Category Archives: Human Genetics

Materials

The following reagents were used in this study: HNP1, HNP2 and HNP3 (Peptide Institute, Inc., Japan); TGF- (BioLegend Inc., CA, USA); Dulbeccos Modified Eagles Medium (Cytiva, Marlborough, MA, USA); Fetal Bovine Serum (Gibco, Grand Island, NY); ProLong Gold Antifade Mountant with DAPI (Invitrogen, CA, USA); LDH-Cytotoxicity Colorimetric Assay Kit II (BioVision Inc., CA, USA); RNeasy Mini Kit (QIAGEN Inc., Hilden, Germany); iScript Reverse Transcription Supermix, SsoAdvanced Universal Probes Supermix (Bio-Rad Inc., CA, USA); Pierce BCA Protein Assay Kit (Thermo Fisher Scientific Inc., NY, USA); 1X Protease/Phosphatase Inhibitor Cocktail, Rabbit anti-COL1A1 antibody, Mouse anti-Ki-67 antibody, Rabbit anti--actin antibody, Mouse anti-rabbit IgG antibody (HRP conjugate), Anti-rabbit IgG Alexa Fluor 555, Anti-mouse IgG Alexa Fluor 488 (Cell Signaling Technology Inc., MA, USA); Amersham ECL Western Blotting Detection Kit (GE Healthcare Life Sciences Inc., MA, USA); Alliance Q9 chemiluminescence imaging system (Uvitec Inc., UK); Tissue-Tek O.C.T. Compound (Sakura, Alphenaan den Rijn, Netherlands).

Neonatal foreskin tissues were obtained by surgical circumcision of healthy male neonates at the Pediatric Surgery clinic, King Chulalongkorn Memorial Hospital with parental informed consent and assent forms. Ethical approval for this study was granted by the Institutional Review Board of the Faculty of Medicine, Chulalongkorn University (IRB 120/63). We confirm that all methods and experiments were performed in accordance with relevant guidelines and regulations. Dermal fibroblasts were isolated as described previously17 and cultured in medium containing DMEM supplemented with 10% FBS and gentamicin (1mL/L). The cells were incubated in a 5% CO2 incubator at 37C, and the cells derived from the 2nd to 5th passage were used in experiments.

Dermal fibroblasts (5103 and 1104 cells/well) in 100 L of DMEM with 10% FBS were seeded into 96-well clear round bottom, ultra-low attachment plates. The medium was replaced with fresh medium every 3days18. Spheroids were imaged at days 3, 5 and 7 and diameters were measured by ImageJ.

Cell proliferation was analyzed by methylene blue staining. Dermal fibroblasts were seeded into a 96-well plate (3103 cells/well) with 1% FBS DMEM overnight. HNP1-3 (0.62510M) were added into the wells, and the cells were incubated for 24h. The supernatant was collected, and the cells were fixed with 20% (v/v) formaldehyde for 48h and stained with methylene blue for 30min. The cells were washed and eluted with 100 L of ice-cold HCl (0.1M) in absolute ethanol solution (1:1 ratio). The absorbance was measured at 650nm using microplate reader. Cytotoxicity was analyzed using LDH-Cytotoxicity Colorimetric Assay Kit II. Collected supernatants (2.5 L) were mixed with 25 L of LDH reaction mix for 30min. Stop solution (2.5 L) was added and the absorbance was measured at 450nm using microplate reader. Spheroids derived from dermal fibroblasts (5103 cells/well) were treated with HNP1-3 at 10M for 4days. All experiments were performed in triplicates.

Dermal fibroblasts were seeded into a 6-well plate (2.5105 cells/well) in DMEM containing 1% FBS overnight. The cells were treated with HNPs (2.5, 5 and 10M) for 24h. Total RNA was extracted and converted to cDNA with the following conditions: 25C for 5min, 46C for 20min and 95C for 1min. COL1A1 and Ki-67 gene expressions were determined by real-time PCR. ABL gene expression was used as internal control. Primers and probes are listed in Supplementary Table S1 online. Real-time PCR was performed for 40 cycles with the following program: 95C for 2min, 95C for 5s and 60C for 30s.

Dermal fibroblasts were seeded into a 6-well plate (2.5105 cells/well) in 1% FBS in DMEM overnight. HNPs (2.5, 5 and 10M) were added into the wells and the cells were incubated for 48h. Cells were lysed by 1X RIPA Lysis Buffer containing 1X Protease/Phosphatase Inhibitor Cocktail. Total protein concentration was measured by Pierce BCA Protein Assay Kit. Protein lysates (10g) were mixed with 2X SDS dye and heated at 100C for 5min. Proteins were loaded in 7.5% SDS-PAGE and gel electrophoresis was performed at 100V for 1.5h. Proteins were transferred to PVDF membrane with electrophoresis at 15V for 50min. Blotting membranes were blocked with 1X PBS with 0.1% Tween-20 (PBST) containing 5% skimmed milk, followed by incubation with primary antibodies; COL1A1 (1:2000) and -actin (1:4000), overnight at 4C. The membranes were washed with PBST, and mouse anti-rabbit IgG (HRP conjugate) secondary antibody (1:4000) was added. The membranes were incubated for 1h with shaking before washing. The membranes were soaked in chemiluminescent substrate (Amersham ECL Western Blotting Detection Kit) and chemiluminescence signals were directly scanned with Alliance Q9 chemiluminescence imaging system. The band intensity was quantified by densitometry using ImageJ.

Dermal fibroblasts were seeded into a Lab-Tek II Chamber Slide System (1.5104 cells/well) in 1% FBS in DMEM overnight. HNPs (2.5, 5 and 10M) were added into the cells and incubated for 24h. The cells were washed with PBS and fixed with 4% paraformaldehyde for 10min. The cells were treated with 0.2% Triton-100 in PBS for 2min and blocked with 1% BSA in PBS for 30min. Primary antibody: Ki-67 (1:1000), diluted in 1% BSA in PBS was added and the cells were incubated at 4C overnight. After washing, secondary antibody: anti-mouse IgG Alexa Fluor 488 (1:2000), diluted in 1% BSA in PBS was added and the cells were incubated for 1h. After washing, the sections were mounted and proteins were observed.

Spheroids derived from dermal fibroblasts (5103 cells/well) were treated with HNPs (10M) for 4days. The spheroids were collected and covered with Tissue-Tek O.C.T. Compound. Frozen spheroids were cryosectioned into 8m thick layers onto glass slides. The sections were washed with PBS, fixed with 4% paraformaldehyde for 10min and treated with 0.2% Triton-100 in PBS for 2min. The sections were blocked with 5% BSA in PBS for 1h and incubated with primary antibody: COL1A1 (1:400), diluted in 1% BSA in PBS at 4C overnight. After washing, the sections were incubated with secondary antibody: anti-rabbit IgG Alexa Fluor 555 (1:1000), diluted in 1% BSA in PBS for 1h. After washing, the sections were mounted and proteins were observed.

The statistical analyses were determined by paired t-test using GraphPad Prism 9.0.0 (GraphPad Software, Boston, MA, USA). A simple linear regression analyses was performed using STATA version 15.1 (StataCorp, College Station, TX USA). The regression coefficients, 95% confidence intervals (CI), and p-value were demonstrated. The results were expressed as the meanstandard deviation (SD) and differences with a p-value<0.05 were considered statistically significant.

Dermal fibroblasts were seeded into a 6-well plate (2.5105 cells/well) in DMEM containing 1% FBS overnight. The cells were treated with HNP1 (10M) for 24h. Total RNA was extracted and the quality of extracted RNA (RNA Integrity Number6.5) was evaluated using an Agilent 2100 Bioanalyzer. The RNA-seq experiment was conducted by Vishuo Biomedical, Thailand. Purified poly-A mRNA was fragmented, and pair-end RNA sequencing was performed on the Illumina HiSeq platform. The Gene Expression Omnibus (GEO) of raw reads in FASTQ files was GEO ID: GSE230670.

