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Ethical statement

Ethical approval of this study was granted from the Human Experimentation Committee of the Research Institute for Health Sciences, Chiang Mai university, Thailand (Certificate of Ethical Clearance No. 31/2022). During the research, we protect the rights of participants and their identity, and we confirm that all experiments were performed in accordance with relevant guidelines and regulations based on the experimental protocol on human subjects under the Declaration of Helsinki. Written informed consent from all volunteers was obtained prior to the interview and sample collection.

A total of 95 unrelated subjects residing in five villages of Nan province, Thailand, were enrolled with written informed consent. Volunteers were healthy subjects who were over 20years old, of Khmuic-speaking ethnicity and had no ancestors that were known to be from other recognized ethnic groups for at least three generations. We collected personal data using form-based oral interviews for self-reported unrelated lineages, linguistics, and migration histories. Following the manufacturer's instructions, we collected buccal or saliva samples and extracted DNA using the Gentra Puregene Buccal Cell Kit (Qiagen, Germany).

Genotyping was carried out using the Affymetrix Axiom Genome-Wide Human Origins array10. Affymetrix Genotyping Console v4.2s primary screening produced a total of 93 samples that were genotyped for 622,834 loci on the hg19 version of the human reference genome coordinates (genotype call rate97%). We used PLINK version 1.90b5.224 to exclude loci and individuals with more than 5% missing data and also exclude mtDNA and sex chromosome from our analysis. We further excluded loci that did not pass the HardyWeinberg equilibrium test (P value<0.00005) or had more than 5% missing data, within any population. We used KING 2.325 to determine individual relatedness, and we removed one person from each pair of first degree kinship. After these quality control measures, there are 81 Khmuic-speaking people (Fig.1) with 612,614 loci overall.

We next used PLINK version 1.90b5.2 to merge our newly obtained genotyping results with a set of genome-wide SNP data8, which included populations from East/Southeast Asia, South Asia, African Mbuti, European French, and Southeast Asian ancient samples9,10,11,12,13. It should be noted that in this collection, allelic data from ancient samples was gathered using pseudo-haploid techniques, and samples with less than 15,000 informative loci were eliminated. After filtering the positions of SNPs that can be jointly analyzed within this dataset, we excluded SNPs that had more than 5% missing data or with a minor allele frequency (MAF) less than 3.3104 or were not in HardyWeinberg equilibrium with a significance level of P<0.00005. As a result, 353,505 positions in a dataset consisting of 979 individuals from 90 populations (Supplementary Table 1 and 2) were used for subsequent analysis.

In order to investigate the genetic structure and relationships of the analyzed sample, we used PLINK version 1.90b5.2 to perform pruning for linkage disequilibrium, excluding one variant from pairs with r2>0.4 within windows of 200 variants and a step size of 25 variants. A total of 959 individuals from the sample set, excluding the Mbuti and French populations, were incorporated. There were 149,384 SNPs positions available for this analysis. The Principal Component Analysis (PCA) was carried out using smartpca from EIGENSOFT with the "lsqproject" and "autoshrink" options.

To infer population structure, we employed 155,709 SNP positions derived from a sample set of 979 individuals, which encompassed both Asian samples and the outgroups represented by the Mbuti and French populations, for the ADMIXTURE analysis. The clustering tool ADMIXTURE version 1.3.014 was run from K=2 to K=10 with 100 replicates for each K and using random seeds with the -P option. For each K, the top 20 ADMIXTURE replicates with the highest likelihood for the major mode were displayed using PONG version 1.4.726. For these PCA and ADMIXTURE analyses, the ancient samples and highly drifted modern populations (Mlabri, Onge, Mamanwa, Khamu, and Lua) were projected.

To test admixture and excess ancestry sharing, we used admixr version 0.7.127 from ADMIXTOOLS version 5.110 to calculate the f3 and f4-statistics, with assessed through block jackknife resampling across the genome and using Mbuti as the outgroup. A total of 353,505 SNPs from 979 samples were used in these analyses. Additionalf4-statistics were computed when ancient samples were involved, using French as the outgroup to avoid deep attraction to Africans and only transversions (2,94751,452 SNPs depending on the quality of samples) to avoid potential noise from ancient DNA damage patterns28. We used pheatmap package in R version 3.6.0 to visualize the heatmap of f3 and f4 profiles.

