar or chocolate agar, in presence of CO2 if necessary, and sub-cultured until the expected (as submitted by sampling partner) species were (presumedly) isolated (visual recognition by experienced laboratory professionals). In doubt of which species to go forward with, multiple isolates were brought forward for DNA extraction and sequencing and the correct isolate was decided upon after bioinformatic species prediction.

DNA was extracted using Qiagen DNeasy Blood & Tissue kit (Qiagen, Venlo, Netherlands) according to manufacturers protocol. DNA concentrations were measured on Qubit using Invitrogens Qubit dsDNA high-sensitivity (HS) assay kit (Carlsbad, CA, USA). DNA concentrations were diluted to approximately 0.2ng/l for library preparation. Libraries were prepared according to the Illumina NexteraXT DNA Library Prep Reference Guide (Illumina, Inc., San Diego, CA, USA) using standard normalisation.

All samples, except eight, were sequenced on an Illumina NextSeq 500 platform, paired-end sequencing, medium output flowcell (NextSeq500/550 Mid Output Kit v2.5 300 cycles, Cat. nr 20024905). Gram-negative samples were run 96 isolates in parallel, and Gram-positive samples were run 192 isolates in parallel. Few flow cells were run with mixed Gram-negative and Gram-positive samples with approximately 100 samples on a single flow cell. Eight samples were sequenced on an Illumina MiSeq platform, paired-end sequencing, 500 cycles (2251) on a V3 flowcell.

Sequencing data was downloaded from BaseSpace (Illuminas customer cloud platform) and transferred to the Danish National Supercomputer for Life Sciences11, a high-performance computing cluster, where it was both stored and processed, and all downstream analytics took place.

An in-house bioinformatics pipeline, called FoodQCPipeline v. 1.512, was used at default settings to quality assess the raw sequence data, trim the raw reads according to predefined quality thresholds and perform de-novo assembly on the genomes. The quality assessment and trimming of raw sequencing data is further described under Technical Validation. Given the spades option, FoodQCPipeline performs de-novo assembly with SPAdes v. 3.11.013. After running the FoodQCPipeline, both trimmed fastq data and fasta (draft assemblies) are available for downstream analyses. QC summary data was submitted to the MySQL database after genome validation, which is explained in detail under Technical Validation.

KmerFinder14, was used as one of two species prediction programs. KmerFinder assesses species identity by matching k-mers from the query sequence to a kmer-based database of reference strains. KmerFinder was run on the draft assemblies with default settings, the evaluation was done on total query coverage, which is calculated as the number of unique k-mers shared between the query and the template, divided by the number of unique k-mers in the query, with the first hit being accepted if it had more than 80% total query coverage.

The other species prediction software used, was rMLST15. In contrast to KmerFinder, rMLST identifies species based only on ribosomal multi-locus sequence typing, which includes the 53 genes that encode subunits of the bacterial ribosome. rMLST was run on assembled genomes through the open access API at https://pubmlst.org/species-id/species-identification-via-api. The first hit was accepted if it had more than 90% support.

The conclusion of the in silico identified species was based on either species or genus level concordance between the top hits for KmerFinder and rMLST, or an acceptable hit from only one of the two software. The point of using two different species prediction software was to allow for a sensitive assessment of whether the genomes were contaminated (KmerFinder), while complementing with a more robust but less sensitive species prediction software (rMLST). Species that could not be exactly identified are given as NA, if the genome was validated. The genome validation is described under Technical Validation. As with QC summary data, species prediction data was submitted to the MySQL database upon genome validation, and concordance between the KmerFinder and rmlst is given.

In order to identify acquired resistance genes in the validated bacterial genomes, ResFinder version 4.116 was run on the assemblies. All samples were run with the -s other option, meaning that the samples were not run as specific species. ResFinder has the option to run the samples as specific species, in which case a secondary program, PointFinder, is run. This analysis is omitted when running as -s other, and allows for complete cross-comparability of the output data resulting from our in-house ResFinder summary script, which in this case only encompasses acquired resistance genes. The ResFinder summary script produces different overviews of the ResFinder data, with both a class level and a drug level overview of acquired resistance genes, as well as the query coverage, percent identity to reference and position in the assembly of the hit. The ResFinder summary script is submitted as supplementary material, and is available as Supplementary file 1

Genetic distance-based phylogeny was inferred for sequencing runs that passed the technical validation (see below), using Evergreen COMPARE17,18,19 (commit b512e6e). The reference database was the complete bacterial chromosomal genomes from the refseq collection of National Center for Biotechnology Information (NCBI), last fetched in April 2021, homology reduced to 98 percent sequence identity, using kma_index from KMA with the settings for homology reduction -hr 0.769 and-ht 0.769. Consequently, the threshold for accepting a matching reference was also lowered to 98% (76.90% k-mer identity), and the inclusion criterium for consensus sequence completeness reduced to 80%. For displaying the phylogenies on the website, a custom script (Supplementary file 2) was used to select the minimum amount of phylogenetic trees that in totality contained all possible samples.

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Department of Biotechnology, College of Science, Taif University, Taif, 21944, Saudi Arabia

Correspondence: Sarah Albogami, Department of Biotechnology, College of Science, Taif University, P.O. Box 11099, Taif, 21944, Saudi Arabia, Email [emailprotected]

Purpose: According to the World Health Organization, Saudi Arabia ranks seventh worldwide in the number of patients with diabetes mellitus. To our knowledge, no research has addressed the potential of noncoding RNA as a diagnostic and/or management biomarker for patients with type 2 diabetes mellitus (T2DM) living in high-altitude areas. This study aimed to identify molecular biomarkers influencing patients with T2DM living in high-altitude areas by analyzing lncRNA and mRNA. Patients and Methods: RNA sequencing and bioinformatics analyses were used to identify significantly expressed lncRNAs and mRNAs in T2DM and healthy control groups. Coding potential was analyzed using codingnoncoding indices, the coding potential calculator, and PFAM, and the lncRNA function was predicted using Pearsons correlation. Differentially expressed transcripts between the groups were identified, and Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were performed to identify the biological functions of both lncRNAs and mRNAs. Results: We assembled 1766 lncRNAs in the T2DM group, of which 582 were novel. This study identified three lncRNA target genes (KLF2, CREBBP, and REL) and seven mRNAs (PIK3CD, PIK3R5, IL6R, TYK2, ZAP70, LAMTOR4, and SSH2) significantly enriched in important pathways, playing a role in the progression of T2DM. Conclusion: To the best of our knowledge, this comprehensive study is the first to explore the applicability of certain lncRNAs as diagnostic or management biomarkers for T2DM in females in Taif City, Saudi Arabia through the genome-wide identification of lncRNA and mRNA profiling using RNA seq and bioinformatics analysis. Our findings could help in the early diagnosis of T2DM and in designing effective therapeutic targets.

Taif Governorate is located at an altitude of over 1800 m above sea level and has recently experienced an improvement in the quality of life, reflected in the increase in employment opportunities and tourism activities.1 At this altitude, oxygen levels are low and atmospheric pressure is decreased.2 Living at high altitudes is stressful due to susceptibility to hypoxia, an extreme form of altitude sickness.3 People living at high altitudes often have a strong, long-evolved response to hypoxic conditions; this is evident in indigenous populations that have adapted several molecular, cellular, and systemic responses to tolerate hypoxia at high altitudes.4 Various physiological responses, including increased heart and respiratory rates and red blood cell production, exist at the systemic level.5 Increased red blood cell mass and hemoglobin content in the blood are thought to be induced by gene regulation.6 Metabolic studies have found a shift in expression patterns that can provide an increased energy supply for the cells in the absence of aerobiosis (and exhibit less demand for ATP).7 This evidence and other physiological responses constitute examples of altitude adaptation.8 Usually, adaptations are considered genetic alterations that cause a particular physiological trait to develop, a phenomenon known as adaptive plasticity.9 However, not everyone responds in this way. Some individuals reportedly develop adaptive responses, but others, particularly those with chronic diseases like diabetes, experience complications due to living in such locations.8 There has been a significant increase in the prevalence of diabetes in high-altitude populations because of urbanization and rapid changes in diets and lifestyles.1013 The global and fast expanding diabetes epidemic is likely to become the primary cause of mortality and disability in the future due to the ageing of the population and lifestyle shifts.14 The International Diabetes Federation estimates that 450 million people aged 18 and above suffer from diabetes, and this number is expected to increase to approximately 690 million by 2045.15 Notably, the World Health Organization (WHO) has identified Saudi Arabia as having the second-highest incidence of diabetes in the Middle East and the seventh-highest worldwide. Approximately 10 million people in the country have diabetes or are prediabetic.16

Type 2 diabetes mellitus (T2DM) is the most prevalent form of diabetes17 and tends to result from genetic, environmental, immunological, and lifestyle factors.18,19 T2DM is a progressive, chronic disorder whose symptoms advance over time. T2DM is characterized by low insulin sensitivity and defective insulin secretion. High blood glucose levels may also increase the risk of retinopathy, nephropathy, neuropathy, and cardiomyopathy.17 Early stages of the illness can go undiagnosed, causing symptoms or complications that are not detected until later stages.20 Approximately half of the people living with diabetes are estimated to be undiagnosed.15 If individuals can be accurately diagnosed early in the asymptomatic phase of the disease, they may benefit from early interventions, limiting the development of the disease and helping them manage their symptoms more effectively. Thus, there has been an increasing focus on finding reliable, responsive, and easily available diagnostics for diabetes. Family history is a significant risk factor for developing this disease; T2DM has a 4- to 6-fold elevated risk among relatives.21 Therefore, collecting the full family history of suspected patients is important. Furthermore, as many changes in insulin-responsive tissues are believed to underlie obesity, insulin resistance, and T2DM, it has become increasingly apparent that genetic and epigenetic markers in the blood can also play crucial roles in their respective pathologies.2224 Therefore, new predictive biomarkers that can help diagnose diabetes at an early stage are needed, which may also aid in identifying new therapeutic targets.