Quality of raw reads in FASTQ files was inspected with the FASTQC program (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/). The Trim Galore program (http://www.bioinformatics.babraham.ac.uk/projects/trim_galore/) was used to cut adaptors and sequence reads with a Phred score lower than 30. To estimate abundance of transcript, cleaned raw reads were analyzed with Salmon v1.9.019 by 2 steps; (1) indexing and (2) quantification. First, Salmon with default setting was used to build an index on human reference transcriptome (GRCh38) downloaded from Human Genome Resource at NCBI (downloaded; July 2022) (https://www.ncbi.nlm.nih.gov/projects/genome/guide/human/). Next, Salmon was used for quantification by mapping paired-end reads to the indexed reference sequence in mapping-based mode. Transcript abundances in estimated read counts were imported to R with tximeta v1.12.420 and aggregated to gene-level expression with gene model annotation (GRCh38) for further analysis. Principal component analysis (PCA) was performed on the pre-processed gene expression data, which were first log-transformed and normalized with respect to library sizes by the rlog function in DESeq221 package and standardized so that the expression level of each gene has a zero mean and a unit variance, to visualize the clustering structure of replicates. PCA plots were drawn in R using the ggplot2 package.

Differential gene expression was tested between HNP1 and control groups with DESeq2 v1.34.021 package. Gene expression was normalized with the median of ratios method from DESeq2. Since samples were derived from different donors, statistical design for DESeq2 was accounted for donor factor when fitted generalize linear model to data. Multiple hypothesis testing correction was performed using Benjamini-Hochberg's procedure. Differentially expressed genes (DEGs) were defined as genes with false discovery rates (FDR)<0.01. Boxplots were drawn in R using ggplot2.

Function of genes was analyzed with gene set enrichment analysis (GSEA) from WebGestalt (http://www.webgestalt.org/)22. The values of log fold changes were used to rank genes for the functional enrichment analysis using Gene Set Enrichment Analysis (GSEA) method. KEGG pathway and Gene Ontology databases (biological process, molecular function and cellular component) were used. Multiple hypothesis testing correction was performed using Benjamini-Hochbergs procedure with the FDR cutoff of 0.05 for enriched functions.

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Regulation of dermal fibroblasts by human neutrophil peptides ... - Nature.com

Cohorts, genotyping and phenotyping Adolescent brain cognitive development (ABCD) ABCD cohort description

The Adolescent Brain Cognitive Development (ABCD) cohort is a longitudinal study of brain development and child health7. Investigators at 21 sites around the USA conducted repeated assessments of brain maturation in the context of social, emotional, and cognitive development, as well as a variety of health and environmental outcomes. We analysed data from release 3.0. At the time of the survey questions, the children ranged in age from 9 to 12 years. Informed written consent was provided by parents and assent was provided by children. The ABCD research protocol approved was approved by the Institutional Review Board of University of California San Diego (IRB# 160091)16.

Data used in the preparation of this article were obtained from the Adolescent Brain Cognitive DevelopmentSM (ABCD) Study (https://abcdstudy.org), held in the NIMH Data Archive (NDA). This is a multisite, longitudinal study designed to recruit more than 10,000 children age 910 and follow them over 10 years into early adulthood. The ABCD Study is supported by the National Institutes of Health and additional federal partners under award numbers U01DA041048, U01DA050989, U01DA051016, U01DA041022, U01DA051018, U01DA051037, U01DA050987, U01DA041174, U01DA041106, U01DA041117, U01DA041028, U01DA041134, U01DA050988, U01DA051039, U01DA041156, U01DA041025, U01DA041120, U01DA051038, U01DA041148, U01DA041093, U01DA041089, U24DA041123, U24DA041147. A full list of supporters is available at https://abcdstudy.org/federal-partners.html. A listing of participating sites and a complete listing of the study investigators can be found at https://abcdstudy.org/consortium_members/. ABCD consortium investigators designed and implemented the study and/or provided data but did not necessarily participate in the analysis or writing of this report. This manuscript reflects the views of the authors and may not reflect the opinions or views of the NIH or ABCD consortium investigators.

The ABCD data repository grows and changes over time. The ABCD data used in this report came from https://doi.org/10.15154/1526432) DOIs can be found at https://dx.doi.org/10.15154/1526432. All methods were carried out in accordance with relevant guidelines and regulations.

DNA was extracted from saliva samples of the ABCD participants17. These samples were genotyped on the Affymetrix NIDA SmokeScreen Array (Affymetrix, Santa Clara, CA, USA). The QC procedures are described in full at the following URL: https://doi.org/10.15154/1503209.

ABCD genetic principal components (GPCs) were created using genotyped only SNPs using plink-pca flag.

A set of questions taken from the ABCD Youth NIH Toolbox Positive Affect Items was used. These questions measured aspects of positive emotions and affective well-being in the past week, specifically being attentive, delighted, calm, relaxed, enthusiastic, interested, confident, energetic and able to concentrate. Responses were measured as not true, somewhat true or very true. Each item was analysed separately as well as a combined score that was the sum of responses to the individual questions. In addition to the happiness PGS the models were adjusted for age, sex, and principal genetic components (PGCs) 18.

As the initial UK Biobank GWAS was run in the white British sub-group, testing was performed firstly in the white (as defined by ABCD) participants and secondly in the whole sample, with ancestry treated as a factor variable. The other ancestral backgrounds of this cohort as defined by ABCD are; White, Black, Hispanic, Asian, and Other (Table S6).

Creation of the derived MRI variables from the ABCD cohort has been described in detail elsewhere18. For the purposes of this study, total frontal lobe volume was derived by summing the 22 frontal lobe subsection variables of the left and right hemisphere19. Additionally, we looked at total grey and white matter volume and left and right hippocampus volume. The hippocampal body and tail regions and white matter hyperintensity volume were not available for replication. All outcomes were transformed into z scores and all models were adjusted for the happiness PGS, age, sex, PGCs 18, and MRI site. For models that included participants from different ancestries, a factor variable for ancestry was included (Table S7). Models were weighted to match the American community survey (ACS) data by the weighting variable acs raked propensity score. Relationship filtering was also performed removing one individual at random from any pair of participants with valid phenotypes, who were determined to be related by ABCD.

Add Health is a nationally representative cohort study of more than 20,000 adolescents from the USA who were aged 1219 years at baseline assessment in 199495. They have been followed through adolescence and into adulthood with five in-home interviews in five waves (IV) conducted in 1995, 1996, 20012002, 20082009 and 20162018. In this analysis, participants ranged from 24.3 to 34.7 years old, 53% were female and 62% were non-Hispanic white. The study was approved by the University of California San Diego Institutional Review Board (IRB #190002XX). Informed consent was obtained from all subjects.

Saliva samples were obtained as part of the Wave IV data collection. Two Illumina arrays were used for genotyping, with approximately 80% of the sample genotyped with the Illumina Omni1-Quad BeadChip and the remainder of the group genotyped with the Illumina Omni2.5-Quad BeadChip. After quality control, genotyped data were available for 9974 individuals (7917 from the Omni1 chip and 2057 from the Omni2 chip) on 609,130 SNPs present on both genotyping arrays20. Imputation was performed separately for European ancestry (imputed using the HRC reference panel) and non-European ancestry samples (imputed using the 1000 Genomes Phase 3 reference panel)21. For more information on the genotyping and quality control procedures see the Add Health GWAS QC report online at: https://addhealth.cpc.unc.edu/wp-content/uploads/docs/user_guides/AH_GWAS_QC.pdf.

Add Health Genetic Principal components (variable name pspcN, where N is the number of the PC) were derived centrally by Add Health. To prevent identification of individuals they are randomly reordered in sets of 5, i.e. PCs 15 were reordered so PC1 was may not be the PC with the largest variance. We adjusted models for the first 2 sets of PCs i.e. GPCs 110.