To examine the haplotype sharing between different groups, we used SHAPEIT version 4.1.329 to phase the modern samples. We employed South Asian and East Asian populations as a reference panel (excluding the Kinh Vietnamese) and the recombination map from the 1000 Genomes Phase330 was also used. Our analysis specifically focused on modern population data, consisting of 359,539 SNPs. For the preparation of the reference panel, we extracted individuals of East and South Asian descent, as well as the overlapping sites with our data, for each chromosome from the 1000 Genomes Phase3 data using bcftools version 1.4. The phasing accuracy of SHAPEIT4 can be improved by increasing the number of conditioning neighbors in the Positional Burrows-Wheeler Transform (PBWT) on which haplotype estimation is based29. We conducted phasing with the option -pbwt-depth 8 for 8 conditioning neighbors, while keeping other parameters as default. Subsequently, we employed ChromoPainter version 231 on the phased dataset to initiate the investigation of haplotype sharing with sample sizes for each population were randomly down-sampled to 4 and 8. The former was used for 10 iterations of the EM (expectation maximization) process to estimate the switch rate and global mutation probability. The latter was employed for the chromosomal painting process with the estimated switch and global mutation rates. The output of this process was then used for downstream analyses. We then attempted to paint the chromosomes of each individual, with all the modern Asian samples serving as donors and recipients via the -a argument. The EM estimation yielded a switch rate of approximately 251.21 and a global mutation probability of approximately 0.00001, which were subsequently used as starting values for these parameters for all donors in the painting process. The heatmap results were generated using the pheatmap package in R.

To construct the admixture graph, our initial step involved selecting backbone populations from different language families in Southeast Asia. Specifically, we used f4-statistics to choose representative ethnic groups that speak Austronesian, Tai-Kadai, Austroasiatic, Hmong-Mien, and Sino-Tibetan languages, which included Atayal, Dai, Cambodian, Miao, and Naxi, respectively. We employed the African Mbuti and North Indian populations (Gujarati, Brahmin Tiwari, and Lodhi) who speak Indo-European languages as outgroups. Our focus was on constructing the admixture graph for the Austroasiatic language family in Thailand. Thus, we categorized these populations according to their linguistic branches; Katuic (Bru and Soa), Monic (Mon), Palaungic (Lawa_Eastern, Lawa_Western, Palaung, Blang), and Mlabri. Our interested Khmuic-speaking people were divided into the Khamu (consist of four Khamu populations) and Lua (consist of two Lua populations together with HtinMal and HtinPray).

For modeling the admixture graph, we used a dataset of 359,539 SNPs from modern populations as the input for ADMIXTOOLS 232. Initially, we computed pairwise f2 statistics between the groups using the extract_f2 function with specific parameters; maxmiss=0 (no missing SNPs to calculate), useallsnp: NO (no missing data to allow), and blg=0.05 (SNP block size set in 0.05 morgans). Then, we extracted allele frequency products from the computed f2 blocks using f2_from_precomp. Next, for each scenario, we searched for the best-fitting admixture graph by running ten independent runs of find_graphs. From the 100 independent runs, we selected the one with the lowest score (computed based on residuals between the expected and observed f-statistics given the data) using random_admixturegraph. To confirm the fitting graph, we tested the graph with the lowest score using qpgraph with parameters numstart=100, diag=0.0001, return_fstats=TRUE. This allowed us to check if the absolute value of the worst-fitting Z score was below 3. Starting with no migrations (numadmix=0), we gradually added migrations until we found a fitting graph, which we considered as the best-fitting graph for that particular scenario.

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Genetic diversity and ancestry of the Khmuic-speaking ethnic groups ... - Nature.com

Yomiuri Shimbun file photo National Cancer Center Hospital in Chuo Ward, Tokyo, in May 2021

The Yomiuri Shimbun

13:51 JST,September 19, 2023

While it is a simple fact that every case of cancer begins with a genetic mutation that occurs when cells divide, it is a complex reality that types of cancer and the right drugs for treating them vary depending on the specific type of mutation. A team of researchers is working to address this problem via genome analysis of pediatric cancer patients.

As early as November, researchers from the University of Tokyo Hospital, the National Cancer Center and other institutions are to embark on research on whole genome analysis of pediatric cancer patients to help diagnose and treat cancers that affect children. The teams goal is to confirm the efficacy of whole genome analysis, put the analysis into practice and, in the future, establish a system in which most patients can be diagnosed using this analysis.

Cancer begins when cells in the body become abnormal because of a mistake in copying a gene during cell division and proliferate out of control. By identifying the particular mutation, genome analysis leads to the selection of the best treatment for each patient.

Pediatric cancer, with 2,000 to 2,500 new cases diagnosed each year, is a general term for cancers that develop in children under the age of 15. The affected population is small and diverse, making accurate diagnosis difficult.

As part of the governments 2019 action plan for whole genome analysis, the team will receive research funding from the Japan Agency for Medical Research and Development.

Gene panel testing, which has been covered by insurance since 2019, examines some of the genetic mutations associated with cancer, but advances in technology have now made it possible to analyze the entire genome. The panel test was developed primarily for adults, and it is believed that some genetic mutations unique to pediatric cancer can only be detected by whole genome analysis.