Currently, genetic and genomic studies are being conducted for disease prevention and treatment.25 New genetic knowledge must be spread across the wide medical field, and the technical skills needed for disease genetic screening, diagnosis, and prevention should not be confined to research or specialist practice.26 Understanding the genetic basis of diseases requires an understanding of variation across the whole genome to determine overall influence. The current focus of clinical genomics is mainly on protein-coding genes; however, the noncoding genome is far larger than the protein-coding equivalent.27 The noncoding genome encompasses transcriptional, regulatory, and structural information, which needs to be integrated into genome annotations to optimize the use of genomic information in the healthcare system.28 According to genome-wide association studies, most diabetes-related genetic variations do not lie in protein-coding regions, making it difficult to identify functional variants.29 This highlights the importance of identifying and characterizing early noncoding RNA (ncRNA) biomarkers for T2DM management. Over the years, several classes of ncRNAs have been discovered.30 Almost all of these ncRNAs are commonly categorized as small ncRNAs (<200 nucleotides), consisting of microRNAs (miRNAs) and circular RNAs (circRNAs), and large ncRNAs, such as long ncRNAs (lncRNAs).3133 lncRNA consists of transcripts with a size range from 200 nucleotides to 100 kilobase pair (kbp).34,35 lncRNAs are transcribed from either strand and classified as sense exonic lncRNAs, antisense exonic lncRNAs, intronic sense and antisense lncRNAs, and 3- and 5-UTR-associated RNAs based on their relationship with the neighboring protein-coding genes.36 lncRNAs generate a complex regulatory network by establishing links with transcription factors, transcriptional co-activators, and repressors, which can influence several aspects of transcription.37 Investigations on the effect of lncRNAs under different clinical and physiological conditions have been conducted.3840

lncRNAs are implicated in the regulation of numerous biological reactions associated with health and disease.41 Research has demonstrated the importance of lncRNAs to inflammation,42 and the connection between different mediators of inflammation and T2DM has been determined.43,44 A cross-sectional cohort study showed that the serum neuregulin-4 level is substantially elevated in patients with T2DM compared to that in healthy controls.45 This suggests that neuregulin-4 level may serve as a biomarker for T2DM because euregulin-4 has potential anti-inflammatory properties. Furthermore, several other markers have been studied in T2DM. For example, T2DM complications, such as diabetic renal disease, could be diagnosed based on the uric acid to HDL ratio (UHR) because this ratio is connected to T2DM and inflammation.46 In T2DM, the UHR ratio is a robust predictor of metabolic syndrome.47 Another study found that uncontrolled hypertension is associated with an increased UHR ratio, which is linked to inflammation48 and fatty liver disease.49 Although inflammation plays a vital role in the development of T2DM and its related complications, hemogram parameters, including mean platelet volume, were regarded as a new inflammatory biomarker in obese patients with T2DM.50

As mentioned above, lnc-RNA is linked to inflammatory conditions and T2DM, as well as its associated conditions such as diabetic kidney disease. Additionally, hypertension, obesity, and fatty liver disease are associated with inflammation, so investigating lnc-RNA in diabetes is rational. However, no research has, to the best of our knowledge, expressly investigated the possible function of certain lncRNAs as diagnostic or management biomarkers for T2DM. In this study, we performed transcriptomic analyses to identify molecular biomarkers that influence patients with T2DM who live in high-altitude areas by analyzing noncoding regions (lncRNA) and protein-coding regions (mRNA) of the genome.

This study was conducted in accordance with the Declaration of Helsinki. The study procedure was approved by the Taif University Research Ethical Committee, Taif, Saudi Arabia (NO.: 43220). The aim and nature of the methods to be used in this study were discussed with the participants, and written informed consent was obtained from each participant. Two groups of participants living in the Taif region were enrolledpatients diagnosed with T2DM (five women; age: 2756 years) and a healthy control group (four women; age: 2957 years)between January and March 2022. T2DM diagnoses were based on the 1999 WHO diabetes diagnostic criteria.51 None of the subjects had received hypoglycemic medication. Exclusion criteria for participants included a history of type 1 diabetes, pregnancy, cancer, and chronic or acute diabetic complications.

Fresh blood (5 mL) was collected from each participant. Thereafter, 1.5 mL of the collected blood sample (with 40007000 leukocytes/L) was processed immediately for total RNA extraction using a QIAamp RNA Blood Mini Kit (Qiagen, Hilden, Germany), following the manufacturers protocol. The integrity of the RNA was evaluated with an Agilent Bioanalyzer 2100 system (Agilent Technologies, Santa Clara, CA, USA), and its purity was determined using agarose gel electrophoresis and a NanoDrop2000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA). RNA samples with an RNA integrity number 8.0 were processed further.

Ribosomal RNA (rRNA) was removed from total RNA using an rRNA removal kit (Illumina, San Diego, CA, USA), following the manufacturers protocol. A KAPA Stranded RNA-Seq Library Preparation Kit (Illumina) was used to complete the RNA sequencing library following the manufacturers protocol. Qubit (Thermo Fisher Scientific) and real-time PCR were used to quantify the constructed library, and a bioanalyzer was used to identify the size distribution. Quantified libraries were sequenced on an Illumina HiSeq 2500 platform (Illumina). The annotation data for the reference genome and gene models were acquired directly from the Ensembl genome browser 106 (https://asia.ensembl.org/index.html). Using hierarchical indexing for spliced alignment of transcripts (HISAT 2; version 2.0.4), clean reads were mapped to the Homo sapiens genome (genome assembly: GRCh38.p13).52 Figure 1 illustrates the workflow of this study.

Figure 1 Workflow of lncRNA and mRNAs analysis for patients with T2DM versus healthy controls.

Abbreviations: CNCI, coding-noncoding-index; CPC, coding potential calculator; PFAM, Pfam Scan database; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes.

StringTie software (version 3.3.0) was utilized to assemble each samples mapped reads53 and run using the library-type option; all other parameters were left at their default values. Transcripts from all samples were merged using 2/cuffmerge. To find new protein-coding transcripts, the transcripts were examined for signs of protein-coding possibility and conserved sequences. Such transcripts were filtered out, and lncRNA candidates comprised those without coding potential.

The coding-noncoding index (CNCI) software tool (version 2) was utilized to profile and differentiate protein-coding and noncoding sequences.54 The coding potential calculator algorithm (CPC) was used to assess the quantity and quality of the open reading frame in a transcript and search the sequences against a database of known protein sequences to distinguish between coding and noncoding transcripts. In our study, we gathered functional protein information using the UniProt Knowledgebase (https://www.uniprot.org /UniProtKB) and set the e-value to 1e10. The Pfam Scan tool (version 1.3) was used to determine the presence of any known protein family domains listed in the Pfam database (release 27; Pfam A and Pfam B).55 Transcripts with a Pfam match were excluded in the following step.

A correlation analysis was performed using Pearsons correlation to assess the possibility of co-expression between lncRNAs and mRNAs. An interaction between a lncRNA and an mRNA was considered significant when Pearsons correlation value was |0.70| and the P-value was <0.05. Two analyses were conducted on the total correlation matrix to determine and categorize the interactions and potential activities of lncRNAs (cis and trans) regarding their target gene. Cis-regulated genes are protein-coding genes co-expressed with a dysregulated lncRNA and located within 30 kb upstream or downstream of the same gene. Some lncRNAs trans-regulate the central transcription factors to engage specific cellular processes.

Ballgown R package (version 2.4.2) was used to identify transcripts differentially expressed between the groups using the data from StringTie.56 Among any two groups, transcripts with a P-value <0.05 were classified as differentially expressed transcripts.

To verify the functions of the 84 mRNA transcripts that exhibit differential expression in T2DM, the Type 2 Diabetes Knowledge Portal (https://t2d.hugeamp.org) was utilized. This portal contains a collection of genes that have been linked to T2DM and other glycemic traits, including HOMA-B, HbA1c, and fasting insulin adj BMI through various genome-wide association studies (GWAS).

GOseq R package (version 1.48.0) was used to implement Gene Ontology [GO; annotates genes to biological processes (BPs), molecular functions (MFs), and cellular components (CCs)] enrichment analysis of the differentially expressed genes (DEGs) or lncRNA target genes. GO terms with a P-value < 0.05 were deemed significantly enriched among DEGs.57 The Kyoto Encyclopedia of Genes and Genomes (KEGG) database (http://www.genome.jp/kegg/) was used to annotate genes to pathways.58

GraphPad Prism (version 10.0.0) was used for statistical analyses. The results are presented as mean standard error. For all data, P < 0.05 indicated statistical significance. KOBAS R package (Version 3.0) was used to examine the statistical enrichment of DEGs or lncRNA target genes.59

Several metrics, including the total number of reads, number of reads, error rate, number of reads mapped to the genome, and number of spliced and non-spliced reads, were used to evaluate the quality of the transcriptome data. The quality parameter findings between the groups are displayed in Table 1.

Table 1 Quality Parameter Information for Transcriptome Data for Both Patients with T2DM and Healthy Controls

To demonstrate the differences in lncRNA profiles between patients with T2DM and healthy controls and to determine diabetes-related lncRNAs, RNA-seq was performed. The CNCI, CPC, and Pfam Scan database (PFAM) were used to exclude protein-coding transcripts and predict lncRNAs. Significantly expressed lncRNAs were identified using the overlapping results of these three approaches. Finally, 1766 lncRNAs were assembled using the three software, of which 582 were novel (Figure 2A). The lncRNAs were categorized based on their genomic location to simplify functional interpretation and undertake extensive analysis; this revealed that 637 (45.57%) of the lncRNAs were sense overlapping, 279 (19.96%) were long intergenic noncoding RNAs (lincRNA), 211 (15.09%) were sense intronic, 208 (14.88%) were antisense, and 63 (4.51%) were others (Figure 2B). The findings indicated that sense overlapping lncRNAs were the most abundant lncRNAs in the T2DM group.

Figure 2 lncRNA transcriptome analysis in the T2DM group compared with the healthy control group. (A) Venn diagram representing predicted lncRNA findings using CNCI, CPC, and PFAM. The sum of the numbers in each large circle reflects the overall number of noncoding transcripts, and the portions of the circle that overlap represent the noncoding transcripts identified by all three methods. (B) A pie chart of lncRNA classificationsense overlapping, lincRNA (long intergenic noncoding RNA), sense intronic, antisense, and other distributions.

Abbreviations: CNCI, coding-noncoding-index; CPC, coding potential calculator; PFAM, Pfam Scan database; T2DM, type 2 diabetes.

A screen was performed for lncRNAs or mRNAs with significant expression (the default threshold of FPKM score was selected as 1), and the results were analyzed to generate Venn diagrams. The co-expressed lncRNAs and mRNAs were displayed in a Venn diagram separately (Figures 3A and B) to determine the total number of lncRNAs and mRNAs specifically expressed within and between the groups. The co-expression of lncRNAs and mRNAs between T2DM and healthy control groups provides insights into the influence of T2DM on the co-expression pattern. Figure 3A shows that 148 lncRNAs were uniquely expressed in patients with T2DM, 118 in healthy controls, and 191 in both groups. Furthermore, 467 mRNAs were exclusively expressed in patients with T2DM, 654 in healthy controls, and 658 in both groups (Figure 3B).

Figure 3 Venn Diagram of uniquely expressed and co-expressed lncRNAs and mRNAs in the T2DM and healthy control groups. Expression pattern of (A) lncRNAs and (B) mRNAs.

Abbreviation: lncRNA, long noncoding RNA.

The relative expression of lncRNAs and mRNAs was analyzed using high-throughput sequencing to explore possible correlations between alterations in lncRNAs and mRNAs and the development of T2DM. The results identified 582 lncRNAs and 2131 mRNAs in the T2DM group. We found that in the T2DM group, 22 lncRNA transcripts were differentially expressed, of which 10 (1.72%) were upregulated, 12 (2.06%) were downregulated, and 560 showed no difference (96.22%). Furthermore, 84 mRNA transcripts were significantly differentially expressed, of which 27 (1.27%) were upregulated, 57 (2.67) were downregulated, and 2048 showed no difference (96.06%). Transcripts were categorized as differentially expressed when the fold change in expression was more than 2.0 and P 0.05. Volcano plots and pie charts were used to compare the expression profiles of lncRNAs and mRNAs between the T2DM and healthy control groups (Figure 4AD).