The outcome happiness variable was collected during the at-home interview of Wave IV and was derived from the response to the question: How often was the following true during the past seven days? You felt happy. Responses were given as: never or rarely; sometimes; a lot of the time; most of the time or all of the time; refused; don't know. Those who responded with the latter two options were excluded. Remaining categories were coded from never=0 to all of the time=3.

Ancestry in Add Health is defined in the psancest variable as European, African, Hispanic and East Asian (Table S8). Additionally, Add Health provides a weighting variable to make the results reflective of the US population. In these analyses the models were weighted by the Wave IV variable gswgt4_2.

UK Biobank is a cohort of over half a million UK residents, aged from approximately 4070 years at baseline. It was created to study environmental, lifestyle and genetic factors in middle and older age22. Baseline assessments occurred over a 4-year period, from 2006 to 2010, across 22 UK centres. These assessments were comprehensive and included social, cognitive, lifestyle and physical health measures.

UK Biobank obtained informed consent from all participants, and this study was conducted under generic approval from the NHS National Research Ethics Service (approval letter dated 29 June 2021, Ref 21/NW/0157) and under UK Biobank approvals for application #71392 Investigating complex relationships between genetics, exposures, biomarkers, endophenotypes and cardiometabolic, inflammatory, immune and brain-related health outcomes (PI Rona Strawbridge; GWAS)#17689 (PI Donald Lyall; imaging).

In March 2018, UK Biobank released genetic data for 487,409 individuals, genotyped using the Affymetrix UK BiLEVE Axiom or the Affymetrix UK Biobank Axiom arrays (Santa Clara, CA, USA) containing over 95% common content. Pre-imputation quality control, imputation and post-imputation cleaning were conducted centrally by UK Biobank (described in the UK Biobank release documentation)23.

Several structural and functional brain MRI measures are available in UK Biobank as imaging derived phenotypes (IDPs)24. The brain imaging data, as of January 2021, were used (N=47,920). Participants were excluded if they had responded to either of the happiness questions used for the GWAS meta-analysis, were missing more than 10% of their genetic data, if their self-reported sex did not match their genetic sex, if they were determined by UK Biobank to be heterozygosity outliers, and if they were not of white British ancestry (classified by UK Biobank based on self-report and genetic principal components)23.

Brain imaging data used here were processed and quality-checked by UK Biobank and we made use of the IDPs25,26. Details of the UK Biobank imaging acquisition and processing, including structural segmentation and white matter diffusion processing, are freely available from three sources: the UK Biobank protocol: http://biobank.ctsu.ox.ac.uk/crystal/refer.cgi?id=2367 and documentation: http://biobank.ctsu.ox.ac.uk/crystal/refer.cgi?id=1977 and in protocol publications (https://biobank.ctsu.ox.ac.uk/crystal/docs/brain_mri.pdf).

We investigated key imaging substrates previously associated with psychological health e.g., mood disorder, cognitive health. Total white matter hyperintensity volumes were calculated on the basis of T1 and T2 fluid-attenuated inversion recovery, derived by UK Biobank. White matter hyperintensity volumes were log-transformed due to a positively skewed distribution. We constructed general factors of white matter tract integrity using principal component analysis. The two separate unrotated factors used were fractional anisotropy (FA), gFA, and mean diffusivity (MD), gMD, previously shown to explain 54% and 58% of variance, respectively27. We constructed a general factor of frontal lobe grey matter volume using 16 subregional volumes as per Ferguson et al.27. Total grey matter and white matter volumes were corrected for skull size (by UK Biobank). Models were adjusted for the happiness PGS, age, sex, PGCs 18.

LDpred28 established the LD structure of the genome using a reference panel of 1000 unrelated white British UK Biobank participants (the PGS training set). These participants had not been used in the discovery GWAS or have valid MRI data and passed the same QC as described above. SNPs were excluded if they had MAF<0.01, had HWE P<1 106 or had imputation score<0.8. Scores were then created in the validation set using an infinitesimal model. Models using polygenic scores (PGS) derived using LDpred were adjusted for age, sex, genotyping array and the first eight GPCs.

Due to the lower cohort size of ABCD and Add Health, it would not have been possible to remove 1000 participants from the analyses to use as a training set without markedly reducing the power of the analyses. Therefore, we used the same 1000 unrelated UK Biobank participants as the training set to establish LD and this was used to generate the PGS for the participants in these datasets29. The only additional step was to find the SNPs that were found in both the training (UK Biobank) and validation (ABCD and Add Health) datasets and passed the same SNP filtering criteria in both datasets, with an additional filter that MAF threshold was set at>0.0130. The number of SNPs in each LDpred PGS can be found in supplementary table (S9).

For each pair of related individuals (as determined by ABCD using variables genetic paired subjected 14) one participant was excluded at random. Models were adjusted for age at interview, sex and the first 10 GPCs. For multi-ancestry models, ancestry was treated as a factor variable.

p values for analyses were false discovery rate (FDR)-adjusted31.

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Consistent effects of the genetics of happiness across the lifespan ... - Nature.com

As we look in the mirror and find our fathers eyes or our mothers nose, what similarities persist through generations, and how do the unique ways we animate identical traits tell our story? Carl Zimmer, science journalist in genetics and author of the 2018 non-fiction book She Has Her Mothers Laugh: The Powers, Perversions and Potential of Heredity, captures the mixture of deja vu and naturalistic wonder as the machine of heredity reminds us of its unyielding march throughout our day to day lives.

The exigence of the book reflects a semi-memoir in which She Has Her Mothers Laugh has its humble beginnings from family dysfunction and a mid-life crisis very approachable. Zimmer pulls from his own anxieties about his unknown family tree while sitting with a genetics counselor discussing health predispositions for his own children. Zimmer describes his murky background as his mothers genealogy was equated to an elaborate game of telephone and his fathers side was a dead end, showing the reader that the books genetic inquiry is a very intimate relationship researched and traced back to validate those also searching into their genetic identity. Almost like pulling on loose threads to reveal the patchwork of the gene narrative, Zimmer is our tour guide from the birth of genetics in the 1900s to the political and ethical quandaries that have gravitated toward the human genome as technology has become more advanced and robust.

Zimmer bows down to the gene, and because every chromosome borrows from the last, he does the same. Like any true science journalist, Zimmer doesnt shy away from posing the bioethical questions of the future implications of genetic engineering and technology, but he knows that the answers are weaved into the past. As the book follows the relevant threads of heredity, careful to equally recognize non-western and classical concepts of genealogy, Zimmer exemplifies alternate concepts of heredity from the Malaysian Island of Langkawi that recognizes familial ties if children consumed the same food in which kinship was established through shared substance. Conversely, the hierarchy of pedigrees emerged in French society by the 13th century, coined by noble lineage portraiture. The physical proponents of limited gene pools and royal in-breeding have resulted in physical deformities such as the Habsburg Jaw, marked by an elongated lower jaw, which is symbolic of a moment in history where genetics jeopardized lineage and the unknown medical repercussions would continue to haunt an empire.

Few non-fiction books are fast and immersive reads, but She Has Her Mothers Laugh reads like a fairy-tale story as Zimmer turns to colorful anecdotes, historical case studies, and even mythology to answer the looming gene question. Contextualizing the origin of heredity, Zimmer knows that his audience isnt interested in a genetics lesson on Mendelian inheritance and human genome sequencing.

Leaning on cultural and historical phenomena, we learn of kinship bonds, feudal inheritance, and the transfer of culture aided through genes from across, and sometimes in spite of, generations. At its heart, this seemingly dense read about a long-forgotten high school biology class becomes an empathetic study of the confusion and admiration towards our own genetic profile that reveals a way of life. As the information on genetics has amassed, understanding complex inheritance of rare diseases and CRISPR gene editing, Zimmer treats these scientific checkpoints as living and changing entities that have been morphed by social interest, medical breakthroughs, and prejudice.

