Degraded ecosystem soil and type 2 diabetes gut microbiomes share altered potential metabolism for sugars, lignin and branched-chain fatty acids: a blind spot for global health?

Shared potential metabolism trends in degraded soils and type 2 diabetes gut microbiomes | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Shared potential metabolism trends in degraded soils and type 2 diabetes gut microbiomes View ORCID Profile Craig Liddicoat , View ORCID Profile Bart A. Eijkelkamp , View ORCID Profile Timothy R. Cavagnaro , View ORCID Profile Jake M. Robinson , View ORCID Profile Kiri Joy Wallace , View ORCID Profile Andrew D. Barnes , View ORCID Profile Garth Harmsworth , View ORCID Profile Damien J. Keating , View ORCID Profile Robert A. Edwards , View ORCID Profile Martin F. Breed doi: https://doi.org/10.1101/2025.03.11.642605 Craig Liddicoat 1 College of Science and Engineering, Flinders University , Bedford Park, South Australia , Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Craig Liddicoat For correspondence: craig.liddicoat{at}flinders.edu.au Bart A. Eijkelkamp 1 College of Science and Engineering, Flinders University , Bedford Park, South Australia , Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Bart A. Eijkelkamp Timothy R. Cavagnaro 1 College of Science and Engineering, Flinders University , Bedford Park, South Australia , Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Timothy R. Cavagnaro Jake M. Robinson 1 College of Science and Engineering, Flinders University , Bedford Park, South Australia , Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jake M. Robinson Kiri Joy Wallace 2 Te Aka Mātuatua – School of Science, University of Waikato , Hamilton, Aotearoa New Zealand Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Kiri Joy Wallace Andrew D. Barnes 2 Te Aka Mātuatua – School of Science, University of Waikato , Hamilton, Aotearoa New Zealand Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Andrew D. Barnes Garth Harmsworth 3 Manaaki Whenua – Landcare Research , Palmerston North, Aotearoa New Zealand Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Garth Harmsworth Damien J. Keating 4 College of Medicine and Public Health, Flinders University , Bedford Park, South Australia , Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Damien J. Keating Robert A. Edwards 1 College of Science and Engineering, Flinders University , Bedford Park, South Australia , Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Robert A. Edwards Martin F. Breed 1 College of Science and Engineering, Flinders University , Bedford Park, South Australia , Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Martin F. Breed Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF Abstract Microbiome-mediated impacts of global change on human metabolic health remain understudied. Type 2 diabetes (T2D) is shaped by nutrition, host and environmental factors, with rapidly increasing global prevalence. Soil microbiomes shift with ecosystem degradation and may influence human metabolism through altering food quality and gut microbiomes, including metabolite exposures without requiring colonization. Here, we investigated functional overlaps between soil microbiomes across ecosystem degradation gradients (USA, Australia) and gut microbiomes in T2D versus normal health (Sweden, China). We developed a method to translate metagenomic functional pathways to potential metabolism of biochemical compounds. In silico trend analyses revealed consistent shifts relevant to energy harvesting and management. Both T2D gut microbiomes and degraded soil microbiomes exhibited increased potential metabolism for sugars and decreased potential metabolism for lignin and monomethyl branched-chain fatty acids. Our findings suggest ecosystem degradation may contribute to T2D pathogenesis through nutrient-depleted food and/or adverse shaping of gut microbiome functional capacities. Introduction Microbiomes connect ecosystems and humans under One Health 1 , 2 . Yet, microbiome-mediated impacts of accelerating global change 3 on human metabolic health have received little attention. Type 2 diabetes (T2D) is a major chronic multifaceted metabolic disease, characterized by insulin resistance and high blood sugar, and linked to host, nutritional and environmental factors 4 , 5 , 6 . As the health and economic burden of T2D rise rapidly worldwide 7 , 8 , understanding its complex pathogenesis is critical. The human gut microbiome is integral to our metabolism 9 , 10 with a vast enzymatic repertoire that complements human digestive capabilities 11 and produces metabolites that modulate appetite, gut motility, hormonal signaling and gene expression 10 . Gut microbiomes shift in T2D and with metformin treatment 12 , while fecal microbiome transplantations can improve clinical outcomes 13 , 14 . Given that long-term diet and environmental exposures have major influences on the gut microbiome 15 , 16 , which is intricately linked to metabolism and T2D risk, there is a pressing need to identify upstream nutritional and environmental factors that influence this crucial interface to human metabolic health. Beyond diet and lifestyle factors, environmental exposures are increasingly recognized as contributors to T2D risk 5 , including shifts from rural to urban areas 17 , 18 , 19 , 20 . Microbiomes are a hypothesized link between environments and gut-associated health 21 , 22 . Soils are the dominant reservoir of microbial life 23 , 24 , and exposure to diverse soil microbiomes (for example, via direct contact, food and airborne dust), especially from a young age, can shape gut microbiomes 16 , 21 , 25 , 26 , 27 . In wild primates, gut communities vary with environmental heterogeneity, especially soil differences 28 . Soil microbiomes also