Figure 4 lncRNAs and mRNA expression profiles in T2DM and healthy control groups. Volcano plots clustering analysis of (A) lncRNAs and (B) mRNA. Pie charts represent the percentage of differentially expressed (C) lncRNAs and (D) mRNA. P < 0.05 was considered significant; expression changes are shown in the T2DM group compared with those in the healthy control group. Magenta represents genes whose expression has increased by >2 fold, while green represents genes whose expression has decreased by >2 fold.

Abbreviations: T2DM, type 2 diabetes; lncRNA, long noncoding RNA; mRNA, messenger RNA.

We demonstrated that the T2DM group had altered expression of lncRNAs and mRNAs compared with that of the healthy control group. The top 10 (5 upregulated and 5 downregulated) differentially expressed lncRNAs and mRNAs are shown in Table 2 and Table 3. Under varied experimental settings, cluster analysis was performed to identify genes with comparable expression patterns. A hierarchical clustering analysis was performed to identify the expression patterns of differentially expressed lncRNAs (22) and mRNA transcripts (84) in study groups by considering the FPKM. The clustering information from several experiments indicated that genes with the same gene expression patterns might have comparable roles or be involved in the same biological processes (Figure 5A and B). These findings suggest that differentially expressed lncRNAs and mRNAs are associated with T2DM development.

Table 2 Top 10 Differentially Expressed lncRNAs in the T2DM and Healthy Control Groups

Table 3 Top 10 Differentially Expressed mRNAs in the T2DM and Healthy Control Groups

Figure 5 Hierarchical clustering analysis of significant differential expression profiles between T2DM and healthy groups. (A) lncRNAs and (B) mRNAs. Each row is a transcript ID, and each column represents a sample. Upregulation is represented by magenta, whereas downregulation is represented by green.

Abbreviations: DM, diabetes mellitus; T2DM, type 2 diabetes; H, healthy; lncRNA, long noncoding RNA; mRNA, messenger RNA.

The 84 mRNA transcripts that exhibited substantial differential expression were further cross-referenced with T2DM GWAS to determine their potential relevance to the genetic underpinnings of the disease. These genes wereNUDT22, ATM, IL6R, FMNL1, TANGO2, ACRBP, PTPRJ, SMCHD1,andNUCB2; 3 genes related to HOMA-B-related loci, RNF19B,TNRC18, and KXD; 10 genes related to HbA1c-related loci,ZSWIM1, AKAP13, STK10, ZAP70, LAMTOR4, METRNL, CTAGE5, USP34, MAPKAPK5, and APOBEC3A; and 3 related to fasting insulin adj BMI-related loci, PIK3R5, SETX, and TAF13 (see Table 4).

Correlation analysis was performed to investigate the possibility of co-expression between lncRNAs and mRNAs and to predict the lncRNA target genes. In predicting cis lncRNAsmRNA, no differentially expressed lncRNAs could be linked to nearby genes. However, several lncRNAs were identified to regulate their target protein-coding genes in a trans manner. The top 10 differentially expressed lncRNAs identified in this study significantly correlated to 64 nearby genes, as listed in Table 5, with a Pearsons correlation value |0.70| and P-value <0. 05.

Table 5 Prediction of Top Differentially Expressed lncRNA-Target mRNA Genes via lncRNAmRNA Co-Expression Trans-Interaction in the T2DM Group Compared with the Healthy Control Group

The lncRNA TCONS_00098523 was linked to 11 genes, namely, RIOK3, ZEB1, PPM1B, ZNF621, LRRFIP1, TCF25, ZNF383, ZNF844, ZNF611, SFPQ, and SIN3B. The lncRNA TCONS_00098587 was linked to six genes, namely, TCF25, ZEB1, LRRFIP1, PPM1B, ZNF844, and TRIM22. The lncRNA TCONS_00060460 was linked to six genes, namely, ZNF621, ZNF383, GTF2H2, RIOK3, SFPQ, and PPM1B. The lncRNA TCONS_00007325 was linked to TCF25. The lncRNA TCONS_00098489 was linked to 13 genes, namely, PPM1B, ZNF844, ZNF383, LRRFIP1, ZEB1, RIOK3, SIN3B, ZNF417, UBE2I, ZNF611, SFPQ, MED6, and TRIM22. The lncRNA TCONS_00059776 was linked to eight genes, namely, KLF2, AKNA, ZNF580, CREBBP, ZNF708, ZNF791, REL, and ZNF841. The lncRNA TCONS_00004761 was linked to ZNF414. The lncRNA TCONS_00098679 was linked to two genes, namely, ZNF101 and ZBTB25. The lncRNA TCONS_00060436 was linked to eight genes, namely, ZNF580, REL, CREBBP, ZNF708, ZNF841, KLF2, AKNA, and ZNF791. The lncRNA TCONS_00029866 was linked to eight genes, namely, AKNA, ZNF708, CREBBP, REL, ZNF580, ZNF791, KLF2, and ZNF841.

GO terms were predicted to determine the function and relationship of differentially expressed lncRNA target genes and mRNAs in the T2DM and healthy groups. The most significant GO analysis results of lncRNA targets and mRNAs are shown in Figure 6. For lncRNA target genes, the enriched MF terms were DNA binding transcription factor activity, DNA binding, and ion binding (Figure 6A). Enriched CC terms were intracellular, organelle, and nucleoplasm (Figure 6B). The most significantly enriched BP terms were cellular nitrogen compound metabolic and biosynthetic processes (Figure 6C). The most significant GO terms of the mRNAs were enriched in MFs (Figure 6D).

Figure 6 Gene Ontology enrichment analysis of differentially expressed lncRNA target genes and mRNAs in the T2DM and healthy groups. (A) Molecular functions (MF), (B) cellular components (CC), and (C) biological processes (BP) of lncRNA target genes. (D) MF, (E) CC, and (F) BP of mRNA.

Abbreviations: T2DM, type 2 diabetes; lncRNA, long noncoding RNA; mRNA, messenger RNA.

For mRNA, the most significantly enriched MF term was kinase activity. The other top terms, which were not significant, were ion binding, mRNA binding, and helicase activity (Figure 6D). No significantly enriched CC terms were found, but the gene networks appeared to be involved with the intracellular, lysosome, and organelle terms as the top three terms (Figure 6E). No significantly enriched BP terms were found, but the top three terms were cellular protein modification process, cell motility, and response to stress (Figure 6F).

Key pathways for lncRNA target genes and mRNA were analyzed through KEGG enrichment. lncRNA target genes were enriched in nine pathways but not significantly (Figure 7A). Notably, we found three lncRNA target genes enriched in six pathways. UBE2I was enriched in the NF-kappa B signaling pathway, ubiquitin-mediated proteolysis, and RNA transport. GTF2H2 was enriched in basal transcription factors and nucleotide excision repair. PPM1B was enriched only in the MAPK signaling pathway.

Figure 7 KEGG pathway analysis of differentially expressed lncRNA targets and mRNAs in T2DM and healthy groups. (A) Upregulated and (B) downregulated KEGG pathways of lncRNA target genes. (C) Upregulated and (D) downregulated KEGG pathways of mRNA.

Abbreviations: lncRNA, long noncoding RNA; KEGG, Kyoto Encyclopedia of Genes and Genomes; mRNA, messenger RNA.

Twenty-seven pathways were downregulated, of which only two were significantly downregulated (Figure 7B shows the top 20 pathways). The significantly enriched pathways identified were the FoxO signaling (P = 0.00075) and viral carcinogenesis pathways (P = 0.00172). Based on the results, the affected lncRNA target genes in the FoxO signaling pathway were KLF2 and CREBBP, and those in the viral carcinogenesis pathway were REL and CREBBP. Notably, we found that CREBBP was enriched in the most relevant downregulated pathways, including notch, TGF-beta, glucagon, HIF-1, wnt, and Jak-STAT signaling pathways; long-term potentiation; adherens junction; and cell cycle.

Upregulated mRNA transcripts were enriched in 22 pathways but not significantly (Figure 7C shows the top 20 pathways). The downregulated mRNA transcripts were enriched in 98 pathways, of which 81 were significantly downregulated (Figure 7D shows the top 20 pathways). The related pathways include the Ras signaling pathway (P = 0.0000053), Jak-STAT signaling pathway (P = 0.000028), EGFR tyrosine kinase inhibitor resistance pathway (P = 0.000105), HIF-1 signaling pathway (P = 0.000209), T cell receptor signaling pathway (P = 0.000221), cholinergic synapse (P = 0.000259), natural killer cell-mediated cytotoxicity (P = 0.000454), PI3K-AKT signaling pathway (P = 0.000529), mTOR signaling pathway (P = 0.000661), aldosterone-regulated sodium reabsorption (P = 0.000910), axon guidance (P = 0.000966), chemokine signaling pathway (P = 0.001148), carbohydrate digestion and absorption (P = 0.001245), and type II diabetes mellitus (P = 0.00135).

Figure 8 shows the most likely KEGG pathways linked to downregulated mRNA transcripts involved in T2DM .

Figure 8 Enriched mRNA transcript genes in the KEGG pathways most likely involved in T2DM. The graph was generated using Origin Pro 2023 (OriginLab, Northampton, MA, USA).

Abbreviations: T2DM, type 2 diabetes; KEGG, Kyoto Encyclopedia of Genes and Genomes; mRNA, messenger RNA.

According to the latest data from WHO, Saudi Arabia is ranked seventh worldwide in the number of individuals diagnosed with diabetes mellitus.60 In addition, over the past 3 years, Saudi Arabia has recorded an increase in diabetes mellitus cases, roughly equivalent to a 10-fold increase.61 The pathogenesis of T2DM is complicated and consists of multiple factors that operate in concert to produce this condition.62 Genetic, environmental, immunological, and lifestyle factors typically contribute to developing T2DM.18,19 Recent research has demonstrated the importance of lncRNA in T2DM.63 The present study utilized genome analysis using RNA sequencing to investigate the expression of lncRNA and mRNA transcripts of female patients with T2DM compared with those of healthy females in Taif City, Saudi Arabia. To our knowledge, this study is the first to be conducted in a high-altitude area, such as Taif City, to evaluate the lncRNA and mRNA expression profiles in females with T2DM to gain a better understanding of the molecular mechanisms behind the etiology of T2DM at high altitudes. In the present study, we identified 1766 lncRNAs in the T2DM group, of which 582 were novel. Additionally, we found that compared with those in the healthy control group, 22 lncRNA transcripts (10 upregulated and 12 downregulated) and 84 mRNA transcripts (27 upregulated and 57 downregulated) were differentially expressed in patients with T2DM, and most of these transcripts were novel. Hierarchical clustering analysis of expression profiles showed significant differences between the T2DM and healthy control groups. The data indicated that this analysis may lead to identifying important target genes implicated in the development of T2DM.