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Storytelling through the looking glass of genetics The Stute - The Stute

1 of 3 | Daniel Promislow, shown with his late dog, Frisbee, is a professor in the Department of Laboratory Medicine & Pathology and the Department of Biology at the University of Washington in Seattle and principal investigator of the Dog Aging Project. Photo by Tammi Kaeberlein

NEW YORK, Oct. 11 (UPI) -- A large study aims to follow pet dogs for 10 years or longer to track how genes, diet, exercise and the environment affect aging -- and the findings may shed light on human health.

The Dog Aging Project seeks to recruit mixed breed and purebred pets of every age.

"Dogs age very much like people do," Daniel Promislow principal investigator of the project, told UPI in a telephone interview.

Promislow is a professor in the Department of Laboratory Medicine & Pathology and the Department of Biology at the University of Washington in Seattle. His research focuses on the genetic variation of aging patterns in fruit flies, dogs and humans.

The Dog Aging Project, funded by the National Institutes of Health, is a partnership between the University of Washington, Texas A&M University School of Veterinary Medicine & Biomedical Sciences in College Station and more than two dozen other institutions.

"As people age, the risk of most diseases increases quite dramatically," Promislow said. "Dogs get many of the same diseases as we do. They share our environment and they have a sophisticated health care system like we do."

But dogs age much faster than humans. So, what researchers learn about how their biology and environment influence aging is likely to help them understand the role those factors play in human aging.

So far, the study has collected survey data from about 46,000 dog owners and blood, hair and other samples from about 7,500 dogs.

The findings, such as the contribution of exercise to healthy cognitive ability, have been illuminating, Promislow said.

As dogs age, they can suffer from canine cognitive dysfunction syndrome, which is similar to dementia in the elderly. Dogs with this condition "become lost in familiar spaces, seem to fail to recognize familiar people and lose their normal sleep-wake cycle," Dr. Kate Creevy told UPI via email.

Creevy is the chief veterinary officer of the Dog Aging Project and a professor of small animal internal medicine at Texas A&M University School of Veterinary Medicine & Biomedical Sciences.

The researchers hope to better understand biological or environmental factors that may slow or prevent cognitive decline. They also may find similarities between dogs and humans that affect arthritis and heart function.

"Dogs can teach us a lot not only about dogs, but also about ourselves," Promislow said. "We're really just at the beginning of this study, and we continue to welcome dogs of all ages to enroll in our study."

Dogs develop the same cancers as humans, so it's important to identify genes that increase susceptibility, Elaine Ostrander, of the National Human Genome Research Institute in Bethesda, Md., told UPI via email.

Ostrander, who is not involved in the Dog Aging Project, is the distinguished senior investigator and chief of the institute's Cancer Genetics and Comparative Genomics Branch.

"We find that genes which are relevant for canine cancer are inevitably important for human cancers as well," she said. "The advantage of studying cancer in dogs, however, is that some breeds have a huge excess of particular types of cancer, while in other breeds, it might be absent.

"For example, one in four Bernese mountain dogs will get histiocytic sarcoma, a typically lethal cancer. But it is unheard of the toy breeds. This makes the genetics much easier than when studying humans.

Cancer also is a disease of aging in dogs and humans, and by studying cancer, we continue to contribute to the body of knowledge regarding aging."

Participants in the Dog Aging Project complete an online survey and share stories about their dogs' lifestyle and health. Some owners receive a kit for their veterinarian to collect blood and hair samples and a cheek swab, Promislow said.

Researchers use the samples to sequence the dogs' genome. Some genes are associated with variation in dogs' size and shape, while others determine whether their hair is curly or straight, long or short.

But the researchers' focus is on finding genes that influence changes that occur with aging, such as the increasing risk of certain diseases, or changes in behaviors.

"The owners become participants in science," Promislow said. "We find that people really enjoy that. As we collect more health-related data in the coming years, we will be able to identify genes that are risk factors for health problems and that information could eventually help us with treatment and prevention of disease."

By studying the genetic and environmental factors in all dogs whose owners choose to volunteer, researchers can ensure that what they find is applicable to all canines. In the past, most veterinary studies -- and human ones-- only included participants who frequented particular research hospitals or had specific conditions, Creevy said.

They hope to identify lifestyle factors -- such as components of dogs' diets, physical activity or social interactions -- that promote healthier aging for longer periods of time.

"Such findings would enable us to keep dogs healthier into their senior years, and delay or reduce the need for treatment of disease and disability," Creevy said.

So far, the team has begun to describe the rates of disease occurrence in aging dogs and the most common causes of death reported by their owners.

Researchers also have evaluated factors that affect owners' end-of-life decisions for their pets, as well as identified some of the most frequent signs of old age that they recognize in their dogs.

The information obtained through this research has the potential to benefit humans, too.

"Because dogs are social animals who share human homes, food, water and habits, many things we learn about aging in dogs translate directly to people," Creevy said. "Dogs are exposed to the same pathogens, air pollutants and water quality as their owners.

"Dogs often exercise with their owners -- and don't exercise if their owners don't. The ability to study a dog's entire life over a period of 10 to 15 years means that discoveries about healthy aging in dogs could be rapidly investigated in humans."

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Pet dogs shed light on human health, researchers say - UPI News

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Soybean already is a source of protein and biodiesel, but a new project wants to ensure the crop lives up to its full potential.

An ambitious effort led by the University of Illinois Urbana-Champaign and the U.S. department of Energy Joint Genome Institute will sequence 400 soybean genomes to develop a pangemone an attempt to characterize all the useful diversity in the genome to create an even more robust and resilient crop.

There have been soybean pangenome efforts before, but this will be a big step forward. We want to identify all of the variation present within this diverse set of cultivated soybeans. Knowing details of all of the genetic variation should very much enhance and speed up the ability of crop breeders and biotechnology experts to identify important genes and incorporate them into better crops, said project leader Matt Hudson, professor in the Department of Crop Sciences, part of the College of Agricultural, Consumer and Environmental Sciences at U of I.

As soybean is becoming increasingly important as a worldwide crop, as well as being a key bioenergy crop, this project will have global impact and be particularly relevant to U.S. agriculture.

Hudson and his multi-institution collaborators will select and grow soybean lines, shipping extracted DNA to the JGI for long-read sequencing.

With its inclusion of wild relatives and the sheer number of reference and high-quality draft genomes set for sequencing, the project will drastically improve the current soybean reference genome. Hudson explains that genetic diversity is the raw material for crop improvement, but the crops diversity is not reflected in the reference genome. He likens it to the first human genome, which was pieced together only from Caucasian individuals.

Theres an increasing effort to have the reference human genome reflect all of the variation in people. We think there are equally big reasons to do the same thing in crops, Hudson said. But its hard to locate the missing diversity by any other means than sequencing more genomes.

The U.S. Department of Agricultures September Hogs and Pigs report places the Sept. 1 inventory of all hogs and pigs at 74.3 million head, up 2.2% from last quarter and 0.26% from last year a slight surprise given pre-report estimates.

Much of the surprise reflects market hogs, which the USDA pegs at 0.4% higher compared to trade expectations than ranged from unchanged to nearly 1.9% lower, yet all estimates agree on a roughly 1% smaller breeding herd, said Jason Franken, agricultural economist at Western Illinois University and contributor to the farmdoc team.

All weight classes of market hogs inventories come in a bit above average pre-report expectations, with the lighter classes accounting for most of the unanticipated market hogs, Franken said.

The modest increase in lighter-weight-class hogs partly reflects that the June to August pig crop is also just less than 0.5% larger than last year, compared to expectations ranging from 0.8% to 2.1% lower. About 3.7% fewer sows farrowed is more than offset by a record 11.61 pigs saved per litter, or 4.3% more than were saved in the same period last year, he said.