shape food quality by driving nutrient cycling and gathering 29 , incorporating microbial metabolites into plants 30 , and influencing gut microbiota of ruminants 31 . Beyond the microbes per se, microbiomes also comprise important metabolites (for example, biochemical compounds, constituent molecules) and potentially transferable genetic material 32 with diverse structural, functional and nutritional roles 9 , 30 , 33 . In soils and the gut, a vast range of metabolites with varying bioactivities may be accumulated, depleted and/or transferred via microbial activity and turnover 9 , 34 . This means microbiomes can extend their influence via metabolites and necromass without requiring direct colonization. While the survival of soil microbes in the gut may be low or uncertain, the shaping of gut microbiomes by soil microbiome exposures is well-recognized 1 , 35 . Moreover, soil microbiome composition and functional capacities may shift in generalizable ways with ecosystem degradation, for example, via urbanization and land-use change, with potential to impact human health 36 , 37 , 38 , 39 . Together, these phenomena create pathways whereby degraded environments may adversely shape soil microbiomes, food quality and gut microbiomes, and potentially contribute to metabolic dysfunction in T2D. In ongoing work to define ‘healthy microbiomes’, approaches based on community-scale functional capacity (rather than taxonomy) should provide greater insight 40 , 41 . Notably, many processes involve assemblages using shared resources in extracellular space 9 . Striving for deeper functional insights and interpretability, metabolite- or compound-oriented approaches 38 aim to move beyond metabolic pathways towards measures related to specific chemical compounds (such as carbohydrates, lipids, proteins, often linked to microbial feedstocks or by-products; Supplementary Fig. 1). Compound-scale insights should offer more foundational information (as compounds can underpin multiple functional pathways), benefit from known links to biochemistry and host health, and may point to new mechanistic hypotheses and therapeutic targets. However, existing metabolite prediction frameworks 42 , 43 , 44 rely on organism-specific and environment-specific approaches unsuited to highly biodiverse, poorly characterized and varying environments, such as soils 38 . Therefore, there is a need for new, compound-oriented methods to examine normal versus aberrant functioning in comparative analyses across soil and gut microbiomes. In this integrative in silico study, we explored potential links between ecosystem degradation and metabolic alterations in the T2D gut by examining functional overlaps between soil microbiomes across ecosystem degradation gradients and gut microbiomes in T2D versus normal health (datasets are described in Supplementary Table 1). We translated metagenomic functional gene-associated biochemical pathways to ‘compound processing potential’ (CPP, %), reflecting potential metabolism of individual compounds. While not measuring compounds directly, our method quantified the capacity of metagenomes to metabolize (or process) them. Reactants and products were not differentiated, as compounds can be inputs and outputs of various processes. Examining (1) 30 selected compounds relevant to soil and gut health (Supplementary Table 2) and (2) via exhaustive compound-wide trend analyses, we identified compounds relevant to energy harvesting and management that shared potential metabolism (i.e., CPP) trends. In a staged approach, we first examined soils from a forest restoration chronosequence 45 (from the United States, US; spanning degraded post-mining, reforested, and unmined reference sites), then progressively considered overlapping CPP trends in T2D versus control cohorts from Sweden 46 and then China 47 . Lastly, we sought corroboration of overlapping trends in a ‘disturbed versus natural’ soil dataset from the Australian Microbiome Initiative (AMI 48 , described in ref. 38 ; see Supplementary Table 1). Results Trends with ecosystem degradation For the 30 selected compounds we assessed trends with ecosystem degradation in the chronosequence soils (US) (Supplementary Table 2; Supplementary Figs. 2-31) and found increased potential metabolism for ( P < 0.001) D-fructose, sucrose, L-arabinose, D-glucose, xylose and chitin; also ( P < 0.01) galactose, mannose and hydrogen sulfide; and ( P ≤ 0.05) glucosamine and arabinoxylan (hemicellulose). We found decreased potential metabolism with ecosystem degradation for ( P < 0.001) lignin, acetate and carbon dioxide; also ( P < 0.01) glycogen, butyrate, trimethylamine N-oxide and trimethylamine; and ( P ≤ 0.05) propionate and methane. Compound-wide trend analyses identified 2,122 compounds with significant CPP trends ( n = 757 decreased, n = 1,365 increased with ecosystem degradation; compound and trend data are in Supplementary Material files). Carbon-containing compounds were mapped using chemical formula ratios oxygen:carbon (O:C), hydrogen:carbon (H:C) and nitrogen:carbon (N:C) ( Fig. 1 ) to visualize energetically and chemically similar compounds that trend together (see example plot that visualizes all carbon-containing compounds within a single sample in Supplementary Fig. 32). Download figure Open in new tab Fig. 1 Compound-wide trend analysis results from restoration chronosequence soil (US) metagenomes. Points represent carbon-containing compounds with decreased † (aqua, n = 720) or increased † (red, n = 1,255) potential metabolism with ecosystem degradation ( † trends with ecosystem degradation are interpreted as the opposite to displayed trends with revegetation age). Each compound is mapped via its chemical formula O:C, H:C and N:C elemental ratios, where clusters of points