Based on whole-genome sequencing, lncRNA target genes in patients with diabetes were downregulated in two pathways: Forkhead box O (FoxO) signaling and viral carcinogenesis. KLF2 and CREBBP genes were most likely affected in the FoxO signaling pathway, while in the viral carcinogenesis pathway, REL and CREBBP were the most likely affected genes. FOXO is a family of transcription factors, and the FoxO signaling pathway controls many cellular physiological processes, such as glucose metabolism, cell death, cell-cycle regulation, DNA damage repair, resistance to oxidative stress, and adaption to stress stimuli.6466 Post-translational modification strictly controls the activity of FOXOs. Patients with diabetes are at an elevated risk of acquiring various severe health complications. Evidence indicates that diabetes-induced activation of FOXO1 is linked to several diabetic problems.67 In vivo model knockdown of FOXO1 can help eliminate retinal microvascular endothelial cells that occur in the initial phase of diabetic retinopathy.68 In our study, KLF2 (encoding a zinc-finger transcription factor) was the most likely affected gene in the FoxO signaling pathway. According to reports, KLF2 is crucial in preserving endothelial function.69 Cell-based investigations have demonstrated that KLF2 directly regulates important endothelial genes, including endothelial nitric oxide synthase (eNOS), thrombomodulin (THBD),70,71 and genes that encode proteins with anti-thrombotic and anti-inflammatory properties.72 KLF2 is inhibited by 30 mM glucose exposure in human umbilical vein endothelial cells.73 KLF2 inhibition by high glucose is a potential diabetic vasculopathy mechanism.74 Furthermore, KLF2 is a powerful angiogenesis inhibitor; as shown in an animal angiogenesis model, the overexpression of KLF2 suppresses vascular endothelial growth factor A (VEGFA).75 In addition, KLF2 can reduce HIF1- production and affect its function.76 HIF1 is a key transcription factor that regulates metabolic adaptation to hypoxia.77 Moreover, HIF1 regulates the promotion of glycolysis and inhibition of mitochondrial respiration, thereby decreasing oxygen uptake and inhibiting the generation of reactive oxygen species.78 Under intermittent hypoxic conditions, HIF1 increases the expression of pro-inflammatory and pro-angiogenic genes to induce angiogenesis.79 In endothelial cells, the expression of KLF2 was increased under hypoxia, whereas KLF2 knockdown boosted HIF1- expression.80 The results of the present study show that CREBBP most likely plays a role in downregulating the FoxO viral carcinogenesis signaling pathway. CREBBP, a lysine acetyl transferase involved in many signaling pathways, is implicated in controlling the accessibility of chromatin and transcription.81 Based on our study, CREBBP downregulates the FoxO signaling pathway to reduce diabetes complications. We also found that the viral carcinogenesis pathway is significantly downregulated.82 Patients with T2DM are associated with a higher chance of contracting viral infections, as was recently demonstrated during the COVID-19 pandemic.82

We found that the mRNAs significantly downregulated 81 pathways. The most relevant pathways included the Ras, Jak-STAT, PI3K-AKT, mTOR, HIF-1, T cell receptor, and chemokine signaling pathways; cholinergic synapse; natural killer cell-mediated cytotoxicity; aldosterone-regulated sodium reabsorption; axon guidance; carbohydrate digestion and absorption; type II diabetes mellitus pathway; and EGFR tyrosine kinase inhibitor resistance pathway.

The Ras signaling pathway is an essential growth regulator in all eukaryotic organisms.83 The reninangiotensin system (RAS) is closely associated with the pathogenesis of insulin resistance/diabetes,84 and RAS inhibition improves insulin sensitivity in humans.85

In our study, PIK3CD and PIK3R5 were enriched in all relevant significantly downregulated pathways. Consistent with our findings, PIK3CD expression was significantly reduced in T2DM in a previous study.86 As insulin resistance is frequently identified as the most important contributor to the development of T2DM, insulin resistance might be treated by targeting the PIK3CD gene.86 Furthermore, by analyzing the microRNAmRNA expression patterns and functional network of the submandibular gland in T2DM mice, PIK3CD was surmised to play essential roles in developing diabetes-mediated hyposalivation.87 PIK3CB and PIK3CA are among the genes predicted to be predominantly ordered, according to a comprehensive analysis of the functions of highly disordered proteins in T2DM.88 These findings elucidated the primary biological functions of these proteins as well as the functional significance of some of their sites, which often play a part in binding between proteins and possess sites for diverse post-translational modifications.88 A previous study used high-throughput sequencing to investigate the lncRNA and circular RNA network in T2DM. A proteinprotein interaction network was built to identify several hub mRNAs, including PIK3R5, enriched in key pathways such as the mTOR signaling and apoptosis pathways.89 In a previous in silico study, bioinformatics analysis was performed to comprehend differential gene expression and patterns and the enriched pathways for obesity and T2DM. Several overexpressed genes that are direct components of the T cell signaling pathway, including PIK3R5, were identified.90

In the current study, the IL6R gene was enriched in four relevant pathways, including the Jak-STAT, HIF-1, and PI3K-Akt signaling pathways and EGFR tyrosine kinase inhibitor resistance. Serum levels of the IL6/IL6R are considerably elevated in T2DM;91 IL6/IL6R has important implications for T2DM. IL6R suppresses pancreatic beta-cell viability and decreases apoptosis-related gene expression to inhibit cell apoptosis by controlling the JAK/STAT signaling pathway via miR22.92 IL-6 primarily activates the JAK/STAT signaling pathway but also activates ERK1/2 and PI3K.93 Modifications in JAK/STAT signaling are linked to numerous complications of T2DM.94 In the present study, TYK2 was enriched in the Jak-STAT signaling pathway and osteoclast differentiation. Tyk2 is a member of the Janus family kinases (Jaks), which are activated by cytokines, including IL10, IL12, and IL18, and perform important functions in signal transduction.95 In mice with gene-targeted knockout of Tyk2 kinase, the function of Tyk2 in the progression of obesity and diabetes was examined. As these animals aged, they developed obesity and T2DM, suggesting that Tyk2 kinase plays a vital role in the progression of these disorders.96 Furthermore, a study investigated the association of TYK2 gene polymorphisms with T1DM and T2DM, focusing on the correlation with flu-like syndrome. The results revealed that the variant of the TYK2 promoter has been linked with an increased risk for diabetes, making it an attractive candidate for virus-induced diabetes.97

In the current study, ZAP70 was enriched in the Ras and T cell receptor signaling pathways and natural killer cell-mediated cytotoxicity. ZAP70 is a Syk family kinase that plays a key role in triggering the T cell receptor signaling pathway and cell migration and death.98 Utilizing gene expression profiles from the Gene Expression Omnibus and a weighted gene correlation network, a comprehensive study was conducted to identify key genes implicated in the development of T2DM-associated cardiovascular disease; the researchers identified 19 genes, including ZAP70, involved in atherosclerosis.99 Earlier work combined miRNA and mRNA datasets to identify significant sepsis-related miRNA and mRNA pairings.

In the present study, the LAMTOR4 gene was enriched in the mTOR signaling pathway. mTOR signaling controls development, growth, motility, and protein production, in addition to various cellular and metabolic functions.100 A study showed that mTOR dysregulation has a significant pathology in the progression of diseases, including T2DM.101 Earlier research emphasized the crucial role of LAMTOR4 as a regulatory element.102 LAMTOR1 and LAMTOR4 are important in the mTOR signaling pathway. To the best of our knowledge, information on the role of this gene in the development of T2DM at the molecular level is unknown.

In the current study, the SSH2 gene was enriched in axon guidance pathways. These pathways control axon guidance, synaptic development, progenitor movement, and cell migration.103 Axon guidance pathways are stimulated in patients with T2DM.104,105 The profiles and networks of miRNAmRNA expression in the submandibular gland tissues of an animal model of spontaneous T2DM were described in a previous study, which demonstrated that the 11 differentially expressed microRNAs were related to 820 mRNAs, indicating a link between the miRNAs and mRNAs of their target genes. From these, a network of 11 differentially expressed microRNAs and their target genes was built. According to the network, every miRNA was associated with many mRNAs, and every mRNA was associated with different miRNAs. The mRNA SSH2, for instance, interacts with three miRNAs.87 Studies to uncover the correlations between diabetes and sensorineural hearing loss identified two new genes, NOX1 and SSH2.106

To highlight the origin-specific targets, our results were compared to previously published transcriptomes of T2DM and healthy neutrophils of people of different ethnicity, including 9 Caucasians, 1 Hispanic, and 11 African-Americans, In their investigation, the researchers found a considerable difference in gene expression between individuals with T2DM and those with healthy neutrophils.107 Specifically, they observed a reduction in gene expression associated with inflammation and lipid metabolism in T2DM, as evidenced by the downregulation of SLC9A4, NECTIN2, and PLPP3. Furthermore, the top KEGG pathways included sphingolipid metabolism, glycerophospholipid metabolism, ether lipid metabolism, Fc gamma R-mediated phagocytosis, and phospholipase D signaling pathway. The top GO terms in the biological processes category included ammonium ion metabolic process and surfactant homeostasis; those associated with molecular functions included sphingosine-1-phosphate-phosphatase activity; and those involved in cellular components included plasma membrane and integral component of plasma membrane.107

There are some limitations to this study. The small number of samples used for RNA sequencing might have influenced the precision of the results; therefore, it is essential to increase the sample size to validate the results. The results acquired are preliminary and must be verified.

To the best of our knowledge, this comprehensive study is the first to explore the applicability of certain lncRNAs as diagnostic or management biomarkers for T2DM in females in Taif City, Saudi Arabia through the genome-wide identification of lncRNA and mRNA profiling using RNA seq and bioinformatics analysis. This study identified three lncRNA target genes, namely KLF2, CREBBP, and REL. Seven mRNAs, namely PIK3CD, PIK3R5, IL6R, TYK2, ZAP70, LAMTOR4, and SSH2, were significantly enriched in important pathways and perhaps play an important role in the progression of T2DM. Our findings could help in the early diagnosis of T2DM and in designing effective therapeutic targets.

The study was conducted in accordance with the Declaration of Helsinki and approved by the Taif University Research Ethical Committee, Taif, Saudi Arabia (protocol NO.: 43-220; date of approval 23-01-2022).

Informed consent was obtained from all subjects.

The author would like to acknowledge the Deanship of Scientific Research at Taif University for their support of this work.

This research received no external funding.

The author declares no conflicts of interest in this work.

1. Al Saeed MS, Awad NS, El-Tarras AE. Prevalence of some genetic polymorphisms among cardiovascular patients residing at high altitude and sea level. Int J Curr Microbiol App Sci. 2015;4(11):443449.

2. Fadl MA, Al-Yasi HM, Alsherif EA. Impact of elevation and slope aspect on floristic composition in wadi Elkor, Sarawat Mountain. Saudi J Biol Sci. 2021;11(1):110.