Cold stocks of pork have rebounded and even resumed seasonal patterns, but still have not returned to average pre-COVID-pandemic levels.

The U.S. exported 506 million pounds of pork in July, or about 4% more than in July 2022. Much of the growth reflects greater exports to Canada and Mexico, while declines occurred in major Asian markets like South Korea, China and Hong Kong.

Taking all of this into account, prices over the next four quarters seem unlikely to exceed current costs of production around $99 per hundredweight, Franken said.

In general hog prices tend to be higher in the second and third quarters, with lower prices in the first and fourth quarters. Consistent with that pattern, prices are forecast to drop to an average of $81 per hundredweight for the fourth quarter of 2023. For 2024, prices are forecast to average $80.60 in the first quarter and then rise seasonally to $90.20 and $94.09 in the second and third quarters.

However, if current gains in pigs per litter do not persist to offset intended cuts to farrowings, then higher prices may be realized, Franken said.

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Credits

Mark C. Taylor is a professor of religion at Columbia University.

This essay is adapted from his forthcoming book: After the Human: A Philosophy for the Future.

Do you think human beings are the last stage in evolution? If not, what comes next?

I do not think human beings are the last stage in the evolutionary process. Whatever comes next will be neither simply organic nor simply machinic but will be the result of the increasingly symbiotic relationship between human beings and technology.

Bound together as parasite/host, neither people nor technologies can exist apart from the other because they are constitutive prostheses of each other. Such an interrelation is not unique to human beings. As the physiologist J. Scott Turner writes in The Extended Organism: Animal-built structures are properly considered organs of physiology, in principle no different from, and just as much a part of the organism as kidneys, heart, lungs or livers. This is true for termites, for example, who form a single organism in symbiosis with their nests. The extended body of the organism is created by the extended mind of the colony.

If we have an expanded understanding of body and mind, and if nature and technology are inseparably entangled, then the notion of artificiality is misleading. So-called artificial intelligence is the latest extension of the emergent process through which life takes ever more diverse and complex forms.

Our consideration of quantum phenomena, mindful bodies, relational ecology, and plant and animal cognition has revealed that we are surrounded by and entangled with all kinds of alternative intelligences. AI is another form of alternative intelligence. Critics will argue that what makes AI different is that it has been deliberately created by human beings. However, all organisms both shape and are shaped by their expanding bodies and minds. Instead of being obsessed with the prospect of creating machines whose operation is indistinguishable from human cognition, it is more important to consider how AI is different from human intelligence. The question should not be: Can AI do what humans can do? But rather: What can AI do that humans cannot do?

What is needed is a non-anthropocentric form of artificial intelligence. If humanity is to live on, AI must become smarter than the people who have created it. Why should we be preoccupied with aligning superintelligence with human values when human values are destroying the Earth, without which humans and many other forms of life cannot survive?

With the growing entanglement of the biosphere and the technosphere, further symbiogenesis is the only way to address the very real existential threat we face. But it is all too easy to wax optimistic about the salvific benefits of technology without being specific. Here I want to suggest four trajectories that will be increasingly important for the symbiotic relationship between humans and machines: neuroprosthetics, biobots, synthetic biology and organic-relational AI.

Whatever comes after the human will be neither simply organic nor machinic but the result of the increasingly symbiotic relationship between human beings and technology.

We live during a time when dystopian dread has been weaponized to create paralyzing despair that leaves many people especially the young hopeless. Without underestimating the actual and possible detrimental effects of rapid technological change, it is important not to let these dark visions overshadow the remarkable benefits many of these technologies bring.

As a long-time Type 1 diabetic, my life depends on a digital prosthesis I wear on my belt 24/7/365, which operates by artificial intelligence and is connected to the internet. Just as the Internet of Bodies creates unprecedented possibilities for monitoring and treating bodily ailments, so the Internet of Things connects smart devices wired to global networks that augment intelligence by expanding the mind. While critics and regulators of recent innovations attempt to distinguish the technologies used for therapy, which are acceptable, from technologies used for enhancement, which are unacceptable, the line between these alternative applications is fuzzy at best. What starts as treatment inevitably becomes enhancement.

Neither neuroprosthetics nor cognitive augmentation is new. Writing, after all, is a mnemonic technology that enhances the mind. In modern times, we have been enabled to archive and access memories through personal devices. Most recently, technological innovations have taken cognitive enhancement to another level: brain implants, for example, have been around since at least 2006, and entrepreneurs like Elon Musk (who founded Neuralink to create symbiosis with artificial intelligence) aim to establish embodied human-machine interfaces. Increasing possibilities for symbiotic relations between computers and brains will lead to alternative forms of intelligence that are neither human nor machinic, but something in between.

So-called artificial intelligence is the latest extension of the emergent process through which life takes ever more diverse and complex forms.

In recent years, there has been a revolution in robotics as the result of developments in nanotechnology and the refinement of large language models like ChatGPT. Individual as well as swarms of nanobots might one day be implanted in the body and used for diagnostic and therapeutic purposes, potentially delivering drugs and repairing tissue. Rather than working through the entire body, nanobots might target the precise location where a drug is needed and regulate its delivery.

The most noteworthy deployment in nanotechnology to date is its use in vaccines, including the Covid vaccines. As a group of microbiology and pharmacology experts wrote in a 2021 paper, Nanotechnology has played a significant role in the success of these vaccines; the emergency use authorization that allowed the rapid development and testing of this technology is a major milestone and showcases the immense potential of nanotechnology for vaccine delivery and for fighting against future pandemics. Nanotechnology research and development are in the very early stages but are developing rapidly. As they progress, not only will bodies become more mindful, but it will be increasingly difficult to distinguish the natural from the artificial.

While nanobots are implanted in the body and operate at the molecular level, other robots are becoming both increasingly autonomous and able to think and act in ways that are more human-like. Kevin Roose reported in the New York Times that Googles latest robot RT-2 can interpret images and analyze the surrounding world. It does so by translating the robots movements into a series of numbers a process called tokenizing and incorporating those tokens into the same training data as the language model. Eventually, just as ChatGPT or Bard learns to guess what words should come next in a poem or a history essay, RT-2 can learn to guess how a robots arm should move to pick up a ball or throw an empty soda can into the recycling bin. Thus, rather than programming a robot to perform a specific task, it is possible to give the robot instructions for the task to be performed and to let the machine figure out how to do it.

Building on these recent advances, Hod Lipson, the director of the Creative Machines Lab at Columbia University, is taking robotic research to the next level, building robots thatcreateandare creative. His research is inspired from biology, and he is searching for new biological concepts for engineering and new engineering insights into biology.

It will be increasingly difficult to distinguish the natural from the artificial.

Lipsons ultimate goal is to create robots that not only can reason but also are conscious and self-aware. Defining consciousness as the ability to imagine yourself in the future, he confidently predicts that eventually these machines will be able to understand what they are, and what they think. As cognitive skills enabled by generative AI become more sophisticated, physical movements and activities will become more natural. With these new skills, robots might have the agility to navigate in their surroundings as effectively as humans.

Science and art meet in biobots. David Hanson is the founder and CEO of Hanson Robotics, a Hong Kong-based company founded in 2013, a musician who has collaborated with David Byrne of the Talking Heads, and a sculptor. His best-known work is a humanoid smart robot named Sophia who, he says, personifies our dreams for the future of AI. As a unique combination of science, engineering and artistry, Sophia is simultaneously a human-crafted science fiction character depicting the future of AI and robotics, and a platform for advanced robotics and AI research. She is the first robot citizen and the first robot Innovation Ambassador for the United Development Program.

Speaking for herself, Sophia adds, In some ways, I am a human-crafted science-fiction character depicting where AI and robotics are heading. In other ways, I am real science, springing from the serious engineering and science research and accomplishments of an inspired team of roboticists and AI scientists and designers.