indicate energetically and chemically similar compounds. The O:C by H:C panels display a limited extent to emphasize data-rich regions. N:C ratios are displayed as three panels to represent the third dimension. Rectangular zones denoting compound classes offer a visual guide only, adapted from 49 , 50 . Chrosequence samples 45 spanned planting ages 6, 12, 22, 31 years and unmined (UM), n = groups of 3. Trends in type 2 diabetes For the 30 selected compounds, we found consistent trends in the Swedish (SWE) and Chinese (CHN) cohort datasets (Supplementary Table 2; Supplementary Figs. 2-31, 33) for increased potential metabolism in T2D for D-fructose (SWE: P ≤ 0.05, CHN: P < 0.01), L-arabinose (SWE: P ≤ 0.05, CHN: P < 0.001), and amylopectin (SWE and CHN: P ≤ 0.05); and decreased potential metabolism in T2D for lignin (SWE and CHN: P ≤ 0.05). Numerous trends were only found in one T2D cohort or the other. Increased potential metabolism was found in T2D in the Swedish cohort for sucrose and starch ( P ≤ 0.05), and in the Chinese cohort for mannose ( P < 0.001); glucose, galactose and xylose (all P < 0.01). Decreased potential metabolism was found in T2D in the Chinese cohort for acetate, propionate, butyrate, carbon dioxide, hydrogen sulfide, indole (all P < 0.001); menaquinone (Vitamin K2), glucosamine ( P < 0.01); methane, p-cresol and serotonin (all P ≤ 0.05). We found conflicting potential metabolism trends for ammonia in T2D, which increased in Swedish ( P < 0.01) but decreased in Chinese ( P < 0.001) cohorts. The compound-wide trend analyses in T2D Swedish and Chinese cohorts found substantially more compounds with decreased potential metabolism in the Chinese cohort (compounds and trend data are in Supplementary Material files; carbon-containing compounds are visualized in Supplementary Figs. 34-35). In the T2D (SWE) data, 1269 compounds had trending potential metabolism (n = 911 decreased, n = 358 increased). For the T2D (CHN) data a total of 4292 compounds showed trending potential metabolism ( n = 3984 decreased, n = 308 increased). We note, to explore the most interesting (potentially significant) results, the T2D compound-wide associations were not subject to P -adjustment. From visual inspection, the chemical ratio mapping (Supplementary Figs. 34-35) showed limited commonality in CPP trends in the vicinity of carbohydrates and widespread reduced CPP in the T2D (CHN) dataset. Consistent trends with ecosystem degradation and T2D In the 30 selected compounds, we found consistent CPP trends with ecosystem degradation and T2D in the chronosequence soils (US), T2D (SWE), and T2D (CHN) datasets, for increased potential metabolism in D-fructose and L-arabinose, and decreased potential metabolism in lignin (Supplementary Table 2). From overlapping compound-wide trend results from the chronosequence soils (US) and T2D (SWE) datasets, based on matching ModelSEED compound identifiers, 304 compounds displayed CPP trends in both datasets (detailed in Supplementary Material files), from which 286 carbon-containing compounds were visualized in Fig. 2 . There, we observed two prominent clusters along straight lines in chemical mapping space: ‘Sugars’ (L-arabinose, melibiose, manninotriose, D-lyxulose, sucrose, D-fructose) that showed increased CPP with T2D (SWE) and ecosystem degradation (US) (Supplementary Table 3); and ‘BCFA-ACPs’ comprising 35 compounds dominated by branched-chain fatty acid (BCFA) – acyl-carrier proteins (ACPs) (Supplementary Table 4), that showed reduced CPP with T2D (SWE) and ecosystem degradation (US). Importantly, the BCFA-ACPs were all involved in fatty acid biosynthesis and dominated by monomethyl unsaturated (-enoyl) BCFAs that were not found elsewhere in the datasets beyond this trend group. The Sugars and BCFA-ACPs are described in detail in Supplementary Material Supporting Information, Supplementary Figs. 36-40, and trends shown in Fig. 3 . Download figure Open in new tab Fig. 2 Overlapping compound-wide trend results from restoration chronosequence soils (US) and Swedish (SWE) T2D cohort. Chemical ratio mapping of carbon-containing compounds with overlapping potential metabolism trends from Fig. 1 and Supplementary Fig. 34). Clusters of each color represent energetically and chemically similar compounds occurring in each of the four trend groups shown. Total (and carbon-containing) compounds in the four trend groups are: decreased potential metabolism with T2D (SWE) and ecosystem degradation, n = 70 (n = 66 red points); increased potential metabolism with T2D (SWE) and ecosystem degradation, n = 89 (n = 81 green points); decreased potential metabolism with T2D (SWE) and ecosystem restoration, n = 95 (n = 90 orange points); increased potential metabolism with T2D (SWE) and ecosystem restoration, n = 50 (n = 49 blue points). Expanded views of ‘Sugars’ and ‘BCFA-ACPs’ (i.e., branched-chain fatty acid - acyl-carrier proteins) are in Supplementary Figs. 36, 38. The plot format is further described in Fig. 1 . Download figure Open in new tab Fig. 3 Key potential metabolism trends. Sugars (a-c), lignin (d-f), and branched-chain fatty acid – acyl carrier proteins (BCFA-ACPs, listed in Supplementary Tables 4-5) (g-i), within the restoration chronosequence (US; spanning planting ages 6, 12, 22, 31 years and unmined, UM, n = groups of 3) (a, d, g), T2D (Swedish cohort, SWE; T2D met-= type 2 diabetes not treated with metformin, n = 33; Normal = normal healthy subjects, n = 43) (b, e, h), and T2D (Chinese cohort, CHN; T2D met- n = 30; Normal n = 52) (c, f, i). Sugars and BCFA-ACPs were groupings identified via matching trends in T2D (SWE) and with ecosystem degradation ( Fig. 2 ). Sugars were graphed as the sum of compound processing potential (CPP, %) for six compounds (L-arabinose, melibiose, D-fructose, sucrose, manninotriose and D-lyxulose), however subsequent testing showed only three of these sugars (L-arabinose, melibiose, D-fructose) displayed individual trends in T2D (Chinese cohort, CHN) (Supplementary Table 3). Lignin CPP values were log 10 -transformed to improve display. Further matching of parallel compound-wide association trends across chronosequence soils (US), T2D (SWE) and T2D (CHN) datasets identified 76 compounds, with n = 60 decreased, n = 16 increased CPP with ecosystem degradation T2D ( Fig. 4 ; see Supplementary Material files). Even after filtering due to intersecting the T2D (CHN) dataset, the increased CPP for sugars: L-arabinose, melibiose, D-fructose still remained as trending, together with 6-phosphosucrose, an intermediate in sucrose biosynthesis. The signals of decreased potential metabolism for lignin and all 35 BCFA-ACPs also remained. Download figure Open in new tab Fig. 4 Relative importance of compounds with consistent trends across restoration chronosequence soils (US) and T2D (Swedish and Chinese) datasets. Points indicate increased or decreased potential metabolism (or compound processing potential, CPP) with ecosystem degradation (soil) and in T2D (gut) microbiomes. Compounds including branched-chain fatty acid-acyl carrier proteins (BCFA-ACPs), sugars that feature widely across many functional themes and pathways, and lignin are highlighted in the plot. Trends with ecosystem degradation are interpreted as the inverse of restoration (based on Kendall’s tau ordinal correlation with revegetation age classes). Measures of significance (y-axis) from the Wilcoxon difference testing of CPP in T2D (versus normal), and the number of level 3 (L3) subsystems (functional themes) linked to each compound, are calculated as mean values from the Swedish and Chinese cohorts. Details for all n = 76 compounds are in Supplementary Table 5. Lastly, corroboration of the majority of observed overlapping trends was found in the disturbed versus natural AMI soils dataset. In disturbed AMI soils, we observed increased potential metabolism of L-arabinose, D-fructose, melibiose and 6-phosphosucrose, and decreased potential metabolism of lignin and all 35 of the BCFA-ACPs (all P = <0.001; Supplementary Fig. 41). From the 76 compounds with matching potential metabolism trends with ecosystem degradation (US), T2D (SWE) and T2D (CHN), a total of 65 compounds (85.5 %) displayed shared potential metabolism trends in the AMI disturbed soils (Supplementary Table 5). Discussion We identified biologically relevant overlapping trends in the potential metabolism of compounds within soil and human gut microbiomes, supporting the hypothesis that nutrient-depleted food (i.e., low BCFA and lignin content, high sugar content) and/or excessive microbiome exposures associated with degraded ecosystem soil may contribute to the development of type 2 diabetes. These linkages warrant further investigation due to the rising burden of T2D and increasing global change impacts on soil microbiomes – which underpin food quality and shape human gut microbiomes. An alternative or complementary explanation may be that similar ecological stresses and dysfunction in soil and gut ecosystems may be driving convergence in microbiome functional traits. Our compound-oriented approach facilitates linkages to a wealth of published literature that links chemical compounds with metabolism, health and wider biological processes in humans and the environment. Also, mapping of compounds into the bioenergetically informed O:C, H:C and N:C coordinate space helped to reveal meaningful patterns of simultaneously trending compounds (i.e., sugars, BCFA-ACPs) with soil-ecosystem quality and T2D versus normal health. Our results align with the One Health framework, revealing plausible functional linkages between ecosystem condition and human metabolic health, via the microbiome. This work contributes necessary research towards finding synergies and co-benefits in simultaneously advancing UN Sustainable Development Goals, including improving theoretical models of nature–human health relationships 51 , 52 . Do branched-chain fatty acids connect healthy ecosystems and healthy people? Our results suggest that microbiomes in degraded ecosystems and T2D have reduced capacity for monomethyl BCFA (mmBCFA) biosynthesis. This may reflect a shift in the biochemical composition of bacterial assemblages as mmBCFAs are key components that enhance bacterial membrane fluidity, and mmBCFA synthesis capacity enables membrane fluidity-related adaptive responses for survival and proliferation through environmental stresses 53 , 54 . BCFA levels vary among bacterial taxa, being commonly higher in Gram-positive bacteria, present in some Gram-negative species, and especially high in Bacillus and Bifidobacterium 53 , 55 . Within ecosystems, BCFAs are present in lipid/waxy plant compounds, animal (including human) tissues and fluids, ruminant products (milk, cheese, meat), fish, organs, blood serum, skin, hair, adipose tissue, colostrum, breast milk, vernix caseosa of newborns, and at very high levels in saprotrophic fungi such as Conidiobolus spp. found in soil and decaying organic matter 54 , 56 , 57 . Therefore, variation in BCFA synthesis capacity could result from changes in microbial taxonomic composition as ecosystems degrade. Monomethyl BCFAs possess multiple beneficial bioactivities for human health and link bacterial and human physiology 56 . Beneficial bioactivities of mmBCFA include lipid-lowering, reducing metabolic disorder risk, maintaining normal insulin-producing β cell function and insulin sensitivity, anti-inflammation, cytotoxicity to cancer cells and regulation of development 54 , 55 , 56 . Bacterially-derived mmBCFAs facilitate