About 20 university hospitals, including those of the University of Tokyo and Kyoto University, as well as hospitals specializing in pediatric cancer treatment, will participate in the research. Among other goals, they hope to determine how well they can detect genomic abnormalities that can be diagnosed and treated.

Tissues and other specimens containing cancer cells sampled from patients at each medical institution will be collected at the National Center for Child Health and Development and sent to a private laboratory for analysis.

The National Cancer Center will analyze the data and a group of about five experts in pediatric cancer and genomics, led by the University of Tokyo Hospital, will discuss treatment methods for each case based on clinical information such as the patients symptoms. The patients doctor will then explain the potential treatments and other information to the patients family.

The research results will also be anonymized and made available to pharmaceutical companies and research institutions for use in, among other things, the development of new pediatric medicines. The team envisions expanding the number of eligible patients in fiscal 2024 and beyond.

We would like to make whole genome sequencing a standard test so that pediatric cancer patients can have it covered by their insurance, leading to the best possible treatment, said University of Tokyo Prof. Motohiro Kato, the team leader.

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Researchers to Apply Genome Analysis to Childhood Cancers; Goal ... - The Japan News

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A new study has analyzed the genomes of bats to investigate their ability to tolerate viral infections and avoid cancer findings that could have implications for our knowledge of human cancers as well as virus transmission from animals. The research is published in Genome Biology and Evolution.

Mice are some of the most commonly used animals in experiments that inform human health but another mammal may be even more informative. Enter the bat famed as the only mammal capable of flight, but also for its longevity, low cancer rates and strong immune systems.

Bats unusual immune systems allow them to better tolerate viral infections, though this can also spell danger for human health. They can play a key role in the spillover of viral infections into humans.

Studying bats immune systems could reveal more about cancer development and provide insights into preventing the spread of disease from animals to people. However, research efforts to uncover exactly what makes bats immune systems tick have been hampered by small sample sizes and limitations in genetic analysis approaches.

In the current study, researchers utilized long-read sequencing to carry out a comprehensive genomic analysis of two bat species, adding these to existing high-quality genomes to characterize the genetic features associated with their low cancer rates and robust immune responses.

The studys lead author, Dr. Armin Scheben, explained that the team compared 13 existing bat genomes plus their 2 new additional genomes against those of humans, mice, dogs, pigs and horses. Our study increased the quantity of data by sampling 15 bat species and also increased the quality of data by using more complete genomes mainly generated with long-read DNA sequencing, said Scheben, a postdoctoral fellow at Cold Spring Harbor Laboratory, speaking to Technology Networks.

We looked for changes in both gene gains and losses as well as more subtle adaptive changes in DNA sequences that make bats different from the other mammals, he added.

They investigated the positive selection of cancer-related genes genes included either in the Tumor Suppressor Database or the Catalogue of Somatic Mutations in Cancer. They found evidence for positive selection of 33 tumor suppressor genes and 6 DNA repair genes, suggesting a link to the bats low rates of cancer and increased longevity. Strikingly, cancer-related genes were also enriched more than twofold in bat genomes compared to those of other mammals.

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The researchers also found changes in type I interferon (IFN) genes, which are part of the innate immune system and help to activate antiviral responses. They observed a loss of IFN- genes, while IFN- were relatively unaffected. By relying on the potentially more potent IFN- instead of IFN-, bats may have improved antiviral responses, possibly contributing to their ability to tolerate viruses that can be transmitted to humans.

We show that the bat immune system differs strongly from our own in a gene region known as the type I interferon locus, Scheben said. By targeting this gene region and the proteins it produces with therapeutics, we may be able to treat infectious diseases better in humans. Similarly, bats show signs of genetic adaptations in many anti-cancer genes, which could inspire therapeutics to treat cancer.

Scheben goes on to explain that, while the research is somewhat limited by not experimentally testing these genetic mechanisms, he considers the study to be more of a hypothesis generator. To dig deeper into these findings, the team is now working on developing what he calls batified mouse models mice genetically modified to carry bat variants of genes.

By testing these batified mice, we aim to better understand how bats resist infections and cancer, Scheben explains. These findings can help other researchers, at universities and in industry, to prioritize specific genes and gene variants as targets for therapeutics.

Reference: Scheben A, Ramos OM, Kramer M, et al. Long-read sequencing reveals rapid evolution of immunity- and cancer-related genes in bats. 2023. Genome Biol. Evol. doi: 10.1093/gbe/evad148

Dr. Armin Scheben was speaking to Dr. Sarah Whelan, Science Writer for Technology Networks.

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How Bats' Genomes May Help Them Avoid Cancer and Survive ... - Technology Networks

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Longitudinal genomic surveillance of carriage and transmission of ... - Nature.com

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.

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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

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