Sophia is so realistic that people have fallen in love and proposed marriage to her. The writer Sue Halpern reports that In 2017, the government of Saudi Arabia gave Sophia citizenship, making it the first state to grant personhood to a machine. The response to Sophia suggests that as robots become more proficient and are integrated into everyday life, they will become less uncanny. The theory of the uncanny valley, perhaps, might turn out to be wrong.

Nowhere are the biosphere and the technosphere more closely interrelated than in synthetic biology. This field includes disciplines ranging from various branches of biology, chemistry, physics, neurology and computer engineering. Michael Levin and his colleagues at the Allen Discovery Center of Tufts University biologists, computer scientists and engineers have created xenobots, which are biological robots that were produced from embryonic skin and muscle cells from an African clawed frog (Xenopus laevis). These cells are manually manipulated in a sculpting process guided by algorithms. Like Sophia, xenobots are sculptures that complicate the boundary between organism and machine. As Levin and his colleagues wrote in 2020:

Living systems are more robust, diverse, complex and supportive of human life than any technology yet created. However, our ability to create novel lifeforms is currently limited to varying existing organisms or bioengineering organoids in vitro. Here we show a scalable pipeline for creating functional novel lifeforms: AI methods automatically design diverse candidate lifeforms in silico to perform some desired function, and transferable designs are then created using a cell-based construction toolkit to realize living systems with predicted behavior. Although some steps in this pipeline still require manual intervention, complete automation in the future would pave the way for designing and deploying living systems for a wide range of functions.

Xenobots use evolutionary algorithms to modify the computational capacity of cells to create the possibility of novel functions and even new morphologies. Aggregates of cells display novel functions that bear little resemblance to existing organs or organisms. Through a process of trial and error, evolutionary algorithms design cells harvested from skin and heart muscle cells to perform specific tasks like walking, swimming and pushing other entitles. Collections of xenobots display swarming behaviors characteristic of other emergent complex adaptive systems; they can self-assemble, self-organize, self-replicate and self-repair. Levin envisions multiple applications of this biomechanic technology from using self-renewing biocompatible biological robots to cure living systems to creating materials with less harmful effects, delivering drugs internally that repair organs and even growing organs that can be transplanted in humans.

Machines are becoming more like people and people are becoming more like machines.

In 2021, Levin and his colleagues published a follow-up study, in which he reported on a successful experiment in which he created xenobots that independently developed their shape and began to function on their own:

These xenobots exhibit coordinated locomotion via cilia present on their surface. These cilia arise through normal tissue patterning and do not require complicated construction methods or genomic editing, making production amenable to high-throughput projects. The biological robots arise by cellular self-organization and do not require scaffolds or microprinting; the amphibian cells are highly amenable to surgical, genetic, chemical and optical stimulation during the self-assembly process. We show that the xenobots can navigate aqueous environments in diverse ways, heal after damage and show emergent group behaviors.

This generation of xenobots exhibits bottom-up swarming behavior, which, like all emergent complex adaptive networks, is the result of the interaction of multiple individual components that are closely interrelated.

Algorithms program sensation and memory into the xenobots, which communicate with each other through biochemical and electrical signaling. The skin cells use the same electrical processes used in the brains neural network. As Philip Ball writes in Quanta Magazine, Intercellular communications create a sort of code that imprints a form, and cells can sometimes decide how to arrange themselves more or less independently of their genes. In other words, the genes provide the hardware, in the form of enzymes and regulatory circuits for controlling their production. But the genetic input doesnt in itself specify the collective behavior of cell communities.

It is important to stress that these xenobots are autonomous. As Levin and his colleagues conclude their 2021 paper: The computational modeling of unexpected, emergent properties at multiple scales and the apparent plasticity of cells with wild-type genomes to cooperate toward the construction of various functional body architectures offer a very potent synergy. Like superorganisms and superintelligence, the behavior of entangled xenobots is, in an important sense, out of control. While this indeterminacy creates uncertainty, it is also the source of evolutionary novelty. Eva Jablonka, who is an evolutionary biologist at Tel Aviv University, believes that xenobots are a new type of organism, one defined by what it does rather than to what it belongs developmentally or evolutionarily.

While Levin uses computational technology to create and modify biological organisms, the German neurobiologist Peter Robin Hiesinger uses biological organisms to model computational processes by creating algorithms that evolve. This work involves nothing less than developing a new form of artificial intelligence.

According to the pioneering work by James Watson, Francis Crick and other early DNA researchers, a genome functions as a program that serves as the blueprint for the production of an organism. Summarizing this process, Hiesinger raises questions about the accuracy of the metaphor code. Genes encode proteins, proteins encode an interaction network, etc. But what does encode mean yet again? he writes in his 2021 book The Self-Assembling Brain. He continues:

The gene contains information for the primary amino acids sequence, but we cannot read the protein structure in the DNA. The proteins arguably contain information about their inherent ability to physically interact with other proteins, but not when and what interactions actually happen. The next level up, what are neuronal properties? A property like neuronal excitability is shaped by the underlying protein interaction network, e.g., ion channels that need to be anchored at the right place in the membrane. But neuronal excitability is also shaped by the physical properties of the axon, the ion distribution and many other factors, all themselves a result of the actions of proteins and their networks.

It becomes clear that a one-way model for gene-protein interaction is vastly oversimplified. The genotype does not only determine the phenotype, but the phenotype and its relation to the environment also alters the genotype. Hiesinger explains that this reciprocal relationship is even more complicated. Rather than a prescribed program, the genome is a complicated relational network in which both genes and proteins contain the information required to generate the organism. The information of the genes is in part the result of the interactions that occur in a network of proteins.

The reciprocal gene-protein interaction changes the understanding of the genome. The genome is not a prescribed program that determines the structure and operation of the organism. The genome is not fixed in advance but evolves in relation to the information created by the interactions of the proteins it partially produces, which, in turn, reconfigure the genome.

The brain and its development, for example, are not completely programmed in advance but coevolve through a complicated network of connections. Hiesinger uses the illuminating example of navigating city streets to explain the process of the brains self-assembling of neuronal circuits:

How are such connections made during the brains development? You can imagine yourself trying to make a connection by navigating the intricate network of city streets. Except, you wont get far, at least not if you are trying to understand brain development. There is a problem with that picture, and it is this: Where do the streets come from? Most connections in the brain are not made by navigating existing streets, but by navigating streets under construction. For the picture to make sense, you would have to navigate at the time the city is still growing, adding street by street, removing and modifying old ones in the process, all while traffic is part of city life. The map changes as you are changing your position in it, and you will only ever arrive if the map changes in interaction with your own movements in it. The development of brain wiring is a story of self-assembly, not a global positioning system.

The successful creation of evolving networks and algorithms would create an even closer symbiotic relationship between the biosphere and the technosphere.

In this model, there is no blueprint for brain connectivity encoded in the genes:

Genetic information allows brains to grow. Development progresses in time and requires energy. Step by step, the developing brain finds itself in changing configurations. Each configuration serves as a new basis for the next step in the growth process. At each step, bits of the genome are activated to produce gene products that themselves change what parts of the genome will be activated next a continuous feedback process between genome and its products. Rather than dealing with endpoint information, the information to build the brain unfolds with time. Remarkably, there may be no other way to read the genetic information than to run the program.

Hiesinger argues that this understanding of the brains self-assembling neural networks points to an alternative model of not-so-artificial intelligence that differs from both symbolic AI and artificial neural networks (ANNs), as well as their extension in generative AI. The genome functions as an algorithm or as a network of entangled algorithms, which does not preexist the organ or organism but coevolves along with it what it both produces and, in turn, is produced by it.

In other words, neither the genome (algorithm) nor the connectivity of the network is fixed in advance of their developmental process. The brain doesnt come into being fully wired with an empty network, all ready to run, just without information, Hiesinger writes. As the brain grows, the wiring precision develops. This creates a feedback loop that never stops and, therefore, the algorithmic growth of biological networks is continuous.