direct biochemical uptake and cross-domain metabolism between the gut microbiome and animal hosts 58 , 59 , 60 . In the nematode Caenorhabditis elegans and mammalian tissue culture cells, mmBCFA have been shown to be critical for triggering the host mechanistic target of rapamycin complex 1 (mTORC1) 59 , 60 , a central regulator of cell growth and metabolism 61 . The host mTORC1 responds to environmental signals (for example, amino acid availability, growth factors, energy levels, stress) to coordinate cellular status. However, mTORC1 dysregulation is closely linked to diabetes, cancer, and neurodegenerative disorders 61 . Ref. 62 reported high capacities for human fetal intestinal epithelial cells (enterocytes) to incorporate long-chain BCFA into membrane phospholipids. Following uptake by human intestinal epithelial cells, further interactions between BCFA and host endogenous BCFA synthesis and metabolism may occur 54 , 56 , including post-translational modifications to cell proteins, which alter intestinal cell function 63 . While precise mechanisms are uncertain, it is possible that mmBCFA in the gut microbiome may modulate host cellular metabolism and physiological functioning via these interactions. Microbial sources play a dominant role in the supply of health-associated mmBCFA 55 , 56 . Key inputs to BCFA synthesis include branched-chain amino acids which can only be made in bacteria, plants, and fungi, but not in animals 64 ; or shorter BCFA, which can be elongated. Microbes (for example, rumen bacteria) also contribute to BCFA synthesis in animals 56 . Therefore, both diet and gut microbiome synthesis provide major sources of BCFA to humans 55 , 56 . Within soil and gut microbiomes, the interchange of BCFA between substrates and microbes can be expected because fatty acids are a high-value energy source, and bacteria assimilate environmental BCFA into their bodies 65 , 66 . In the gut, the constant release of mmBCFAs will occur from cell membranes with bacterial turnover from competitive interactions 67 and secretion of fatty acids and their derivatives 58 . A portion of these gut bacteria may be supplied from external ecosystem exposures 21 , 22 , while BCFA-synthesizing bacteria may be further shaped by diet and other factors (for example, high BCFA-content Bifidobacterium spp. proliferate in infants receiving breast milk 68 ). As ecosystems degrade, there may be several reasons why BCFA-synthesizing organisms decline. Ecosystem degradation is widely associated with reductions in soil organic matter 3 , 69 , 70 , 71 . Indeed, the more degraded chronosequence soils we analyzed had lower soil organic content than unmined sites 72 . Soil organic matter comprises a continuum of progressively decomposed organic compounds, commonly including amino acids and fatty acids 73 . Amino acids are released from proteins in the soil at rates correlated with soil organic matter pools and soil protein concentrations 74 , and increasing with ecosystem succession 75 . Natural decomposition and recycling networks of saprotrophic fungi (including high BCFA-content spp.) may also be degraded, likely reducing BCFA content and turnover in soil microbiomes. Diverse plant litter contributes to increased soil carbon, higher microbial abundance, diversity, decomposition and turnover, which strengthens over time 76 . Taken together, these influences may reduce access to and recycling of both branched-chain amino acids and fatty acids for soil microbiomes in degraded ecosystems, thereby potentially reducing ambient human exposure to health-promoting microbial mmBCFA synthesis capacity ( Fig. 5 ). Degraded agricultural soils might also produce crops and ruminant products (for example, meat, dairy) with reduced mmBCFA content, that impact gut microbiomes via diet. However, these ideas require further investigation. Download figure Open in new tab Fig. 5 Hypothesis linking ecosystem degradation and type 2 diabetes. Intact natural ecosystems typically have greater biodiversity, woody plant debris (including lignin), fungal recycling and soil organic matter (SOM) pools. SOM releases branched-chain amino acids (BCAA) which are used to make precious monomethyl branched-chain fatty acids (mmBCFA) used in bacterial cell membranes, stored by fungi, and needed to regulate animal cell metabolism via the mechanistic target of rapamycin complex 1 (mTORC1) among other pathways. With ecosystem degradation, soil organic matter decomposes into simple sugars. Degraded soil microbiomes have increased capacity to metabolize simple sugars, decreased capacity to metabolize lignin (potentially impacting production of short-chain fatty acids, SCFAs), and decreased mmBCFA synthesis capacity (potentially linked to mmBCFA content in microbial bodies, necromass and the wider ecosystem). If people consume nutrient-depleted food (low mmBCFA and lignin content, high sugar content) and/or are increasingly exposed to soil microbiomes associated with degraded ecosystems, it may contribute to metabolic shifts in the gut microbiome consistent with type 2 diabetes. Identified compounds linked to energy harvesting We found that potential metabolism of sugars L-arabinose, D-fructose, melibiose and the intermediate compound 6-phosphosucrose each increased with ecosystem degradation and T2D. Additionally, we found the potential metabolism of sucrose, glucose, galactose, xylose and mannose increased in more degraded ecosystems. Sugars are the most abundant organic compounds in the biosphere and are key carbon and energy sources for soil microbes 77 . Sugars in soil can either be derived from primary sources (for example, decomposition of plant litter, rhizodeposits, root exudates) or secondary sources (for example, microorganisms and their residues) 77 . Previous