3. Ulloa NA, Cook J. Altitude Induced Pulmonary Hypertension. StatPearls; 2020.

4. Ely BR, Lovering AT, Horowitz M, et al. Heat acclimation and cross tolerance to hypoxia: bridging the gap between cellular and systemic responses. Temperature. 2014;1(2):107114. doi:10.4161/temp.29800

5. Tso E. High-altitude illness. Emerg Med Clin North Am. 1992;10(2):231247. doi:10.1016/S0733-8627(20)30711-2

6. Mairburl H. Red blood cells in sports: effects of exercise and training on oxygen supply by red blood cells. Front Physiol. 2013;4:332. doi:10.3389/fphys.2013.00332

7. Clerici C, Plans C. Gene regulation in the adaptive process to hypoxia in lung epithelial cells. Am J Physiol Cell Physiol. 2009;296(3):L267L274. doi:10.1152/ajplung.90528.2008

8. Hackett PH, Roach RC. High-Altitude Illness. N Engl J Med. 2001;345(2):107114. doi:10.1056/NEJM200107123450206

9. McGrath C. Highlight: The Epigenetics of Life at 12,000 Ft. Oxford University Press; 2021.

10. Singh D, Bhattarai M. High prevalence of diabetes and impaired fasting glycaemia in urban Nepal. Diab Med. 2003;20(2):170171. doi:10.1046/j.1464-5491.2003.00829_4.x

11. Sasaki H, Kawasaki T, Ogaki T, et al. The prevalence of diabetes mellitus and impaired fasting glucose/glycaemia (IFG) in suburban and rural Nepalthe communities-based cross-sectional study during the democratic movements in 1990. Diabetes Res Clin Pract. 2005;67(2):167174. doi:10.1016/j.diabres.2004.06.012

12. Carrasco Pia E, Prez Bravo F, ngel Badillo B, et al. Prevalence of Type 2 Diabetes and Obesity in Two Chilean Aboriginal Populations Living in Urban Zones. Soc Medica Santiago; 2004.

13. Okumiya K, Sakamoto R, Kimura Y, et al. Diabetes mellitus and hypertension in elderly highlanders in Asia. J Am Geriatr Soc. 2010;58(6):11931195. doi:10.1111/j.1532-5415.2010.02862.x

14. Wagner K-H, Schwingshackl L, Draxler A, et al. Impact of dietary and lifestyle interventions in elderly or people diagnosed with diabetes, metabolic disorders, cardiovascular disease, cancer and micronutrient deficiency on micronuclei frequencya systematic review and meta-analysis. Mutat Res Rev Mutat Res. 2021;787:108367. doi:10.1016/j.mrrev.2021.108367

15. Cho NH, Shaw JE, Karuranga S, et al. IDF diabetes atlas: global estimates of diabetes prevalence for 2017 and projections for 2045. Diabetes Res Clin Pract. 2018;138:271281. doi:10.1016/j.diabres.2018.02.023

16. Robert AA, Abdulaziz Al Dawish M, Braham R, et al. Type 2 diabetes mellitus in Saudi Arabia: major challenges and possible solutions. Curr Diabetes Rev. 2017;13(1):5964.

17. Gmez-Peralta F, Abreu C, Cos X, et al. When does diabetes start? Early detection and intervention in type 2 diabetes mellitus. Rev Clin Esp. 2020;220(5):305314. doi:10.1016/j.rce.2019.12.003

18. Guariguata L, Whiting DR, Hambleton I, et al. Global estimates of diabetes prevalence for 2013 and projections for 2035. Diabetes Res Clin Pract. 2014;103(2):137149.

19. Kadamkode V, Banerjee G. Micro RNA: an epigenetic regulator of type 2 diabetes. Microrna. 2014;3(2):8697.

20. Cerf ME. Beta cell dysfunction and insulin resistance. Front Endocrinol (Lausanne). 2013;4:37. doi:10.3389/fendo.2013.00037

21. Florez JC, Hirschhorn J, Altshuler D. The inherited basis of diabetes mellitus: implications for the genetic analysis of complex traits. Annu Rev Genomics Hum Genet. 2003;4(1):257291.

22. Kwak SH, Park KS. Recent progress in genetic and epigenetic research on type 2 diabetes. Exp Mol Med. 2016;48(3):e220e220.

23. Heyn H, Esteller M. DNA methylation profiling in the clinic: applications and challenges. Nat Rev Genet. 2012;13(10):679692.

24. Geach T. Blood-based markers for T2DM. Nat Rev Endocrinol. 2016;12(6):311. doi:10.1038/nrendo.2016.63

25. Bloss CS, Jeste DV, Schork NJ. Genomics for disease treatment and prevention. Psychiatr Clin. 2011;34(1):147166.

26. Burton H, Jackson C, Abubakar I. The impact of genomics on public health practice. Br Med Bull. 2014;112(1):37.

27. Salzman J, Chen RE, Olsen MN, et al. Cell-type specific features of circular RNA expression. PLoS Genet. 2013;9(9):e1003777. doi:10.1371/journal.pgen.1003777

28. Gloss BS, Dinger ME. Realizing the significance of noncoding functionality in clinical genomics. Exp Mol Med. 2018;50(8):18. doi:10.1038/s12276-018-0087-0

29. Cebola I, Pasquali L. Non-coding genome functions in diabetes 1 2; 2015.

30. Zhang P, Wu W, Chen Q, et al. Non-Coding RNAs and their Integrated Networks. J Integr Bioinform. 2019;16(3):20190027. doi:10.1515/jib-2019-0027

31. Lpez-Jimnez E, Andrs-Len E. The Implications of ncRNAs in the development of human diseases. Non-Coding RNA. 2021;7(1):17. doi:10.3390/ncrna7010017

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Genome-wide identification of lncRNA & mRNA for T2DM | PGPM - Dove Medical Press

A pair of Tasmanian tigers photographed at an Australian zoo in 1933.Credit: Universal History Archive/Universal Images Group via Getty

For the first time, researchers have sequenced RNA from an extinct animal species the Tasmanian tiger, or thylacine (Thylacinus cynocephalus).

Using muscle and skin samples from a 132-year-old museum specimen, scientists isolated millions of RNA sequences. This genetic material provides information about the animals genes and the proteins that were made in its cells and tissues. The findings, published in Genome Research1, offer hope that RNA locked up in the worlds museum collections could provide new insights into long-dead species.

Being able to look at RNA in particular opens up a whole new potential source of information, says Oliver Smith, a geneticist at the medical-diagnostics company Micropathology in Coventry, UK. As opposed to looking at what a genome is, we can look at what the genome does.

The Tasmanian tiger was a carnivorous marsupial that lived on the island of Tasmania in southeast Australia. The last known Tasmanian tiger died in captivity in 1936, but a handful of specimens have been preserved in museums.

Researchers studied thylacine remains that had been stored at the Stockholm Natural History Museum since 1891. They collected three muscle samples and three skin samples, each weighing about 80 milligrams.

Million-year-old mammoth genomes shatter record for oldest ancient DNA

Obtaining RNA from historical samples is challenging because unlike DNA which is highly stable and has been extracted from extinct species that lived more than one million years ago RNA rapidly breaks down into smaller fragments. Outside of living cells, its believed to be degraded or destroyed in minutes, says study co-author Marc Friedlnder, a geneticist at Stockholm University.

The team developed a protocol specifically for extracting ancient RNA from tissue samples, adapting standard methods that are used on fresher samples. Nevertheless, it was surprising that we found these authentic RNA sequences in this mummified Tasmanian tiger, says Friedlnder.

The researchers extracted and purified 81.9 million and 223.6 million RNA fragments from the thylacines muscle and skin, respectively. After removing duplicates and very short sequences, they identified 1.5 million RNA sequences from muscle tissue and 2.8 million from skin.

RNA provides information about how gene expression varies between tissues, says co-author Emilio Mrmol-Snchez, a computational biologist at Stockholm University.

In the muscle samples, the research team found sequences corresponding to 236 genes, including some that code for actin and titin proteins that enable muscles to stretch and contract. In the skin samples, they found sequences corresponding to 270 genes, including the one that encodes the structural protein keratin.

The researchers also found a small number of RNA molecules from viruses that lived in or infected the Tasmanian tiger. Being able to trace and recover these molecules opens the door to studying ancient viruses, says Hannes Schroeder, an ancient-DNA researcher at the University of Copenhagen.

The study of ancient DNA is well established, but ancient RNA sequencing is still underdeveloped, says Smith. This study, he adds, is giving a new life into a field which is under-represented and under-rated. He hopes to see future studies routinely combine both DNA and RNA sequencing.

Read more:
Tasmanian tiger RNA is first to be recovered from an extinct animal - Nature.com

Ferrari AJ, Somerville AJ, Baxter AJ, Norman R, Patten SB, Vos T, et al. Global variation in the prevalence and incidence of major depressive disorder: a systematic review of the epidemiological literature. Psychol Med. 2013;43:47181.

Article CAS PubMed Google Scholar

Lim GY, Tam WW, Lu Y, Ho CS, Zhang MW, Ho RC. Prevalence of depression in the community from 30 countries between 1994 and 2014. Sci Rep. 2018;8:110.

Article Google Scholar

Kessler RC, Berglund P, Demler O, Jin R, Koretz D, Merikangas KR, et al. The epidemiology of major depressive disorder: results from the National Comorbidity Survey Replication (NCS-R). Jama. 2003;289:3095105.

Article PubMed Google Scholar

Smith K. Mental health: a world of depression. Nature. 2014;515:1801.

Article CAS Google Scholar

Choi KW, Stein MB, Nishimi KM, Ge T, Coleman JR, Chen C-Y, et al. An exposure-wide and Mendelian randomization approach to identifying modifiable factors for the prevention of depression. Am J Psychiatry. 2020;177:94454.

Article PubMed PubMed Central Google Scholar

Whisman MA, Sbarra DA, Beach SR. Intimate relationships and depression: searching for causation in the sea of association. Annu Rev Clin Psychol. 2021;17:23358.

Article PubMed Google Scholar

Choi KW, Chen C-Y, Stein MB, Klimentidis YC, Wang M-J, Koenen KC, et al. Assessment of bidirectional relationships between physical activity and depression among adults: a 2-sample mendelian randomization study. JAMA Psychiatry. 2019;76:399408.

Article PubMed PubMed Central Google Scholar

Herrman H, Patel V, Kieling C, Berk M, Buchweitz C, Cuijpers P, et al. Time for united action on depression: a LancetWorld psychiatric association commission. Lancet. 2022;399:9571022.

Article PubMed Google Scholar

Cacioppo S, Grippo AJ, London S, Goossens L, Cacioppo JT. Loneliness: clinical import and interventions. Perspect Psychol Sci. 2015;10:23849.

Article PubMed PubMed Central Google Scholar

Cacioppo, JT & Patrick, W. Loneliness: human nature and the need for social connection. New York: WW Norton & Company; 2008.

Beutel ME, Klein EM, Brhler E, Reiner I, Jnger C, et al. Loneliness in the general population: prevalence, determinants and relations to mental health. BMC Psychiatry. 2017;17:97.

Article PubMed PubMed Central Google Scholar

Holt-Lunstad J, Robles TF, Sbarra DA. Advancing social connection as a public health priority in the United States. Am Psychol. 2017;72:51730.

Article PubMed PubMed Central Google Scholar

Killgore WD, Cloonan SA, Taylor EC, Dailey NS. Loneliness: a signature mental health concern in the era of COVID-19. Psychiatry Res. 2020;290:113117.

Article CAS PubMed PubMed Central Google Scholar

Bu F, Steptoe A, Fancourt D. Loneliness during a strict lockdown: trajectories and predictors during the COVID-19 pandemic in 38,217 United Kingdom adults. Soc Sci Med. 2020;265:113521.

Article PubMed PubMed Central Google Scholar

Office of the Surgeon General. Our Epidemic of Loneliness and Isolation: The U.S. Surgeon Generals Advisory on the Health Efffects of Social Connection and Community. (2023).