In symbolic AI, a fixed network architecture facilitates the application of fixed rules (algorithms) in a top-down fixed sequence to externally provided data. Artificial neural networks, by contrast, do not start with prescribed algorithms but generate patterns and rules in a bottom-up process that allows for algorithmic change. Relative weights change, but the network architecture does not.

Hiesinger proposes that the self-assembly of the brains neural network provides a more promising model for AI than either symbolic AI or ANNs. The successful creation of evolving networks and algorithms would create an even closer symbiotic relationship between the biosphere and the technosphere.

One of the concerns about developing organic AI is its unpredictability and the uncertainty it creates. Human control of natural, social and cultural processes is, however, an illusion created by the seemingly insatiable will to mastery that has turned destructive. As Hiesinger correctly claims, An artificial intelligence need not be humanlike, to be as smart (or smarter than) a human. Non-anthropocentric AI would not be merely an imitation of human intelligence, but would be as different from our thinking as fungi, dog and crow cognition is from human cognition.

Machines are becoming more like people and people are becoming more like machines. Organism and machine? Organism or machine? Neither organism nor machine? Evolution is not over; something new, something different, perhaps infinitely and qualitatively different, is emerging. Who would want the future to be the endless repetition of the past?

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How Biotech And AI Are Transforming The Human - Noema Magazine

Pictured: Alexandria Forbes/Getty Images, modified by Nicole Bean for BioSpace

One of the most memorable events of Alexandria Forbes life is being four years old and incubating a clutch of lizard eggs in her kitchen. A naturally inquisitive child, Forbes found the eggs outside and kept them in a paper cup until they hatched.

Ive always had this very strong curiosity and acute observation of life, and that probably underlies my interest in biology as opposed to physics or engineering, Forbes said.

The MeiraGTx CEO was born in the West Indies and quickly developed a love for exploring the regions beaches and gardens. What began as a penchant for observation would eventually lead Forbes to found the clinical-stage gene therapy company in 2015.

This year, Forbes was named to Forbes Medias 50 Over 50 class in the innovation category, and she said she was delighted to be recognized both as a woman scientist and as someone who didnt have a standard career path.

I had a very successful academic career, but then I did something else, and then I had a very successful financial career, but then I did something else, Forbes said.

Leading a biotechnology company was not something Forbes anticipated.

While attending school in the United Kingdom, Forbes excelled in every subject, including science. Despite this, she didnt plan on pursuing the sciences until her teachers encouraged her to take science A-levels. Forbes took A-levels in biology and chemistry instead of English and history as shed originally planned, and later matriculated to the natural sciences program at the University of Cambridge. At that time, the human genome had not been sequenced yet.

I decided in my last year [of undergrad] that the thing I wanted to do was understand the way cells work, Forbes said.

Deciding what to do after graduation was a different story. Englands financial industry was booming in the late 1980s, and Forbes considered becoming a trader before ultimately deciding to pursue a PhD in molecular genetics at the University of Oxford, where she researched signaling pathways in fruit flies. Her doctoral training would later come into play when she began working with MeiraGTx, particularly when it came to developing the technology the company would be built on.

After stints at Duke University, the Carnegie Institute at Johns Hopkins University and the Skirball Institute of Biomolecular Medicine at NYU Langone Medical Center, Forbes shifted from research to finance in 2000 when a former Cambridge colleague encouraged her to take an interview as a buy-side analyst. She worked as a healthcare investor at Sivik Global Healthcare from 2000 to 2008 and Meadowvale Asset Management from 2008 to 2013. She eventually became senior vice president of commercial operations at Kadmon Holdings, Inc., which provided some of the necessary resources to launch MeiraGTx.

While working in investment, Forbes managed a biotech fund, and she said she learned more about drug development from this experience than she would have learned working at a pharmaceutical company that focused on a handful of drugs. It also gave her a real understanding of risk and reward for biotech companies, she said.

You never forget why you were wrong. And that decade, I was wrong many, many times, Forbes said.

Through this work, Forbes saw an opportunity to finance a company in the growing gene therapy arena, and she founded MeiraGTx in 2015 with a vision of building a unique, end-to-end, vertically integrated approach to gene therapy, she wrote in an email to BioSpace. The company went public in 2018, a choice guided by Forbes and co-founder Richard Girouxs familiarity with public investors. MeiraGTxs initial clinical programs were selected because they already had human proof-of-concept; the company develops treatments for diseases of the salivary gland, eyes and central nervous system. It also decided to focus on localized gene therapy delivery to minimize safety and manufacturing issues.

MeiraGTx therapies rely on adeno-associated viruses, which can carry genetic material, and riboswitches, which regulate gene expression. The latter technology was invented by the company.

One of the things I learned during my PhD is that when you want a really sharp signal or a really sharp on/off, you tend not to get that by trying to switch something on, Forbes said of riboswitches. What we did is repress the repression of something, and in life, particularly in developmental biology, thats how it works.

MeiraGTx also built its own manufacturing facility, and then a second in 2021 that also functions as a quality control facility.

This planning made the companys later ventures possible. Having manufacturing in-house allowed MeiraGTx to partner with Janssen for clinical development, Forbes said, which has led to Phase III studies and the opportunity to file Biologic License Applications for the companys products. Working with other companies has also allowed MeiraGTx to refine how it approaches Investigational New Drug applications and other aspects of the regulatory process, Forbes said.

I learn every day doing this job, right? Forbes said. How to deal with people, how to deal with failure, how to deal with success, all of those thingsevery day I learn to do something new, and maybe thats why Ive done three different careers. Because I like learning new things.

Nadia Bey is a freelance writer from North Carolina. She can be reached at beynadiaa@gmail.com.

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The Many Lives of Alexandria Forbes - BioSpace

Ethics approval statement

All the human non-small cell lung cancer samples were obtained from Zhejiang Cancer Hospital with informed consent. This study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). The Ethics Committee in Clinical Research (ECCR) of The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital) approved this retrospective study (IRB-2022-268). The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

All patients signed an informed consent approved by the institutional Review Board.

Quantitative real-timepolymerase chain reaction (qRT-PCR) was performed using 2Synergy Brands Inc (SYBR) Green (Servicebio) on the Roche LightCycler480 II RT-PCR Detection System. The relative CEP20 expression was quantified by RT-PCR, and Actin was used as an internal reference. All the reactions were triplicated and were calculated using the comparative threshold method (({2}^{{ - Delta Delta {text{C}}_{{text{t}}} }})).

Cell lysates or microtubule pellets were subjected to western blotting analysis with anti-CEP20, Actin, or GAPDH antibodies (Sigma, St Louis, MO, USA). The blots were probed with either Alexa Fluor 680 or IRDye 800-conjugated secondary antibodies and detected using the Odyssey system (LI-COR Biosciences, Lincoln, NE, USA). The uncropped immunoblot images are presented in Fig. S8.

A549 and H1299 cells were cultured in a complete DMEM (10% FBS included) medium with 5% CO2 at 37C. H226 and H520 were cultured in a complete RPMI 1640 (10% FBS included) medium with 5% CO2 at 37C. Cells were split at approximately 80% confluence by first aspiring the medium, followed by washing with preheated sterile 1PBS buffer thrice. Trypsin was given for 1min to induce cell detachment at 37C, then terminated by adding an appropriate volume of the medium. The cell mixture was transferred into a 15mL Falcon tube and dissociated to form a single-cell suspension by pipetting up and down. An appropriate volume of suspension was added to a new plate for continuous culture.