work suggests that galactose, glucose, mannose, arabinose and xylose are chief components of sugar residues found within soil organic matter 78 . Our detection of increased potential metabolism of simple sugars (largely monosaccharides and disaccharides) likely reflects enhanced microbial decomposition of soil organic matter within more degraded ecosystems 69 . In the gut microbiome, carbohydrate metabolism contributes up to 10% of the host’s overall energy extraction and is recognized as an important contributor to insulin resistance and the development of obesity and prediabetes 79 . Ref. 79 found increased gut microbiome metabolism of monosaccharides, including fructose, galactose, mannose and xylose, significantly correlated with insulin resistance. Interestingly, in soils and humans, enhanced sugar metabolism is also associated with more opportunistic bacteria 80 . Increased relative abundances of opportunists (including fast-growing, potential pathogens) have been associated with disturbance in urban greenspace soils 37 and in human-altered land uses 81 . If microbiome components promoting enhanced sugar metabolism are transferred from degraded ecosystems to the human gut, they may foster opportunistic or pathogen-like traits – potentially contributing to increased energy harvest and metabolic imbalances associated with T2D. Identified compounds linked to energy management We found altered potential metabolism for compounds with plausible links to energy storage via fatty acids, gut hormone secretion and appetite regulation that decreased in both degraded ecosystems and T2D. This raises the question whether nutrient-depleted foods and/or excessive exposures to degraded ecosystem soils that are deficient in potential metabolism of compounds involved in energy management may shape gut microbiomes that are also deficient in this way. We discuss above how gut microbiome mmBCFA content and biosynthesis capacity might modulate host cellular metabolism via central mTORC1 regulation, alteration of enterocyte phospholipid composition and function, and/or supply of BCFA for further host endogenous fatty acid synthesis and BCFA-mediated metabolism. Dietary fiber, such as lignin, is known to decrease plasma ghrelin, known as the hunger hormone, and increase other key gut regulatory hormones: cholecystokinin, glucagon-like-peptide-1 (GLP-1), and peptide YY 82 . Short-chain fatty acids (SCFAs) are produced when gut microbes ferment non-digestible fibers, and these SCFAs bind to receptors in the colon, leading to the production of hormones that regulate food intake, body weight, and energy metabolism 10 , 82 . Similar trends to lignin appeared for sinapyl alcohol and coumaryl alcohol (both precursors to lignin) in the compound-wide results spanning all datasets. Potential metabolism for the dietary fiber pectin also decreased in T2D (significantly in the Swedish cohort, and marginally in the Chinese cohort), but not in degraded ecosystems. We searched for oligosaccharides (for example, fructan, inulin, galactan) but did not detect these compounds in our datasets. Additional compounds that we identified remain to be explored (Supplementary Table S5). Disentangling dietary and environmental influences on T2D gut microbiomes Altered potential metabolism for compounds detected within gut microbiomes may arise from dietary and environmental exposures, gut environment and functions, neurological influences, among other factors 83 . Where we found matching trends across all case studies (for example, L-arabinose, D-fructose, melibiose, BCFA-ACPs, lignin), this may be consistent with an alignment between environmental exposures and dietary inputs. In the scenario where compounds consistently showed in both T2D cohorts, but not in the restoration chronosequence soils, this suggests dietary effects alone. For example, increased potential metabolism results for amylopectin suggests that excessive consumption of starch-rich foods may contribute to T2D. For many compounds we found alignment in potential metabolism between the ecosystem condition and T2D gut (Chinese cohort), but not the T2D gut (Swedish cohort) – this may point to greater environmental exposures and detrimental health influence in some population groups. Yet another contrast occurred in the case of ammonia, where potential metabolism trends were opposing in the T2D cohorts, in the absence of any ecosystem condition effect, which suggests influences from dietary exposures alone associating with T2D. We raise further noteworthy implications for community connection to soils and ecosystems, and study limitations in the Supplementary Material, Supporting Information. Conclusions We found overlapping trends in the potential metabolism of compounds with beneficial bioactivities, and involved in energy harvesting and management, within separate model case study datasets of soil-ecosystem degradation from the United States and Australia, and T2D gut microbiomes from Swedish and Chinese cohorts. Our results support existing studies implicating, at least in part, roles for microbiomes, sugars, lignin (dietary fiber) and mmBCFA in metabolism and T2D. Additionally, our findings could inform new T2D interventions; and suggest that degraded soil microbiomes may contribute to metabolic imbalances found in T2D via nutrient-depleted food (for example, low mmBCFA and lignin content, high sugar content) and/or excessive environmental exposures associated with degraded ecosystems. Our method used to examine microbiome potential metabolism at the resolution of individual compounds has raised new biologically interpretable and testable hypotheses regarding potential links between soil, ecosystem and T2D gut health that warrant further transdisciplinary investigation. Methods Case