Holt-Lunstad J, Perissinotto C. Social isolation and loneliness as medical issues. N Engl J Med. 2023;388:1935.

Article PubMed Google Scholar

Erzen E, ikrikci . The effect of loneliness on depression: a meta-analysis. Int J Soc Psychiatry. 2018;64:42735.

Article PubMed Google Scholar

VanderWeele TJ, Tchetgen EJT, Cornelis M, Kraft P. Prognostic significance of social network, social support and loneliness for course of major depressive disorder in adulthood and old age. Epidemiol Psychiatr Sci. 2018;27:26677. (2014).

Article Google Scholar

Heinrich LM, Gullone E. The clinical significance of loneliness: a literature review. Clin Psychol Rev. 2006;26:695718.

Article PubMed Google Scholar

Cacioppo JT, Hawkley LC, Thisted RA. Perceived social isolation makes me sad: 5-year cross-lagged analyses of loneliness and depressive symptomatology in the Chicago Health, aging, and social relations study. Psychol Aging. 2010;25:45363.

Article PubMed PubMed Central Google Scholar

Ebrahim S, Davey Smith G. Mendelian randomization: can genetic epidemiology help redress the failures of observational epidemiology? Hum Genet. 2008;123:1533.

Article PubMed Google Scholar

Yang YC, Boen C, Gerken K, Li T, Schorpp K, Harris KM. Social relationships and physiological determinants of longevity across the human life span. Proc Natl Acad Sci. 2016;113:57883.

Article CAS PubMed PubMed Central Google Scholar

Murthy, VH & Murthy, VH. Together. New York: Harper Collins Publishers; 2020.

Turkheimer, E & Harden, K. Behavior genetic research methods: testing quasi-causal hypotheses using multivariate twin data. In: Reis, HT & Judd, CM, editors. Handbook of research methods in personality and social psychology. New York; Cambridge University Press 2014.159-87.

Davey Smith G, Ebrahim S. Mendelian randomization: can genetic epidemiology contribute to understanding environmental determinants of disease? Int J Epidemiol. 2003;32:122.

Article Google Scholar

Matthews T, Danese A, Wertz J, Odgers CL, Ambler A, Moffitt TE, et al. Social isolation, loneliness and depression in young adulthood: a behavioural genetic analysis. Soc Psychiatry Psychiatr Epidemiol. 2016;51:33948.

Article PubMed PubMed Central Google Scholar

Davey Smith G, Hemani G. Mendelian randomization: genetic anchors for causal inference in epidemiological studies. Hum Mol Genet. 2014;23:R89R98.

Article CAS PubMed PubMed Central Google Scholar

Sanderson E, Glymour MM, Holmes MV, Kang H, Morrison J, Munaf MR, et al. Mendelian randomization. Nat Rev Methods Prim. 2022;2:6.

Article CAS Google Scholar

Lawlor DA, Harbord RM, Sterne JA, Timpson N, Davey Smith G. Mendelian randomization: using genes as instruments for making causal inferences in epidemiology. Stat Med. 2008;27:113363.

Article PubMed Google Scholar

Rdevand L, Bahrami S, Frei O, Lin A, Gani O, Shadrin A, et al. Polygenic overlap and shared genetic loci between loneliness, severe mental disorders, and cardiovascular disease risk factors suggest shared molecular mechanisms. Transl Psychiatry. 2021;11:111.

Article Google Scholar

Abdellaoui A, Sanchez-Roige S, Sealock J, Treur JL, Dennis J, Fontanillas P, et al. Phenome-wide investigation of health outcomes associated with genetic predisposition to loneliness. Hum Mol Genet. 2019;28:385365.

Article CAS PubMed PubMed Central Google Scholar

Sbarra, DA. Social integration and sleep disturbance: a gene-environment interaction study. Collabra. 2016;2:1.

Abdellaoui A, Chen HY, Willemsen G, Ehli EA, Davies GE, Verweij KJ, et al. Associations between loneliness and personality are mostly driven by a genetic association with neuroticism. J Personal. 2019;87:38697.

Article Google Scholar

Boomsma DI, Cacioppo JT, Muthn B, Asparouhov T, Clark S. Longitudinal genetic analysis for loneliness in Dutch twins. Twin Res Hum Genet. 2007;10:26773.

Article PubMed Google Scholar

Wootton RE, Greenstone HS, Abdellaoui A, Denys D, Verweij KJ, Munaf MR, et al. Bidirectional effects between loneliness, smoking and alcohol use: evidence from a Mendelian randomization study. Addiction. 2021;116:4006.

Article PubMed Google Scholar

Day FR, Ong KK, Perry JR. Elucidating the genetic basis of social interaction and isolation. Nat Commun. 2018;9:16.

Article Google Scholar

Goossens L, Van Roekel E, Verhagen M, Cacioppo JT, Cacioppo S, Maes M, et al. The genetics of loneliness: linking evolutionary theory to genome-wide genetics, epigenetics, and social science. Perspect Psychol Sci. 2015;10:21326.

Article PubMed Google Scholar

Burgess S, Smith GD, Davies NM, Dudbridge F, Gill D, Glymour MM, et al. Guidelines for performing Mendelian randomization investigations: update for summer 2023. Wellcome Open Res. 2023;4:186.

Morrison J, Knoblauch N, Marcus JH, Stephens M, He X. Mendelian randomization accounting for correlated and uncorrelated pleiotropic effects using genome-wide summary statistics. Nat Genet. 2020;52:7407.

Article CAS PubMed PubMed Central Google Scholar

Wray NR, Ripke S, Mattheisen M, Trzaskowski M, Byrne EM, Abdellaoui A, et al. Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression. Nat Genet. 2018;50:66881.

Article CAS PubMed PubMed Central Google Scholar

Levey, DF, Stein, MB, Wendt, FR, Pathak, GA, Zhou, H, Aslan, M, et al. Bi-ancestral depression GWAS in the Million Veteran Program and meta-analysis in > 1.2 million individuals highlight new therapeutic directions. Nat Neurosci. 2021;7:110.

Elsworth, BL, Lyon, MS, Alexander, T, Liu, Y, Matthews, P, Hallett, J, et al. The MRC IEU OpenGWAS data infrastructure. bioRxiv (2020).

Gaziano JM, Concato J, Brophy M, Fiore L, Pyarajan S, Breeling J, et al. Million Veteran Program: a mega-biobank to study genetic influences on health and disease. J Clin Epidemiol. 2016;70:21423.

Article PubMed Google Scholar

Lwe B, Kroenke K, Grfe K. Detecting and monitoring depression with a two-item questionnaire (PHQ-2). J Psychosom Res. 2005;58:16371.

Article PubMed Google Scholar

VanderWeele TJ, Tchetgen EJT, Cornelis M, Kraft P. Methodological challenges in mendelian randomization. Epidemiology. 2014;25:42735.

Article PubMed PubMed Central Google Scholar

Verbanck M, Chen C-Y, Neale B, Do R. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat Genet. 2018;50:6938.

Article CAS PubMed PubMed Central Google Scholar

Burgess S, Thompson SG. Multivariable Mendelian randomization: the use of pleiotropic genetic variants to estimate causal effects. Am J Epidemiol. 2015;181:25160.

Article PubMed PubMed Central Google Scholar

Bowden J, Davey Smith G, Burgess S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epidemiol. 2015;44:51225.

Article PubMed PubMed Central Google Scholar

Burgess S, Thompson SG. Interpreting findings from Mendelian randomization using the MR-Egger method. Eur J Epidemiol. 2017;32:37789.

Article PubMed PubMed Central Google Scholar

Bowden J, Davey Smith G, Haycock PC, Burgess S. Consistent estimation in Mendelian randomization with some invalid instruments using a weighted median estimator. Genet Epidemiol. 2016;40:30414.

Article PubMed PubMed Central Google Scholar

Zhao Q, Wang J, Hemani G, Bowden J, Small DS. Statistical inference in two-sample summary-data Mendelian randomization using robust adjusted profile score. Ann Stat. 2020;48:174269.

Article Google Scholar

Bowden J, Del Greco M F, Minelli C, Davey Smith G, Sheehan NA, Thompson JR. Assessing the suitability of summary data for two-sample Mendelian randomization analyses using MR-Egger regression: the role of the I 2 statistic. Int J Epidemiol. 2016;45:196174.

PubMed PubMed Central Google Scholar

Hemani G, Bowden J, Davey Smith G. Evaluating the potential role of pleiotropy in Mendelian randomization studies. Hum Mol Genet. 2018;27:R195R208.

Article CAS PubMed PubMed Central Google Scholar

Cacioppo JT, Hughes ME, Waite LJ, Hawkley LC, Thisted RA. Loneliness as a specific risk factor for depressive symptoms: cross-sectional and longitudinal analyses. Psychol Aging. 2006;21:14051.

Article PubMed Google Scholar

Link:
Loneliness and depression: bidirectional mendelian randomization ... - Nature.com

The fresh funding builds on Romes first Series B closing in 2021, bringing the total to $149 million. The oversubscribed round brought in new investors including Johnson & Johnson Innovation-JJDC, Bristol Myers Squibb, and more. Returning investors included ARCH Ventures, GV, Sanofi Ventures, and others.

Rome plans to carry out phase 1 safety testing of its drug candidate in addition to studies confirming how the drug works. The company also expects to continue developing its early pipeline and the technology that it uses to identify disease targets and make Romes clinical trials more efficient.

Rome researches the so-called dark genome a part of the genome that doesnt directly encode proteins. Some parts of the dark genome called repetitive elements can encode the protein reverse transcriptase (RT), which is vital for cleaning away diseased cells in the body. However, if the mechanism breaks down, it can lead to conditions in Romes crosshairs including autoimmune disease, cancer, and neurodegeneration.

Romes candidate is designed to tackle a range of autoimmune diseases such as lupus by blocking RT encoded by a region in the dark genome called LINE-1. According to Romes public release, the viral-like LINE-1 RT is only expressed in diseased cells so can suppress harmful inflammation without leaving the immune system exposed to infections.

In spite of the difficult fundraising conditions in the biotech industry at present, Romes Romes President, CEO and co-founder, Rosana Kapeller, publicly stated that the firm gained significant industry interest in the company during the latest round, including the strategic investment funds from four pharmaceutical companies.

Other companies working on the dark genome have also attracted attention in the industry. For example, the U.K. dark genome player Nucleome Therapeutics bagged 37.5 million in a Series A financing round in 2022. And in 2021, Boehringer Ingelheim recruited a different UK firm, Enara Bio, in a collaboration and licensing agreement to co-develop cancer immunotherapies based on the dark genome.

Read more:
Rome Therapeutics adds $72 million to Series B round to harness ... - OutSourcing-Pharma.com

In 1994, hikers discovered a group of strange trees growing in a canyon in Wollemi National Park, about 60 miles (100 kilometers) west of Sydney, Australia. One hiker notified a park service naturalist, who then showed leaf specimens to a botanist. It was ultimately determined they represented an ancient species that had been essentially frozen in time since dinosaurs roamed Earth.