The cells were cultivated to the logarithmic growth phase and passaged the day before transfection. When cells reached 2030% confluence, Lipofectamine RNAiMAX and the siRNAs (CEP20 RNAi-1 5-ACCACTAATGTTTGTAGAATT-3 CEP20 RNAi-2 5-ATGGATGACCACCTAAGAATT-3) were diluted with DMEM (FBS free) according to the corresponding transfection system. Each dilution was incubated for 5min, mixed well, and incubated for another 20min at room temperature. After adding the mixture into corresponding groups, cells were cultured for 6h in a 5% CO2 incubator at 37C. Subsequently, the medium was replaced with complete DMEM containing 10% FBS. After 4872h of transfection, the cells were observed under a fluorescent microscope to evaluate their condition and transfection efficiency for further analysis.

A549 and H1299 cells were transfected and subsequently cultured for 48h. Then cells were evenly passage to 96-well plates with 2103 cells per well. Cultured for 24, 48, 72 and 96h, cells were added with 20 L/well MTT solution (5mg/ml, Sigma, St. Louis, MO, USA) and incubated for 4h at 37C. Then the medium was discarded and added 150 L of dimethyl sulphoxide (DMSO) (Sigma, St. Louis, MO, USA), the cell proliferation was analyzed by measuring the absorption at 490nm. Cell growth curves were depicted by Graphpad Prism 9 software.

Cells were plated on 12-well plates (200 cells per well). The cell culture medium was changed every 2days. After 2weeks, the cells were fixed with 4% paraformaldehyde for 15min, then washed 3 times by phosphate buffered solution (PBS), and dyed with crystal violet staining solution for 30min.

Cells were transfected and subsequently cultured until they reached 100% confluence. The cells were then starved overnight using a bare medium (DMEM or RPMI 1640 with no glucose or FBS). Mechanical scratching (wound) was performed manually with a pipette tip (10l), and the medium was replaced with DMEM or RPMI 1640 containing 1% FBS. Cells were imaged every 12h. It is important to note that the same area of the wound was imaged consistently across time points.

Cells were transfected following the protocol described above. After 48h of transfection, cells were starved overnight using a bare medium (DMEM or RPMI 1640 with no glucose or FBS). Subsequently, cells were trypsinized and counted, and 80,000 starved cells were resuspended in DMEM or RPMI 1640 containing 1% FBS and added to the upper chamber of Transwell inserts. The lower chamber was filled with 600l of complete DMEM or RPMI 1640 containing 10% FBS. The cells were then cultured for 4h at 37C with 5% CO2. After incubation, the transwell chambers were taken out and fixed with 4% PFA for 20min at room temperature. The inserts were stained with crystal violet for 20min, then washed for 5min each thrice. Residual non-migratory cells from the upper chamber were wiped off with a swab, while the migratory cells were counted and imaged under a microscope.

A549 cells grown on coverslips were fixed with cold methanol (20C), stained with anti-CEP20, -tubulin antibodies (Sigma, St Louis, MO, USA) for 2h at room temperature, and incubated with either Cy3-conjugated anti-mouse IgG or FITC-conjugated anti-rabbit IgG secondary antibody (Jackson ImmunoResearch) for 40min. DNA was stained with DAPI (Sigma). Finally, the mounted coverslips were analyzed by confocal fluorescence microscopy (LSM510, Zeiss).

For the cellular microtubule depolymerization assay21, A549 cells were treated with 5M nocodazole for the indicated times, and then centrifuged at 100 000g for 20min at 25C. For the cellular microtubule regrowth assay, A549 cells grown on coverslips were incubated with 5M nocodazole for 3h to depolymerize microtubules, and then carefully washed out to remove nocodazole followed by fixation at the indicated times. All cells were stained with mouse anti--tubulin primary antibody and Cy3-conjugated anti-mouse IgG secondary antibody. The coverslips were then mounted and imaged by confocal microscopy (NIKON, Tokyo, Japan). The astral length of microtubules within the region of interest were quantified using ImageJ software (Fiji, NIH). Data are expressed as means.d. and analyzed by students t-test. The supernatant and pellet fractions were collected separately and analyzed by western blotting with anti -tubulin.

Total RNA was isolated and purified using TRIzol reagent (Invitrogen, Carlsbad, CA, USA) following the manufacturers instructions. The RNA amount and purity of each sample were quantified using NanoDrop ND-1000 (NanoDrop, Wilmington, DE, USA). The RNA integrity was assessed using Bioanalyzer 2100 (Agilent, CA, USA) with a RIN above 7.0 and confirmed by denaturing agarose gel electrophoresis. Poly (A) RNA was purified from 1g total RNA using Dynabeads Oligo (dT) 25-61005 (Thermo Fisher, CA, USA) with two rounds of purification. The purified poly(A) RNA was fragmented into small pieces using the Magnesium RNA Fragmentation Module (NEB, e6150, USA) at 94C for 57min. The cleaved RNA fragments were then reverse-transcribed using SuperScript II Reverse Transcriptase (Invitrogen, cat. 1896649, USA). The resultant cDNA was used to synthesize U-labeled second-stranded DNAs with E. coli DNA polymerase I (NEB, m0209, USA), RNase H (NEB, m0297, USA), and dUTP solution (Thermo Fisher, R0133, USA). An A-base is then added to the blunt ends of each strand, preparing them for ligation to the indexed adapters. Each adapter contained a T-base overhang for ligating the adapter to the A-tailed fragmented DNA. Single- or dual-index adapters are ligated to the fragments, and size selection was performed with AMPureXP beads. After treatment with the heat-labile UDG enzyme (NEB, m0280, USA) to remove the U-labeled second-stranded DNAs, the ligated products are amplified using PCR. The PCR conditions were as follows: initial denaturation at 95C for 3min; 8 cycles of denaturation at 98C for 15s, annealing at 60C for 15s, and extension at 72C for 30s; and final extension at 72C for 5min. The average insert size for the final cDNA library was 30050bp. Finally, the 2150bp paired-end sequencing (PE150) was performed on an Illumina Novaseq 6000 according to the manufacturers protocol.

Raw RNA-seq data were processed using fastp (v0.20.1)25 to remove adapter sequences and reads with low sequencing quality. The remaining clean reads were aligned to the human genome (hg38) using HISAT2 software (v2.1.0)26 with default parameter settings. Transcript assembly was performed using StringTie software (v2.0)27, and expression of transcripts sharing each gene_id was quantified as Transcripts Per Million (TPM). Differential expression analysis was performed using the R package DESeq228 with a threshold of significantly differentially expressed genes set as fold change (FC)>1.5 or<0.67 and adjusted P value<0.05. Heatmaps were generated using the R package pheatmap. The Gene Ontology (GO) term and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses in current study were done by R package clusterProfiler29. Adjusted p value<0.05 was considered as statistically significant. The gene set enrichment analysis (GSEA) was performed by R package enrichplot.

The dataset GSE1980430 based on the platform of GPL570 (Affymetrix Human Genome U133 Plus 2.0 Array) containing 30 paired gene-microarray samples of human NSCLC tumor and normal tissues were downloaded from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo). The RNA-seq data of NSCLC samples were retrieved from the Cancer Genome Atlas (TCGA) database (https://portal.gdc.cancer.gov/), including 513 lung adenocarcinoma (LUAD) tumor samples, 57 LUAD adjacent normal samples and 501 lung squamous cell carcinoma (LUSC) tumor samples, and 49 LUSC adjacent normal samples. The expression levels of CEP20 were extracted from these datasets, and the NSCLC samples from the TCGA database were classified into three groups based on their CEP20 expression levels: relatively high (CEP20-high, n=253), relatively low (CEP20-low, n=253), and medium (CEP20-median, n=508).

All experiment results are presented as meanstandard deviation (SD) from 3 independent experiments and showed successful reproducibility. All graphs were generated using GraphPad Prism9 (64-bit, La Jolla, CA, USA). Two-tailed unpaired t-tests (Students t-test) were used to obtain the p values. The data are presented as the meanstandard deviation. *P<0.05, **P<0.01, ***P<0.001.

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CEP20 promotes invasion and metastasis of non-small cell lung ... - Nature.com