study data We examined previously published soil and human gut metagenome case study datasets (detailed in Supplementary Table 1). Restoration chronosequence 45 soil samples spanned planting ages 6, 12, 22, 31 years and unmined (UM) with n = groups of 3. T2D datasets were from Swedish 46 (T2D n = 33, Normal n = 43) and Chinese 47 (T2D n = 30, Normal n = 52) cohorts. We used metadata from ref. 12 to exclude subjects treated with metformin or diagnosed with prediabetes. From the Chinese cohort, we only included subjects >50 years old to compare with the mature age (69-72 years) Swedish subjects. Australian Microbiome Initiative (AMI) disturbed ( n = 29) versus natural ( n = 55) soils were surface depth (0–10 cm), from the temperate climate zone, with 7.5–45 % clay content (i.e., avoiding very sandy and very clayey soils) as described in ref. 38. Disturbed soils were from agricultural land uses (cereals, cotton, horticulture, irrigated pasture), while natural soils were from conservation and national park land uses. Metagenome functional profiling via compound processing potential (CPP) Our CPP analysis approach used a purpose-built bioinformatics workflow ( Fig. 6 ) that redistributes values of functional potential relative abundances (%), from the scale of functions (i.e., biochemical pathways that often comprise multiple linked chemical reactions), down to the level of individual chemical compounds. These compounds are conceptually participating in a weighted array of intersecting chemical reactions that represent the collective functional capacity of a metagenome. Download figure Open in new tab Fig. 6 Overview of the compound processing potential (CPP) analysis workflow. Metagenomic functional potential pathway relative abundances were translated to quantify hypothetical potential metabolism at the scale of individual compounds. CPP % values for carbon-containing compounds can be mapped using chemical formula ratios to visualize clusters of bioenergetically similar compounds that trend together. Measures of CPP were derived via steps outlined in Fig. 6 . Firstly, following ref. 38, using high performance computing resources 84 , 85 raw metagenomic sequences were accessed, trimmed for quality control, and human case study sequences that mapped using Bowtie2 86 to the human genome (GRCh38.p14/hg38) were excluded (low levels were observed; Supplementary Material, Supporting Information). Then good quality read 1 sequences were classified to SEED subsystem 87 functional annotations and relative abundances using SUPER-FOCUS software 88 . Every SUPER-FOCUS function (output row) was translated to one or more corresponding chemical reaction(s) using a purpose-built R-script algorithm based on ModelSEED database lookup tables (from https://github.com/ModelSEED/ModelSEEDDatabase ; accessed 10 Aug 2022). Linking of functions (and sub-functions where present) to chemical reactions was based on either: full matching of functional hierarchies (using subsystem-class, -subclass, -name and -role); detection of Enzyme Commission (EC) number; or matching of SUPER-FOCUS function name within ModelSEED lookup tables for reactions (reaction name or alias), subsystems (role), or reaction-pathways (external reaction name). Reactions were then linked to corresponding compounds using ModelSEED database tables (‘reactions.tsv’, ‘compounds.tsv’). Advancing from the previous method used in ref. 38, here we derived CPP measures at the resolution of individual compounds (all software parameters and supporting R code for CPP calculations are available at: https://github.com/liddic/cpp3d_t2d ): Function level relative abundances from SUPER-FOCUS (which sum to 100% in each sample) were allocated to compounds by dividing values equally across all sub-functions and reactions, and dividing again across all corresponding compounds, with weightings to account for stoichiometric coefficients for each compound within each reaction, with equal consideration to reactants and products. Relative abundances (%) for respective unique compounds (which may arise across multiple reactions and functions) were then collated and summed to provide a summary CPP relative abundance (%) for each compound. (Here, conversion to CPP data format successfully recovered typically 65 to 70% of the initial 100% function potential per sample; Supplementary Table 1). For mapping visualization of CPP patterns for carbon-containing compounds, elemental ratios of O:C, H:C, and N:C were calculated. Such chemical mapping frameworks have been used to display and interpret groupings of energetically and chemically similar compounds 38 , 49 . Statistical Analysis We used a staged approach to explore overlapping potential metabolism (i.e., CPP) trends in the soil and T2D gut samples. We progressively examined the restoration chronosequence soils (US), then Swedish and Chinese T2D cohorts, and lastly the AMI soils to corroborate findings. Further detail is in Supplementary Material and at https://github.com/liddic/cpp3d_t2d : Thirty selected compounds were initially explored for CPP trends, chosen due to their relevance to soil and/or gut health (Supplementary Table 2). In chrosequence soils (US), CPP trends were evaluated using Kendall’s tau rank correlation tests based on ordinal revegetation age classes. We interpreted CPP trends with ecosystem degradation as the inverse of calculated trends with increasing revegetation age (as informed by previous work showing highly disturbed post-mining soil microbiomes progressively shift in their structure with ecosystem restoration towards intact natural condition 36 ). In T2D versus normal health, CPP trends (i.e., differences) were evaluated using non-parametric Wilcoxon rank-sum (Mann-Whitney) tests, due to often differing variance