Called a "living fossil" by some, the Wollemi pine (Wollemia nobilis) is nearly identical to preserved remains dating to the Cretaceous period (145 million to 66 million years ago). There are now just 60 of these trees in the wild and these tenacious survivors are threatened by bushfires in the region. It was thought to have gone extinct around 2 million years ago.

Now, scientists from Australia, the United States and Italy have decoded its genome, shedding light on its unique evolution and reproductive habits, as well as aiding conservation efforts. The paper was posted to the preprint database bioRxiv on Aug. 24 and has not been peer reviewed.

Related: World's deepest canyon is home to Asia's tallest tree - and Chinese scientists only just found it

The pine has 26 chromosomes containing a staggering 12.2 billion base pairs. In comparison, humans have only around 3 billion base pairs. Despite the size of their genome, Wollemi pines are extremely low in genetic diversity, suggesting a bottleneck (when the population is reduced dramatically) some 10,000 to 26,000 years ago.

Indeed, the plants do not exchange much genetic material. The remaining trees appear to reproduce mostly by cloning themselves through coppicing in which suckers emerge from the base and become new trees.

Their rarity may be partly due to the high number of transposons, or "jumping genes" stretches of DNA that can change their position within the genome. These elements also account for the genome's size. "The tiniest plant genome and the largest plant genome have almost the same number of genes. Large differences in size usually come from transposons," Gerald Schoenknecht, program director for the National Science Foundations Plant Genome Research Program told Live Science. Schoenknecht was not involved with the research, but the NSF did provide funding.

As transposons leap to new locations, they can change the sequence of "letters" in a DNA molecule, thus causing or reversing mutations in genes. They may carry functional DNA with them or alter DNA at the site of insertion, and thus have a substantial impact on the evolution of an organism.

If the transposons induced harmful mutations, they may have contributed to population decline precipitated by a changing climate and other factors, the researchers said. These stressful conditions may have led the plant to switch to clonal reproduction. Because increases in transposons correlate to sexual reproduction, a change to asexual reproduction may have reduced their potential introduction of damaging mutations. Paradoxically, while the trees were still reliant on sexual reproduction, the transposons may have played a role in increasing genetic diversity and thus at least temporarily made them more resilient to changing conditions.

"In 99% of all cases, mutations are probably not a good idea," Schoenknecht said. "But over millions of years, the 1% that helps can move the species forward. In this case it may have been a bit of an advantage."

Decoding the genome has also revealed why the Wollemi pine appears to be susceptible to disease in particular, Phytophthora cinnamomi, a pathogenic water mold that causes dieback. The tree's disease resistant genes are suppressed by a type of its own RNA that is associated with the development of wider leaves. Wollemi pines, unlike most conifers, have wide needles.

So, the evolution of wider leaves may have led to the suppression of disease resistance and opened the species up to pathogenic threats which may have been inadvertently tracked in by hikers who illegally visited the protected spot. P. cinnamomi is common in cultivated plants.

While only four small populations remain in the wild, the pines have been extensively propagated by botanic gardens and other institutions in an effort to conserve them and study their unique biology. The species is considered critically endangered by the IUCN.

Thus, the analysis of the Wollemi pine's genome is not simply an academic curiosity it has serious implications for the species' survival.

Originally posted here:
Mystery of 'living fossil' tree frozen in time for 66 million years finally ... - Livescience.com

23andMe Co-founder & CEO Anne Wojcicki says weve only seen the tip of the iceberg for human genomics and DNA research.

Look at all the explosion of all these new technologies with gene therapy, with CRISPR (CRSP), with RNA technologies and understanding the human genome, Wojcicki told Yahoo Finance at the Milken Global Conference in Beverly Hills, California.

Wojcicki says shes disappointed in the lack of progress around genomics, despite having just crossed a significant milestone, 20 years since the first complete sequencing of the human genome.

I think part of the reason is that genetics tells you a lot about what you're at risk for and it doesn't necessarily financially pay to get you that preventative information and to intervene in that way versus just treating people once they have a disease.

The 23andMe (ME) CEO also says they are looking into building new partnerships with pharmaceutical giants once the companys partnership with GlaxoSmithKline (GSK) ends in July.

Interview Highlights:

1:29

How genetics can tell us more about human diversity

2:20

Why 23andMe CEO is disappointed about genome adoption

5:00

Wojcicki on 23andMe partnership with pharma giant GSK

7:15

Genetics needs to be part of medical school training

8:26

Whats next for 23andMe

BRIAN SOZZI: I'm really interested in what 23 is-- 23andMe is working on at this point in its life. But you have said, you see the world through the lens of genetics. What is this world telling you right now?

ANNE WOJCICKI: Oh. Well, genetics-- so I should say, we're on the 20th anniversary of when the first human genome was sequenced. And it has, you know, it's a big milestone of like when it costs billions of dollars to get a single person sequenced and what you can learn from that to where we are today, where 23andMe has over 13 million people. You can learn a tremendous amount from your genome. And you can start to account for like all of this incredible diversity we see in life and all of the variation we have in our health, and like why some people do so well on a treatment, why some people don't, why some people get a disease, why some people don't.

Story continues

So I'm excited about the 20th anniversary and like, where it can go from here. But I do look at everything with that perspective of genetics, because it's almost like a digital code way of looking at all of the diversity that we see in life. And I look around a room, and I do think about like, I look at your eyes right now, and I'm like, ah--

[LAUGHTER]

I know he's an AG.

BRIAN SOZZI: What's AG?

ANNE WOJCICKI: It's just me, like you have like greenish eyes.

BRIAN SOZZI: Ha, what does that say about my DNA?

ANNE WOJCICKI: Well, just like you're not a GG.

BRIAN SOZZI: OK. Is one better than the other?

ANNE WOJCICKI: No, no. It's all-- no, that's the thing about diversity. Like, it's not-- you know, the diversity that we have of humans is about our story of survival, which is like a really beautiful story of like how we as, like, humans are made to keep living on this planet. Like, some people are made for cold, some people are made for hot. Some people have darker skin, and that protects them from sun. Some people are fair skin, and they can absorb more sun. Like, it's just like diversity is amazing.

BRIAN SOZZI: I wasn't going to go here, but are you a-- when you go into a room, are you assessing people like this? I didn't even realize anything. You just told me.

ANNE WOJCICKI: [LAUGHS]

[INTERPOSING VOICES]

[LAUGHTER]

ANNE WOJCICKI: I mean, I do-- I do sometimes see people, then I'm like, and they'll say, they're like, oh, yeah, I haven't done 23andMe yet. And I'll be kind of chomping at the bit. I'd be like I'm dying to see your DNA.

BRIAN SOZZI: Wow. OK, let me go-- let me get back on topic here. We're at the Milken Conference. And there's so much focus on health care because of the efforts by Michael Milken. I went to the doctor recently, just a checkup, didn't tell me anything about my genetics. Didn't even tell me where I can go, what I can do, what I may not do. Is it-- is it interwoven in health care right now? Or is there something missing here?

ANNE WOJCICKI: No. I mean, again, I'd say that's the disappointment I have of the 20 years having been around when they first sequenced the human genome that it's not broadly adopted. And I think part of that reason is that genetics tells you a lot about what you're at risk for. And it doesn't necessarily financially pay to get you that preventative information and to intervene in that way versus just treating people once they have a disease.

And so that's frankly it's my disappointment here is that we don't look at genetics, for instance, when you're getting a prescription and say, like, are you likely to respond? Should you have a different dose? You look at the epidemic of depression. There's all kinds of, you know, you can look at your genetics, look at a number of the drug, you know, interaction genes and see what medication you're likely to most respond to.

It's a tragedy to me that people are not first tested before they are prescribed something. I think also there's all kinds of other conditions like hereditary, you know, colon cancer. People should actually know whether or not they have something like that. And they can have increased screening. Familial hypercholesterolemia is where you have like really high, you know, cholesterol levels, and you need to get screened.

So things like that you could actually really start to, you know, see it for yourself.

BRIAN SOZZI: All of that makes a lot of sense to me.

ANNE WOJCICKI: Yeah.

BRIAN SOZZI: What's the biggest roadblock preventing health care from adopting these things?

ANNE WOJCICKI: It's a good question. I'd say there's two things. Like, one is it's not in the workflow. So meaning like when you go to your doctor, it's not necessarily part of the workflow, the processes. Like, if you said you're interested in having children, it isn't necessarily part of that workflow for actually how your doctor would follow up, how insurance would pay-- be paid. Does the doctor-- is the doctor educated about genetics? And what, you know, why they should do it, what you're potentially going to learn, how to potentially deal with the results if they get them.

I think for a long time we were really just used to genetic counselors and saying, like, hey, it's going to be put on a genetic counselor if you have this particular issue. And more and more, it's going into the mainstream. It should be your primary care physician really integrating it with primary care.

So I think that insurance and payment is a big obstacle. And I would say that physician education is a main-- is a significant obstacle as well as like being part of the workflow.

BRIAN SOZZI: Last time you talked to you around the time of the IPO 2021, you were just starting, I guess, getting going on a partnership with GSK and drug development. Where is that now? And when is that first drugs from this deal coming to market?

ANNE WOJCICKI: Well, that is thriving. GSK is actually-- it's done extraordinarily well. We have over 50 programs underway with GSK. We do have one that is in a phase I study that GSK now controls. We've-- it's co-developed, but we-- they're taking lead now. So, and there's a huge number of programs behind it.

23andMe also has our own wholly owned program. It's an immunotherapy program. So super excited about it. It is definitely exciting to see that you can go from understanding the genetic variation-- that makes me so excited-- to saying, wow, some people are, you know, genetically not likely to develop, you know, a certain kind of condition. And then can I understand that and turn that actually into a drug to help either treat people who have that condition?

BRIAN SOZZI: Is that the holy grail in health care, looking out over the next decade, the ability to match up your genetics with figuring out the cure for cancer or some other disease?

ANNE WOJCICKI: I look at all the explosion of all these new technologies with gene therapy, with CRISPR, with RNA technologies, and understanding the human genome. And I think what 23andMe can really bring to the table here is the understanding of the human genome.

So for instance, one thing that we can do really well is we study healthy people, meaning that you might have a particularly interesting mutation that the scientific world thinks like they don't know. Maybe it's potentially disease-causing. But because I can study you, and I can say, OK, you have finished a knockout mutation, you're doing really well. You have no other health issues.

We potentially know that that, like, changing that or modifying that gene is not going to create any other kinds of issues. So it's a way to help the pharmaceutical industry study essentially what's naturally going on in humans. So we find like studying huge populations and huge numbers helps us just understand that natural variability in people.

BRIAN SOZZI: How do you-- how do you go about championing this in the educational system? Do you see the things you're talking about today being embedded in our education system?

ANNE WOJCICKI: I think that, you know, genetics to be really successful, I think it needs to be part of medical school training. And it needs to be integrated not as a single subject but throughout all aspects of the curriculum. So when you're doing, you know, cardiovascular health, that it's part of that. When you're doing renal health, it's part of that.

That everything, every aspect of health care has a genetic component and helping realize, you know, the personalization that comes with it. Like, every patient that is coming also is unique and different. And so we're all going to metabolize drugs in a different way. You're going to have potentially, like, your blood values are naturally going to be different than my blood values based on your genetics.