of groups (T2D vs. normal) and to avoid normality assumptions. All data were plotted to assist interpretation. Compound-wide association tests were also performed to exhaustively scan CPP trends for all compounds. This used the same approach as above, with further steps to automate analyses. In correlation testing of chrosequence soils, compounds that did not have relative abundance data for at least a quarter of the total sample number were excluded due to low data. In this chrosequence soil dataset, strong CPP trends were discovered, and only significant results based on Benjamini and Hochberg adjusted P -values were considered further. In the subsequent compound-wide association analysis of T2D gut samples, compounds were only included if at least one or other of the T2D or normal groups contained at least 50% non-zero CPP values. Testing was based on log 10 -transformed CPP % values, after zero replacement with small positive values equal to half the minimum non-zero CPP % of all samples. One-sided (directional) difference tests were based on whether the median value in T2D was greater or less than the median value for normal healthy subjects. To retain and explore the most interesting (potentially significant) results from the compound-wide T2D analyses, these data were not subject to P -adjustment for multiple testing. Overlap analysis. Compounds with CPP % trends in both the restoration chronosequence (US) and T2D (SWE) case studies were identified, based on matching ModelSEED compound identifiers. Our focus was to highlight compounds that displayed consistent increased or decreased CPP values with ecosystem degradation and T2D. Visualising compound-wide association test results. Compounds with CPP % trends in the restoration chronosequence (US) and T2D (SWE) gut microbiomes, and overlaps between the two, were mapped using their chemical formula elemental ratios of O:C, H:C, and N:C. From this mapping space, prominent localized clusters of consistently trending compounds (i.e., with ecosystem degradation and T2D) were identified and further analyzed to determine trends in their collective (summed) CPP % and to interpret their possible metabolic importance. Trend consolidation and validation. The above steps were repeated with the second T2D (CHN) cohort, which refined the list of compounds with parallel CPP trends with ecosystem degradation (US) and in both T2D datasets. Trend and contextual information for these compounds was reported and visualized to represent the significance (-log 10 [ P -value]; as used in genome-wide association study Manhattan plots) of association with ecosystem degradation and T2D, and the number of functional themes (level 3 subsystems) they are linked to. Here, data for T2D were visualized using mean results from the Swedish and Chinese cohort datasets. To corroborate parallel CPP trends from the restoration chronosequence (US) and both T2D datasets, a focused analysis of CPP trends only for previously identified compounds was performed using the disturbed versus natural AMI soils. Beta diversity ordinations. Supplementary analyses using the restoration chronosequence (US) and T2D Swedish cohort dataset were performed to examine community-scale compositional patterns and differences between sample groups and analysis approaches, comparing conventional functional potential (%) data (termed ‘functions’) versus CPP (%) data. This analysis used principal coordinate analysis (PCoA) ordinations based on Bray-Curtis distances, and testing for differences between groups via permutational analysis of variance (PERMANOVA) and beta dispersion testing for homogeneity of groupings using the vegan R package 89 . These data are presented in the Supplementary Material, Supporting Information and Supplementary Fig. 42. Data availability The datasets used are available from NCBI Sequence Read Archive (accessions PRJEB1786 and PRJNA422434), MG-RAST (project mgp16379), and soil metagenomes from the Australian Microbiome Initiative data portal ( https://data.bioplatforms.com/organization/australian-microbiome ). Code availability Code used to support this study is available from: https://github.com/liddic/cpp3d_t2d . Acknowledgments We acknowledge and thank the authors of previously published metagenomics datasets used here, which enabled comparison of samples spanning human and environmental health. This includes the Australian Microbiome initiative which was supported by funding from Bioplatforms Australia and the Integrated Marine Observing System (IMOS) through the Australian Government’s National Collaborative Research Infrastructure Strategy (NCRIS), Parks Australia through the Bush Blitz program funded by the Australian Government and BHP, and the CSIRO. We acknowledge Indigenous peoples and the traditional custodians of the lands on which the case study datasets originate, and our authors live and work. This work was supported by the People, Cities and Nature research program (Aotearoa New Zealand Ministry of Business, Innovation and Employment, grant UOWX2101). Footnotes Competing Interest Statement: The authors declare no competing interest. This revision is condensed for journal submission with shorter word counts and further highlights the potential for metabolites / biochemical compounds / necromass, as key components of microbiomes, to extend the influence from soil microbiomes to gut microbiomes, without requiring colonization https://github.com/liddic/cpp3d_t2d References 1. ↵ Muhummed , A.M. , Lanker , K.C. , Yersin , S. , Zinsstag , J. & Vonaesch , P . One Health, One Microbiome . Microbiome 13 , 216 ( 2025 ). OpenUrl CrossRef PubMed 2. ↵ Banerjee , S. & van der Heijden , M.G.A . Soil microbiomes and one health . 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