There's just a lot of variability that we're going to understand from your genetics. And that's going to manifest in different ways in each of us. So what your baseline is going to be different than what my baseline is. So I do see that it needs to be integrated throughout all aspects of health care, not just as a specialty in genetics.

BRIAN SOZZI: Lastly, what's next for your company?

ANNE WOJCICKI: I'm excited-- there's two big areas. One is consumer, which is really about helping. We have over 13 million people and helping them do more with the information that we already provided. So one thing that we've gotten the feedback from our customers. Is that it's almost an overwhelming amount of information. So how do you translate all this into a care plan?

And there's a lot of lifestyle information we're also collecting from our customers about how they eat, how much they sleep, how much they walk, exercise, happiness. So helping people understand their health in the context of what they've self-reported, their genetics, and then all of their lifestyle information, so that then you can know what things should you change. And some of that might be more proactive screening in the medical system. Some of it might be changing how you sleep. Some of it might be changing how you eat.

So I see a real opportunity for us to deliver a type of personalized prevention that's grounded in your genetics. But we take all that other information about you. And we really help you be as healthy as you can.

I think the second slide, I think, we see this from the work that we've done with GSK. Having a large-- you know, a large amount of genetic information with really, really broad phenotypic data is incredibly powerful for drug discovery. And the end of the GSK collaboration comes in July. And it opens up all kinds of doors for us to, you know, start to do more partnerships with more companies. And I feel a responsibility to my customers that if you're somebody who has a family history of Alzheimer's, it's on me.

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Why the human genome could be healthcares holy grail - Yahoo Finance

It has been 20 years since scientists put together the first rough draft of the human genome, the three billion genetic letters of DNA tightly wound inside most of our cells. Today, scientists are still struggling to decipher it.

But a batch of studies published in Science on Thursday has cast a bright light into the dark recesses of the human genome by comparing it with those of 239 other mammals, including narwhals, cheetahs and screaming hairy armadillos.

By tracing this genomic evolution over the past 100 million years, the so-called Zoonomia Project has revealed millions of stretches of human DNA that have changed little since our shrew-like ancestors scurried in the shadows of dinosaurs. These ancient genetic elements most likely carry out essential functions in our bodies today, the project found, and mutations within them can put us at risk of a range of diseases.

The projects strength lies in the huge amount of data analyzed not just the genomes, but experiments on thousands of pieces of DNA and information from medical studies, said Alexander Palazzo, a geneticist at the University of Toronto who was not involved in the work. This is the way it needs to be done.

The mammalian genomes also allowed the Zoonomia team to pinpoint pieces of human DNA with radical mutations that set them apart from other mammals. Some of these genetic adaptations may have had a major role in the evolution of our big, complex brains.

The researchers have only scraped the surface of potential revelations in their database. Other researchers say it will serve as a treasure map to guide further explorations of the human genome.

Evolutions crucible sees all, said Jay Shendure, a geneticist at the University of Washington who was not involved in the project.

Scientists have long known that just a tiny fraction of our DNA contains so-called protein-coding genes, which make crucial proteins like digestive enzymes in our stomach, collagen in our skin and hemoglobin in our blood. All of our 20,000 protein-coding genes make up just 1.5 percent of our genome. The other 98.5 percent is far more mysterious.

Scientists have found that some bits of that inscrutable DNA help determine which proteins get made at certain places and at certain times. Other pieces of DNA act like switches, turning on nearby genes. And still others can amplify the production of those genes. And still others act like off switches.

Through painstaking experiments, scientists have uncovered thousands of these switches nestled in long stretches of DNA that seem to do nothing for us what some biologists call junk DNA. Our genome contains thousands of broken copies of genes that no longer work, for example, and vestiges of viruses that invaded the genomes of our distant ancestors.

But its not yet possible for scientists to look directly at the human genome and identify all the switches. We dont understand the language that makes these things work, said Steven Reilly, a geneticist at the Yale School of Medicine and one of more than 100 members of the Zoonomia team.

When the project began over a decade ago, the researchers recognized that evolution could help them decipher this language. They reasoned that switches that endure for millions of years are probably essential to our survival.

In every generation, mutations randomly strike the DNA of every species. If they hit a piece of DNA that isnt essential, they will cause no harm and may be passed down to future generations.

Mutations that destroy an essential switch, on the other hand, probably wont get passed down. They may instead kill a mammal, such as by turning off genes essential for organ development. You just wont get a kidney, said Kerstin Lindblad-Toh, a geneticist at the Broad Institute and Uppsala University who initiated the Zoonomia Project.

Dr. Lindblad-Toh and her colleagues determined that they would need to compare more than 200 mammal genomes to track these mutations over the past 100 million years. They collaborated with wildlife biologists to get tissue from species spread out across the mammalian evolutionary tree.

The scientists worked out the sequence of genetic letters known as bases in each genome, and compared them with the sequences of other species to determine how mutations arose in different mammalian branches as they evolved from a common ancestor.

It took a lot of computer churn, said Katherine Pollard, a data scientist at Gladstone Institutes who helped build the Zoonomia database.

The researchers found that a relatively small number of bases in the human genome 330 million, or about 10.7 percent gained few mutations in any branch of the mammalian tree, a sign that they were essential to the survival of all of these species, including our own.

Our genes make up a small portion of that 10.7 percent. The rest lies outside our genes, and probably includes elements that turn genes on and off.

Mutations in these little-changed parts of the genome were harmful for millions of years, and they remain harmful to us today, the researchers found. Mutations linked to genetic diseases typically alter bases that the researchers found had evolved little in the past 100 million years.

Nicky Whiffin, a geneticist at the University of Oxford who was not involved in the project, said that clinical geneticists struggle to find disease-causing mutations outside of protein-coding genes.

Dr. Whiffin said the Zoonomia Project could guide geneticists to unexplored regions of the genome with health relevance. That could massively narrow down the number of variants youre looking at, she said.

The DNA that governs our essential biology has changed remarkably little over the past 100 million years. But of course, we are not identical to kangaroo rats or blue whales. The Zoonomia Project is allowing researchers to pinpoint mutations in the human genome that help make us unique.

Dr. Pollard is focused on thousands of stretches of DNA that have not changed over that period of time except in our own species. Intriguingly, many of these pieces of fast-evolving DNA are active in the developing human brain.

Based on the new data, Dr. Pollard and her colleagues think they now understand how our species broke with 100 million years of tradition. In many cases, the first step was a mutation that accidentally created an extra copy of a long stretch of DNA. By making our DNA longer, this mutation changed the way it folded.

As our DNA refolded, a genetic switch that once controlled a nearby gene no longer made contact with it. Instead, it now made contact with a new one. The switch eventually gained mutations allowing it to control its new neighbor. Dr. Pollards research suggests that some of these shifts helped human brain cells grow for a longer period of time during childhood a crucial step in the evolution of our large, powerful brains.

Dr. Reilly, of Yale, has found other mutations that might have also helped our species build a more powerful brain: those that accidentally snip out pieces of DNA.

Scanning the Zoonomia genomes, Dr. Reilly and his colleagues looked for DNA that survived in species after species but were then deleted in humans. They found 10,000 of these deletions. Most were just a few bases long, but some of them had profound effects on our species.

One of the most striking deletions altered an off switch in the human genome. It is near a gene called LOXL2, which is active in the developing brain. Our ancestors lost just one base of DNA from the switch. That tiny change turned the off switch into an on switch.

Dr. Reilly and his researchers ran experiments to see how the human version of LOXL2 behaved in neurons compared with the standard mammalian version. Their experiments suggest that LOXL2 stays active in children longer than it does in young apes. LOXL2 is known to keep neurons in a state where they can keep growing and sprouting branches. So staying switched on longer in childhood could allow our brains to grow more than ape brains.

It changes our idea of how evolution can work Dr. Reilly said. Breaking stuff in your genome can lead to new functions.

The Zoonomia Project team has plans to add more mammalian genomes to their comparative database. Zhiping Weng, a computational biologist at UMass Chan Medical School in Worcester, is particularly eager to look at 250 additional species of primates.

Her own Zoonomia research suggests that virus-like pieces of DNA multiplied in the genomes of our monkey-like ancestors, inserting new copies of themselves and rewiring our on-off switches in the process. Comparing more primate genomes will let Dr. Weng get a clearer picture of how those changes may have rewired our genome.

Im still very obsessed with being a human, she said.

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Scientists Compare Genomes of 240 Mammals to Understand Human DNA - The New York Times

There are more than 6,000 mammalian species on Earth, each of them different. Over the past 100 million years, mammals have evolved to adapt to their surrounding environment, resulting in diverse features.However, certain parts of the genome have remained the same across species and over millions of years, a large international collaboration of 30 research teams has found. This suggests that these regions are important, and the researchers believe these could hold the key to understanding human disease better.

The findings were recently published in 11 papers in the journal Science. The collaboration, called Zoonomia Project, investigated the genomic basis of shared and specialised traits in mammals.

The reason why the authors compared 240 mammalian genomes is to observe which parts remained unchanged across species during the course of evolution. Since evolution is a natural phenomenon that helps species adapt over time in response to the changing environment, any part of the genome that remains unchanged must be important.

A genome is the complete set of genetic information in an organism, and provides all of the information the organism requires to function. It consists of two broad parts. One is the genes, which are responsible for manufacture of protein molecules by the organism.

The other part consists of regulatory elements. These regions do not code for proteins, but instruct other genes where, when and how many proteins they must produce.

The scientists hypothesised that mutations in these regions of the genome may give rise to new diseases, or may be responsible for some unique mammalian features.

One of the paper is about the sled dog Balto, who was partly descended from the Siberian Husky, and was one of the most famous dogs in the world.

In 1925, during an outbreak of diphtheria in Nome, Alaska, Balto helped deliver serum to children. The study examined Baltos genome and compared it with the genomes of other dogs of that time and the present. It found that sled dogs of that time (including Balto) were genetically healthier than modern dogs, while Balto had more genetic diversity than his contemporaries and also modern dogs.

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The scientists have found some genetic variants that may be responsible for rare and common human diseases, including cancer. They studied a disease called medulloblastoma. It is a type of brain cancer that originates in the cerebellum, and is the most common type of cancerous brain tumour in children.

In one of the papers, scientists studied patients with medulloblastoma and found mutations in regions of the human genome which are otherwise conserved across all mammalian species. According to the researchers, these mutations may be associated with the disease, or may slow down the treatment of the illness.

The fact that the regions are conserved across mammalian species, but show mutations in patients with medulloblastoma, supports the hypothesis that the reason these portions are conserved is because they are important.

Therefore, scientists may use this approach in future to identify genetic changes that could be responsible for diseases.

Other papers have described how some parts of the conserved genomic regions are associated with exceptional mammalian traits such as a superior sense of smell, the ability to hibernate in winters and an extraordinary brain size, among others.

According to one of the studies, mammals started changing and diverging about 65 million years ago. This was even before the Chicxulub impactor, the asteroid that killed dinosaurs, hit Earth.

Another study found a link between more than 10,000 genetic deletions in human genomes and the function of neurons.

One paper said that species that have had a small population size historically are at a higher risk of extinction in the present day.

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Genomes From 240 Mammalian Species Help Explain 100 Years Of Evolution And Human Disease - ABP Live